Valeria Di Cola, Olivier Broennimann, Blaise Petitpierre, Manuela D'Amen, Frank Breiner & Antoine Guisan
Miscellaneous methods and utilities for spatial ecology analysis, written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.
ecospat offers the possibility to perform Pre-modelling Analysis, such as Spatial autocorrelation analysis, MESS (Multivariate Environmental Similarity Surfaces) analyses, Phylogenetic diversity Measures, Biotic Interactions. It also provides functions to complement biomod2 in preparing the data, calibrating and evaluating (e.g. boyce index) and projecting the models. Complementary analysis based on model predictions (e.g. co-occurrences analyses) are also provided.
In addition, the ecospat package includes Niche Quantification and Overlap functions that were used in Broennimann et al. 2012 and Petitpierre et al. 2012 to quantify climatic niche shifts between the native and invaded ranges of invasive species.
library(ecospat)
## Loading required package: ade4
## Loading required package: ape
## Loading required package: gbm
## Loading required package: survival
## Loading required package: lattice
## Loading required package: splines
## Loading required package: parallel
## Loaded gbm 2.1.3
## Loading required package: sp
citation("ecospat")
##
## To cite package 'ecospat' in publications use:
##
## Olivier Broennimann, Valeria Di Cola and Antoine Guisan (2017).
## ecospat: Spatial Ecology Miscellaneous Methods. R package
## version 2.2.0.
## http://www.unil.ch/ecospat/home/menuguid/ecospat-resources/tools.html
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {ecospat: Spatial Ecology Miscellaneous Methods},
## author = {Olivier Broennimann and Valeria {Di Cola} and Antoine Guisan},
## year = {2017},
## note = {R package version 2.2.0},
## url = {http://www.unil.ch/ecospat/home/menuguid/ecospat-resources/tools.html},
## }
ecospat.testData()
data(ecospat.testData)
names(ecospat.testData)
## [1] "numplots" "long"
## [3] "lat" "ddeg"
## [5] "mind" "srad"
## [7] "slp" "topo"
## [9] "Achillea_atrata" "Achillea_millefolium"
## [11] "Acinos_alpinus" "Adenostyles_glabra"
## [13] "Aposeris_foetida" "Arnica_montana"
## [15] "Aster_bellidiastrum" "Bartsia_alpina"
## [17] "Bellis_perennis" "Campanula_rotundifolia"
## [19] "Centaurea_montana" "Cerastium_latifolium"
## [21] "Cruciata_laevipes" "Doronicum_grandiflorum"
## [23] "Galium_album" "Galium_anisophyllon"
## [25] "Galium_megalospermum" "Gentiana_bavarica"
## [27] "Gentiana_lutea" "Gentiana_purpurea"
## [29] "Gentiana_verna" "Globularia_cordifolia"
## [31] "Globularia_nudicaulis" "Gypsophila_repens"
## [33] "Hieracium_lactucella" "Homogyne_alpina"
## [35] "Hypochaeris_radicata" "Leontodon_autumnalis"
## [37] "Leontodon_helveticus" "Myosotis_alpestris"
## [39] "Myosotis_arvensis" "Phyteuma_orbiculare"
## [41] "Phyteuma_spicatum" "Plantago_alpina"
## [43] "Plantago_lanceolata" "Polygonum_bistorta"
## [45] "Polygonum_viviparum" "Prunella_grandiflora"
## [47] "Rhinanthus_alectorolophus" "Rumex_acetosa"
## [49] "Rumex_crispus" "Vaccinium_gaultherioides"
## [51] "Veronica_alpina" "Veronica_aphylla"
## [53] "Agrostis_capillaris" "Bromus_erectus_sstr"
## [55] "Campanula_scheuchzeri" "Carex_sempervirens"
## [57] "Cynosurus_cristatus" "Dactylis_glomerata"
## [59] "Daucus_carota" "Festuca_pratensis_sl"
## [61] "Geranium_sylvaticum" "Leontodon_hispidus_sl"
## [63] "Potentilla_erecta" "Pritzelago_alpina_sstr"
## [65] "Prunella_vulgaris" "Ranunculus_acris_sl"
## [67] "Saxifraga_oppositifolia" "Soldanella_alpina"
## [69] "Taraxacum_officinale_aggr" "Trifolium_repens_sstr"
## [71] "Veronica_chamaedrys" "Parnassia_palustris"
## [73] "glm_Agrostis_capillaris" "glm_Leontodon_hispidus_sl"
## [75] "glm_Dactylis_glomerata" "glm_Trifolium_repens_sstr"
## [77] "glm_Geranium_sylvaticum" "glm_Ranunculus_acris_sl"
## [79] "glm_Prunella_vulgaris" "glm_Veronica_chamaedrys"
## [81] "glm_Taraxacum_officinale_aggr" "glm_Plantago_lanceolata"
## [83] "glm_Potentilla_erecta" "glm_Carex_sempervirens"
## [85] "glm_Soldanella_alpina" "glm_Cynosurus_cristatus"
## [87] "glm_Campanula_scheuchzeri" "glm_Festuca_pratensis_sl"
## [89] "glm_Bromus_erectus_sstr" "glm_Saxifraga_oppositifolia"
## [91] "glm_Daucus_carota" "glm_Pritzelago_alpina_sstr"
## [93] "gbm_Bromus_erectus_sstr" "gbm_Saxifraga_oppositifolia"
## [95] "gbm_Daucus_carota" "gbm_Pritzelago_alpina_sstr"
ecospat.testNiche.inv()
data(ecospat.testNiche.inv)
names(ecospat.testNiche.inv)
## [1] "x" "y" "aetpet" "gdd" "p"
## [6] "pet" "stdp" "tmax" "tmin" "tmp"
## [11] "species_occ" "predictions"
ecospat.testNiche.nat()
data(ecospat.testNiche.nat)
names(ecospat.testNiche.nat)
## [1] "x" "y" "aetpet" "gdd" "p"
## [6] "pet" "stdp" "tmax" "tmin" "tmp"
## [11] "species_occ" "predictions"
ecospat.testTree()
fpath <- system.file("extdata", "ecospat.testTree.tre", package="ecospat")
fpath
## [1] "/private/var/folders/y_/356n2gv11490bhhswc7m8m6w0000gn/T/RtmpDWdwN5/Rinst6c6618fe9469/ecospat/extdata/ecospat.testTree.tre"
tree<-read.tree(fpath)
tree$tip.label
## [1] "Rumex_acetosa" "Polygonum_bistorta"
## [3] "Polygonum_viviparum" "Rumex_crispus"
## [5] "Cerastium_latifolium" "Silene_acaulis"
## [7] "Gypsophila_repens" "Vaccinium_gaultherioides"
## [9] "Soldanella_alpina" "Cruciata_laevipes"
## [11] "Galium_album" "Galium_anisophyllon"
## [13] "Galium_megalospermum" "Gentiana_verna"
## [15] "Gentiana_bavarica" "Gentiana_purpurea"
## [17] "Gentiana_lutea" "Bartsia_alpina"
## [19] "Rhinanthus_alectorolophus" "Prunella_grandiflora"
## [21] "Acinos_alpinus" "Plantago_alpina"
## [23] "Plantago_lanceolata" "Veronica_officinalis"
## [25] "Veronica_aphylla" "Veronica_alpina"
## [27] "Veronica_chamaedrys" "Veronica_persica"
## [29] "Globularia_cordifolia" "Globularia_nudicaulis"
## [31] "Myosotis_alpestris" "Myosotis_arvensis"
## [33] "Aposeris_foetida" "Centaurea_montana"
## [35] "Hieracium_lactucella" "Leontodon_helveticus"
## [37] "Leontodon_autumnalis" "Hypochaeris_radicata"
## [39] "Achillea_atrata" "Achillea_millefolium"
## [41] "Homogyne_alpina" "Senecio_doronicum"
## [43] "Adenostyles_glabra" "Arnica_montana"
## [45] "Aster_bellidiastrum" "Bellis_perennis"
## [47] "Doronicum_grandiflorum" "Phyteuma_orbiculare"
## [49] "Phyteuma_spicatum" "Campanula_rotundifolia"
Plot tree
plot(tree, cex=0.6)
ecospat.mantel.correlogram(dfvar=ecospat.testData[c(2:16)],colxy=1:2, n=100,
colvar=3:7, max=1000, nclass=10, nperm=100)
The graph indicates that spatial autocorrelation (SA) is minimal at a distance of 180 meters. Note however that SA is not significantly different than zero for several distances (open circles).
colvar <- ecospat.testData[c(4:8)]
x <- cor(colvar, method="pearson")
ecospat.npred (x, th=0.75)
## [1] 4
x <- cor(colvar, method="spearman")
ecospat.npred (x, th=0.75)
## [1] 4
x <- ecospat.testData[c(4:8)]
p<- x[1:90,] #A projection dataset.
ref<- x[91:300,] # A reference dataset
ecospat.climan(ref,p)
## [1] 0.185415746 -0.028290993 -0.032909931 -0.009237875 -0.034642032
## [6] -0.209006928 -0.084295612 -0.103622863 0.355220600 -0.136258661
## [11] -0.087182448 -0.209006928 -0.143187067 -0.124711316 -0.114844720
## [16] -0.230596451 0.276046242 0.249093277 -0.125288684 -0.101226337
## [21] -0.113883908 -0.204653076 -0.001154734 -0.132217090 -0.100461894
## [26] 0.464738681 -0.416578541 -0.044457275 -0.018475751 -0.122225532
## [31] -0.137611720 -0.050808314 0.254605027 -0.062012319 0.238294633
## [36] -0.159141330 -0.147806005 0.277670365 -0.071593533 -0.019053118
## [41] 0.390781314 0.175132571 0.401892929 0.843703731 0.286155800
## [46] 0.321142114 0.668511130 0.252253209 0.440050672 0.177247206
## [51] 0.831525456 0.303710525 0.197182304 0.219273698 0.196637663
## [56] 0.195300816 0.142395786 0.176988160 -0.051991905 0.265163111
## [61] -0.020785219 -0.017898383 0.553965995 0.409635110 0.323633285
## [66] 0.468693064 0.124983005 -0.032909931 0.165642783 0.147046687
## [71] 0.202895471 0.341992334 0.225508458 0.133254065 0.485295264
## [76] -0.047344111 -0.012282931 0.165429659 0.134199992 0.216655251
## [81] 0.139419127 0.121254775 0.098782992 0.591393741 0.110866239
## [86] 0.146010655 0.095562156 0.093353356 0.081712342 0.160531262
x <- ecospat.testData[c(2,3,4:8)]
proj<- x[1:90,] #A projection dataset.
cal<- x[91:300,] #A calibration dataset
mess.object<-ecospat.mess (proj, cal, w="default")
ecospat.plot.mess (xy=proj[c(1:2)], mess.object, cex=1, pch=15)
In the MESS plot pixels in red indicate sites where at least one environmental predictor has values outside of the range of that predictor in the calibration dataset. In the MESSw plot, same as previous plot but with weighted by the number of predictors.Finally, the MESSneg plot shows at each site how many predictors have values outside of their calibration range.
fpath <- system.file("extdata", "ecospat.testTree.tre", package="ecospat")
tree <- read.tree(fpath)
data <- ecospat.testData[9:52]
pd<- ecospat.calculate.pd(tree, data, method = "spanning", type = "species", root = TRUE, average = FALSE, verbose = TRUE )
## Progress (. = 100 pixels calculated):
## ... [300]
## All 300 pixels done.
pd
## [1] 6.9782188 6.7981743 4.9964700 4.9964700 4.9964700
## [6] 29.8820547 58.7451752 6.5223035 30.6152478 1.5258335
## [11] 0.0000000 44.3661803 38.4155607 6.5223035 24.0929443
## [16] 78.1607950 38.4155607 29.0894143 29.0894143 89.9839758
## [21] 27.4135569 40.2827035 1.5258335 56.7686202 18.9535475
## [26] 34.8871800 0.0000000 1.5258335 39.9291325 48.5997861
## [31] 82.8763723 29.0894143 24.0929443 24.0929443 35.0949481
## [36] 85.1406422 54.7974724 41.2817284 32.4100269 30.0984781
## [41] 46.8247511 42.8358475 35.6223697 91.5539224 72.7022527
## [46] 0.0000000 21.1862293 29.7320308 10.1187868 30.6152478
## [51] 27.4135569 59.0015345 78.1536692 42.6423378 24.0929443
## [56] 46.8050070 49.3924266 29.0894143 38.5290848 43.3611373
## [61] 63.6397674 49.6097169 34.6522309 37.1871282 109.8813371
## [66] 106.6971561 52.2512132 80.6221671 68.3867818 49.1362998
## [71] 56.6138690 41.9283257 29.0894143 33.2026673 16.1897593
## [76] 79.1938213 42.8115427 25.6187778 34.6805724 96.9902366
## [81] 75.2672695 7.5313673 31.4078882 50.5865673 13.9570775
## [86] 104.4121025 43.0464918 36.6693230 52.8590823 24.8855847
## [91] 107.2302322 33.9358604 54.0048319 30.6152478 102.0983385
## [96] 8.3170826 52.3071062 8.3170826 61.8562896 58.1179346
## [101] 59.7939424 8.3170826 81.6495398 51.1054635 75.8701970
## [106] 77.6947419 56.7929250 70.3693202 81.3965205 29.9118877
## [111] 111.0790432 75.7518798 112.5482496 32.9763735 42.5644761
## [116] 40.4507005 83.8955419 36.6693230 2.3184739 57.5978451
## [121] 91.3453370 33.3983912 50.1351419 7.7084002 63.9227817
## [126] 0.7926404 67.2813325 91.2965996 90.9578739 105.9024741
## [131] 74.6128871 46.1321553 15.2479619 24.0929443 70.4802708
## [136] 68.8949899 118.6657550 101.3545260 119.8539056 23.6602184
## [141] 105.8968281 15.9336325 138.4059855 39.6674173 51.7391372
## [146] 58.4119283 81.1388699 96.6048825 72.2156025 56.3601992
## [151] 112.9489963 63.3258805 50.1594468 23.0021994 87.1886965
## [156] 12.7714946 33.7421666 23.2537702 14.3226164 6.9752071
## [161] 0.7926404 13.5641350 36.2007616 63.9227817 40.3310946
## [166] 52.8264129 67.9956878 29.5843437 0.0000000 191.7818606
## [171] 133.6077875 83.3977825 118.6711630 51.1512871 69.3838811
## [176] 87.7066616 35.8005270 93.7797077 85.8984840 23.4933413
## [181] 149.7094684 52.4451847 112.1873673 53.4479612 51.4341108
## [186] 106.6959500 14.4361405 41.6547546 89.4018733 59.1068292
## [191] 3.0516670 60.7852739 28.1850877 52.1002690 114.3651475
## [196] 86.2640717 83.7092232 39.8499777 55.3514065 116.1795597
## [201] 21.2346203 75.4593878 197.8157358 140.3806968 93.2192350
## [206] 36.5337815 146.3370747 214.5450205 64.2439145 83.3740177
## [211] 57.0440643 149.5697614 196.9415036 31.0984631 57.4769230
## [216] 28.4014469 42.3978747 194.5384819 60.5204195 73.0060715
## [221] 52.1628582 30.2801165 63.1752097 29.1789484 82.7662787
## [226] 196.8309769 3.4666557 0.0000000 31.5688084 60.5650008
## [231] 43.3334929 62.5952411 13.9570775 18.9495667 35.2646601
## [236] 32.6155790 0.0000000 14.6693623 24.2745827 73.9480832
## [241] 19.2825866 0.0000000 40.6115985 68.9862341 6.9782188
## [246] 11.5030881 27.9105497 72.4020225 39.6781995 35.4596364
## [251] 33.9160835 27.5735165 15.9619740 27.9105497 17.8628493
## [256] 36.0936777 87.0440848 27.9105497 66.6907987 21.6475811
## [261] 67.5969904 0.0000000 0.0000000 0.0000000 58.0542370
## [266] 0.0000000 0.0000000 27.9105497 0.0000000 0.0000000
## [271] 27.9105497 34.8887684 56.5556633 27.9105497 30.3097595
## [276] 88.4296666 37.8150727 54.2397810 31.6243116 7.5799087
## [281] 73.0136833 31.8638035 41.7172212 120.5228857 32.2001243
## [286] 151.4545228 10.1544492 70.8133537 59.3255687 25.7211220
## [291] 24.1115267 43.1500941 150.0299191 72.2758570 85.9498096
## [296] 159.7242106 66.8328159 24.0929443 0.0000000 27.9105497
plot(pd)
Loading test data for the niche dynamics analysis in the invaded range
inv <- ecospat.testNiche.inv
Loading test data for the niche dynamics analysis in the native range
nat <- ecospat.testNiche.nat
Calibrating the PCA in the whole studay area, including both native and invaded ranges (same as PCAenv in Broenniman et al. 2012)
pca.env <- dudi.pca(rbind(nat,inv)[,3:10],scannf=F,nf=2)
ecospat.plot.contrib(contrib=pca.env$co, eigen=pca.env$eig)
The correlation circle indicate the contribution of original predictors to the PCA axes.
# PCA scores for the whole study area
scores.globclim <- pca.env$li
# PCA scores for the species native distribution
scores.sp.nat <- suprow(pca.env,nat[which(nat[,11]==1),3:10])$li
# PCA scores for the species invasive distribution
scores.sp.inv <- suprow(pca.env,inv[which(inv[,11]==1),3:10])$li
# PCA scores for the whole native study area
scores.clim.nat <- suprow(pca.env,nat[,3:10])$li
# PCA scores for the whole invaded study area
scores.clim.inv <- suprow(pca.env,inv[,3:10])$li
For a species in the native range (North America)
# gridding the native niche
grid.clim.nat <- ecospat.grid.clim.dyn(glob=scores.globclim,
glob1=scores.clim.nat,
sp=scores.sp.nat, R=100,
th.sp=0)
For a species in the invaded range (Australia)
# gridding the invasive niche
grid.clim.inv <- ecospat.grid.clim.dyn(glob=scores.globclim,
glob1=scores.clim.inv,
sp=scores.sp.inv, R=100,
th.sp=0)
# Compute Schoener's D, index of niche overlap
D.overlap <- ecospat.niche.overlap (grid.clim.nat, grid.clim.inv, cor=T)$D
D.overlap
## [1] 0.224586
The niche overlap between the native and the ivaded range is 22%.
It is reccomended to use at least 1000 replications for the equivalency test. As an example we used rep = 10, to reduce the computational time.
eq.test <- ecospat.niche.equivalency.test(grid.clim.nat, grid.clim.inv,
rep=10, alternative = "greater")
Niche equivalency test H1: Is the overlap between the native and invaded niche higher than two random niches?
Shifting randomly the invasive niche in the invaded study area It is recomended to use at least 1000 replications for the similarity test. As an example we used rep = 10, to reduce the computational time.
sim.test <- ecospat.niche.similarity.test(grid.clim.nat, grid.clim.inv,
rep=10, alternative = "greater",
rand.type=2)
Niche similarity test H1: Is the overlap between the native and invaded higher than when the invasive niche is randomly introduced in the invaded study area?
ecospat.plot.overlap.test(eq.test, "D", "Equivalency")
ecospat.plot.overlap.test(sim.test, "D", "Similarity")
We see that the niche overlap D is 22% and this value is compared to the random distribution of the niche equivalency and niche similarity tests.
niche.dyn <- ecospat.niche.dyn.index (grid.clim.nat, grid.clim.inv, intersection = 0.1)
Plot niche overlap
ecospat.plot.niche.dyn(grid.clim.nat, grid.clim.inv, quant=0.25, interest=2,
title= "Niche Overlap", name.axis1="PC1",
name.axis2="PC2")
ecospat.shift.centroids(scores.sp.nat, scores.sp.inv, scores.clim.nat, scores.clim.inv)
# gridding the native niche
grid.clim.t.nat <- ecospat.grid.clim.dyn(glob=as.data.frame(rbind(nat,inv)[,10]),
glob1=as.data.frame(nat[,10]),
sp=as.data.frame(nat[which(nat[,11]==1),10]),
R=1000, th.sp=0)
# gridding the invaded niche
grid.clim.t.inv <- ecospat.grid.clim.dyn(glob=as.data.frame(rbind(nat,inv)[,10]),
glob1=as.data.frame(inv[,10]),
sp=as.data.frame(inv[which(inv[,11]==1),10]),
R=1000, th.sp=0)
t.dyn<-ecospat.niche.dyn.index (grid.clim.t.nat, grid.clim.t.inv,
intersection=0.1)
ecospat.plot.niche.dyn(grid.clim.t.nat, grid.clim.t.inv, quant=0,
interest=2, title= "Niche Overlap",
name.axis1="Average temperature")
data <- ecospat.testData[c(9:16,54:57)]
For each pair of species (sp1, sp2), the number (N) of plots where both species were present is divided by the number of plots where the rarest of the two species is present. This index ranges from 0 (no co-occurrence) to 1 (always in co-occurrence) as given in eq. 1.
where N(S1 intersects S2) is the number of times species S1 and S2 co-occur, while Min(NS1, NS2) is the number of times species S1 and S2 co-occur, while is the occurrence frequency of the rarest of the two species.
ecospat.co_occurrences (data)
## Aposeris_foetida Arnica_montana Aster_bellidiastrum
## Aposeris_foetida 1.0000000 0.3636364 0.25000000
## Arnica_montana 0.3636364 1.0000000 0.36363636
## Aster_bellidiastrum 0.2500000 0.3636364 1.00000000
## Bartsia_alpina 0.2222222 0.5454545 0.59090909
## Bromus_erectus_sstr 0.1250000 0.0000000 0.00000000
## Campanula_scheuchzeri 0.2444444 0.6818182 0.79545455
## Carex_sempervirens 0.4000000 0.5000000 0.65909091
## Cynosurus_cristatus 0.4222222 0.2272727 0.04545455
## Bartsia_alpina Bromus_erectus_sstr
## Aposeris_foetida 0.22222222 0.1250
## Arnica_montana 0.54545455 0.0000
## Aster_bellidiastrum 0.59090909 0.0000
## Bartsia_alpina 1.00000000 0.0000
## Bromus_erectus_sstr 0.00000000 1.0000
## Campanula_scheuchzeri 0.76086957 0.0000
## Carex_sempervirens 0.69565217 0.0625
## Cynosurus_cristatus 0.02173913 0.3750
## Campanula_scheuchzeri Carex_sempervirens
## Aposeris_foetida 0.24444444 0.40000000
## Arnica_montana 0.68181818 0.50000000
## Aster_bellidiastrum 0.79545455 0.65909091
## Bartsia_alpina 0.76086957 0.69565217
## Bromus_erectus_sstr 0.00000000 0.06250000
## Campanula_scheuchzeri 1.00000000 0.67058824
## Carex_sempervirens 0.67058824 1.00000000
## Cynosurus_cristatus 0.04705882 0.05882353
## Cynosurus_cristatus
## Aposeris_foetida 0.42222222
## Arnica_montana 0.22727273
## Aster_bellidiastrum 0.04545455
## Bartsia_alpina 0.02173913
## Bromus_erectus_sstr 0.37500000
## Campanula_scheuchzeri 0.04705882
## Carex_sempervirens 0.05882353
## Cynosurus_cristatus 1.00000000
This function allows to apply a pairwise null model analysis to a presence-absence community matrix to determine which species associations are significant across the study area. The strength of associations is quantified by the C-score index and a 'fixed-equiprobable' null model algorithm is applied.
It is recomended to use at least 10000 permutatiobns for the test. As an example we used nperm = 100, to reduce the computational time.
data<- ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
nperm <- 100
outpath <- getwd()
ecospat.Cscore(data, nperm, outpath)
## Computing observed co-occurence matrix
## .............
## .............
## .............
## Computing permutations
## .............
## 100 permutations to go
## .............
## 50 permutations to go
## .............
## Computing P-values
## .............
## Exporting dataset
## .............
## .............
## .............
## $ObsCscoreTot
## [1] 2675.468
##
## $SimCscoreTot
## [1] 2466.257
##
## $PVal.less
## [1] 1
##
## $PVal.greater
## [1] 0.00990099
##
## $SES.Tot
## [1] 52.74909
The function returns the C-score index for the observed community (ObsCscoreTot), p.value (PValTot) and standardized effect size (SES.Tot). It saves also a table in the working directory where the same metrics are calculated for each species pair (only the table with species pairs with significant p-values is saved in this version)
data <- ecospat.testData[,4:8]
ecospat.cor.plot(data)
A scatter plot of matrices, with bivariate scatter plots below the diagonal, histograms on the diagonal, and the Pearson correlation above the diagonal. Useful for descriptive statistics of small data sets (better with less than 10 variables).
data <- ecospat.testData
caleval <- ecospat.caleval (data = ecospat.testData[53], xy = data[2:3],
row.num = 1:nrow(data), nrep = 2, ratio = 0.7,
disaggregate = 0.2, pseudoabs = 100, npres = 10,
replace = FALSE)
caleval
## $eval
## yeval yeval
## 1 159 129
## 2 NA NA
## 3 NA NA
## 4 297 33
## 5 295 152
## 6 21 238
## 7 75 258
## 8 71 94
## 9 155 245
## 10 286 11
## 11 211 234
## 12 133 36
## 13 43 27
## 14 221 85
## 15 34 188
## 16 212 236
## 17 271 219
## 18 253 273
## 19 123 37
## 20 55 262
## 21 113 232
## 22 251 266
## 23 220 155
## 24 4 214
## 25 192 241
## 26 248 256
## 27 8 268
## 28 293 217
## 29 225 75
## 30 266 189
## 31 294 20
## 32 233 251
## 33 184 265
##
## $cal
## ycal ycal
## 1 NA 108
## 2 6 227
## 3 NA NA
## 4 NA NA
## 5 12 NA
## 6 202 NA
## 7 NA NA
## 8 205 51
## 9 154 267
## 10 249 199
## 11 56 243
## 12 265 169
## 13 196 181
## 14 222 201
## 15 106 133
## 16 171 290
## 17 178 53
## 18 264 3
## 19 49 180
## 20 300 230
## 21 139 56
## 22 5 255
## 23 231 244
## 24 237 250
## 25 274 79
## 26 200 23
## 27 156 182
## 28 260 157
## 29 157 286
## 30 198 198
## 31 14 247
## 32 140 22
## 33 57 17
## 34 275 100
## 35 145 259
## 36 232 274
## 37 150 5
## 38 292 24
## 39 17 233
## 40 229 2
## 41 268 204
## 42 67 240
## 43 290 246
## 44 23 44
## 45 116 254
## 46 255 134
## 47 296 283
## 48 189 168
## 49 288 71
## 50 210 95
## 51 121 261
## 52 31 206
## 53 256 253
## 54 269 229
## 55 110 15
## 56 291 106
## 57 45 279
## 58 30 16
## 59 276 203
## 60 166 224
## 61 241 272
## 62 217 186
## 63 115 193
## 64 204 270
## 65 299 185
## 66 242 84
## 67 252 114
## 68 214 121
## 69 281 239
## 70 235 223
## 71 20 154
## 72 18 139
## 73 223 30
## 74 44 263
## 75 147 120
## 76 278 289
## 77 177 228
We obtained an evaluation and calibration dataset with a desired ratio of disaggregation.
The argument fit is a vector containing the predicted suitability values
fit <- ecospat.testData$glm_Saxifraga_oppositifolia
The argument obs is a vector containing the predicted suitability values of the validation points (presence records)
obs<-ecospat.testData$glm_Saxifraga_oppositifolia[which(ecospat.testData$Saxifraga_oppositifolia==1)]
Calculate and plot Boyce Index with ecospat.boyce
ecospat.boyce (fit, obs, nclass = 0, window.w = "default", res = 100,
PEplot = TRUE)$Spearman.cor
## [1] 0.91
Here the boyce index is 0.91. If the rank of predicted expected ratio would be completely ordered along habitat suitability axis then boyce index would be 1.
Indices of accuracy of community predictions ecospat.CommunityEval()
eval<-ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
pred<-ecospat.testData[c(73:92)]
ecospat.CommunityEval (eval, pred, proba=T, ntir=5)
## trial 1 on 5
## trial 2 on 5
## trial 3 on 5
## trial 4 on 5
## trial 5 on 5
## $deviation.rich.pred
## 1 2 3 4 5
## 1 -2 0 -2 1 -3
## 2 -7 -5 -5 -8 -5
## 3 -5 -2 -5 -7 -5
## 4 -8 -5 -4 -4 -2
## 5 -9 -7 -4 -10 -11
## 6 3 -2 1 -3 -1
## 7 -6 -1 -3 -5 -6
## 8 -6 -6 -8 -5 -4
## 9 4 1 4 4 2
## 10 -2 -6 -4 -5 -1
## 11 -11 -7 -8 -9 -10
## 12 2 0 1 -1 0
## 13 0 -1 -1 2 0
## 14 -4 -4 -1 -3 -1
## 15 -3 -1 -3 -3 2
## 16 -3 1 -2 -4 -3
## 17 -3 -6 -4 0 -2
## 18 -3 -3 -4 -4 -3
## 19 5 4 3 4 2
## 20 -5 -6 -6 -6 -5
## 21 -3 -5 -3 -4 -1
## 22 -3 -5 -5 -6 -6
## 23 -6 -5 -6 -5 -8
## 24 1 2 4 3 -1
## 25 -5 -3 -5 -9 -2
## 26 0 2 -1 0 1
## 27 -7 -2 -5 -7 -6
## 28 -5 -2 -2 1 -2
## 29 1 3 2 2 1
## 30 -5 -5 -5 -5 -6
## 31 -2 -3 -2 -4 -1
## 32 -1 -1 3 3 -1
## 33 -1 -2 -1 -1 -1
## 34 -6 -6 -5 -1 -4
## 35 -2 4 4 2 2
## 36 -5 -4 -1 -2 -4
## 37 4 4 5 4 2
## 38 -3 -6 -5 -4 -4
## 39 0 -1 -2 1 1
## 40 1 0 2 -1 2
## 41 2 1 3 3 1
## 42 3 3 4 1 -1
## 43 -3 0 0 1 0
## 44 2 2 2 6 2
## 45 3 3 1 1 -3
## 46 -2 -2 -1 -1 1
## 47 1 -2 -4 -1 -2
## 48 -3 0 1 1 -2
## 49 1 -2 0 -1 0
## 50 4 1 8 2 2
## 51 7 5 6 6 5
## 52 -2 -2 -1 -3 -6
## 53 -3 4 0 -2 -1
## 54 3 3 5 -2 5
## 55 -3 -2 -5 -5 -6
## 56 -2 -8 -5 -8 -4
## 57 -2 2 -2 0 0
## 58 -1 2 -4 -2 -1
## 59 0 2 -2 1 -1
## 60 -1 -3 1 3 2
## 61 4 -1 1 1 -1
## 62 2 2 2 1 3
## 63 3 3 6 4 3
## 64 -3 2 1 -3 -2
## 65 7 3 3 6 4
## 66 5 4 6 5 8
## 67 4 1 5 3 3
## 68 1 0 -2 2 -2
## 69 3 3 5 3 2
## 70 4 5 6 7 6
## 71 -5 -1 -3 -5 -4
## 72 1 1 1 0 1
## 73 0 3 6 1 2
## 74 0 -1 1 4 3
## 75 -8 -9 -9 -8 -5
## 76 5 3 7 8 4
## 77 1 6 3 2 4
## 78 1 2 3 2 2
## 79 -5 -3 -7 -7 -3
## 80 3 0 -1 -1 4
## 81 2 7 4 4 8
## 82 -1 2 5 -1 1
## 83 7 2 5 7 4
## 84 -3 -4 -1 0 -1
## 85 -3 -2 -4 -5 -3
## 86 8 8 5 6 6
## 87 3 5 5 5 5
## 88 3 4 4 5 3
## 89 -1 0 2 -1 2
## 90 0 1 4 7 6
## 91 3 6 2 2 3
## 92 3 2 6 1 -1
## 93 6 4 -1 4 3
## 94 -6 -5 -1 -2 -4
## 95 2 2 3 3 3
## 96 4 5 4 6 7
## 97 -2 -4 0 -1 -3
## 98 3 3 2 3 3
## 99 7 5 6 7 6
## 100 6 0 1 5 2
## 101 3 -2 1 -1 2
## 102 4 1 3 4 3
## 103 0 4 2 -3 4
## 104 5 7 4 4 5
## 105 5 0 4 -1 5
## 106 5 2 3 5 3
## 107 2 3 2 -1 0
## 108 5 2 4 6 0
## 109 5 6 4 5 6
## 110 -6 -7 -6 -9 -13
## 111 5 3 2 3 1
## 112 2 4 2 5 6
## 113 5 5 3 6 2
## 114 -9 -5 -7 -4 -5
## 115 -1 -2 5 -2 2
## 116 -5 -8 -6 -6 -5
## 117 3 4 4 5 8
## 118 5 6 5 7 5
## 119 -3 0 -6 -2 -3
## 120 -4 -2 -4 0 -6
## 121 -4 -1 -3 -2 1
## 122 4 4 2 7 2
## 123 3 4 3 4 6
## 124 6 2 2 1 3
## 125 -2 -4 -2 -3 -2
## 126 1 -1 1 4 4
## 127 9 5 6 8 10
## 128 3 5 3 2 1
## 129 4 3 6 9 9
## 130 -2 2 3 1 3
## 131 1 7 2 3 5
## 132 6 4 3 7 6
## 133 1 0 -1 -2 1
## 134 -3 -6 -6 -4 -2
## 135 5 7 5 6 7
## 136 4 3 3 6 1
## 137 4 2 2 2 6
## 138 -2 -2 5 -2 5
## 139 -1 0 -1 -3 -3
## 140 -3 -2 -2 -3 -3
## 141 5 5 2 5 3
## 142 4 6 3 3 3
## 143 -3 -5 -4 2 -1
## 144 8 9 6 8 4
## 145 -3 -2 1 -4 0
## 146 2 -1 2 1 1
## 147 -1 -3 2 -1 1
## 148 3 3 4 5 3
## 149 3 7 7 5 5
## 150 -2 -6 -5 -1 -6
## 151 -2 2 0 2 -1
## 152 -1 0 -1 -6 -6
## 153 2 2 6 7 6
## 154 -1 -4 -3 -1 -3
## 155 2 1 1 0 -2
## 156 -3 -6 -2 -2 -5
## 157 -3 -3 -3 -5 -5
## 158 6 3 2 5 1
## 159 6 4 3 5 3
## 160 0 -1 -4 0 -1
## 161 -3 -2 -5 -3 1
## 162 3 1 -1 -1 -2
## 163 3 0 4 2 1
## 164 1 -2 -2 -1 -1
## 165 1 0 4 2 2
## 166 -3 -5 -5 -7 -2
## 167 0 1 2 -1 1
## 168 -7 -2 -2 -1 -2
## 169 -1 -3 -6 -3 -5
## 170 6 3 2 4 6
## 171 1 -2 -3 0 -2
## 172 -3 -1 0 -1 -3
## 173 4 4 3 5 5
## 174 -4 -1 -4 0 -2
## 175 5 5 4 3 3
## 176 1 -1 1 1 2
## 177 -1 -2 -2 -2 -1
## 178 3 4 5 2 5
## 179 2 2 7 4 6
## 180 -2 -4 -2 -2 -4
## 181 -4 -2 -5 -2 -4
## 182 4 2 0 4 4
## 183 -3 1 2 3 2
## 184 4 0 2 1 1
## 185 -3 -2 1 3 0
## 186 -3 -4 -1 -4 -2
## 187 -1 0 -4 1 2
## 188 0 -1 0 -2 -2
## 189 3 4 2 1 1
## 190 6 3 1 -1 2
## 191 3 3 2 2 -1
## 192 1 0 -3 -4 0
## 193 -4 1 -5 -1 -5
## 194 2 1 2 2 2
## 195 0 3 2 0 2
## 196 -5 -3 -1 -5 -3
## 197 4 2 4 1 2
## 198 -1 -2 -4 0 -1
## 199 -3 -1 -2 -3 -1
## 200 -1 -2 -6 -5 -2
## 201 -3 0 -4 -2 0
## 202 3 5 1 5 2
## 203 -1 -3 0 -1 -2
## 204 1 -1 1 -2 2
## 205 -2 2 1 1 -5
## 206 1 -1 -2 -3 -1
## 207 5 4 0 3 5
## 208 3 -1 2 5 2
## 209 1 2 3 2 2
## 210 -4 -4 -2 -2 -4
## 211 0 -1 -2 -2 0
## 212 0 -1 1 1 0
## 213 3 4 3 2 3
## 214 -3 -1 -2 -3 -4
## 215 0 -1 2 5 2
## 216 1 1 0 0 -1
## 217 1 -3 -2 1 -2
## 218 1 2 1 1 2
## 219 1 0 -1 1 3
## 220 -1 3 0 0 -1
## 221 0 -2 -1 -1 -1
## 222 2 -4 -2 -5 1
## 223 -1 -3 -2 -2 1
## 224 2 -1 2 -1 0
## 225 2 0 0 3 0
## 226 3 2 0 2 3
## 227 -1 3 -1 3 2
## 228 -2 0 -3 -4 -3
## 229 -4 -2 -3 -4 -2
## 230 1 2 -3 2 -1
## 231 2 1 4 1 4
## 232 2 4 -1 -2 3
## 233 1 1 0 1 1
## 234 2 1 -2 1 -3
## 235 -2 -5 -3 -3 -4
## 236 -4 -2 -1 -3 -4
## 237 -2 -4 -2 -2 -1
## 238 -2 -5 -5 -3 -3
## 239 -2 0 0 1 -3
## 240 -2 -2 -4 -1 0
## 241 -2 0 -7 -2 -1
## 242 -4 -4 -1 -2 -3
## 243 1 2 0 1 0
## 244 -1 -1 2 -2 1
## 245 -2 -4 -3 -4 -4
## 246 0 -1 0 -2 -1
## 247 -2 0 -1 -2 -3
## 248 0 -2 -2 -2 0
## 249 0 2 2 2 1
## 250 -2 -3 0 -1 1
## 251 1 0 -1 0 0
## 252 -2 -3 -2 -2 0
## 253 0 -3 -2 -2 -2
## 254 -4 -3 -2 -4 -4
## 255 -2 -5 -5 -1 -2
## 256 1 -3 -1 -2 -3
## 257 1 -2 4 1 0
## 258 -2 -1 -1 -2 -3
## 259 -3 -4 -1 -1 -1
## 260 -1 2 -1 0 -1
## 261 -1 -3 -1 -3 -1
## 262 -2 -5 -6 -1 -2
## 263 -2 0 -1 -4 -2
## 264 -3 -2 -3 -3 -2
## 265 -6 1 1 -2 -1
## 266 -1 -4 -2 -3 -2
## 267 -1 -2 -1 -1 1
## 268 -2 -1 -3 -2 -2
## 269 -2 -4 -1 -3 -5
## 270 -3 -2 -4 -2 -4
## 271 -4 -4 -3 -2 -4
## 272 0 -3 -4 -1 -1
## 273 -2 -1 -1 -5 0
## 274 -2 -4 -4 -4 -2
## 275 -2 -1 -1 -3 -1
## 276 -2 -3 -6 -2 -5
## 277 1 2 1 0 3
## 278 -4 -7 -5 -3 -2
## 279 1 -3 -5 2 -2
## 280 10 9 7 8 8
## 281 0 0 -1 -1 -1
## 282 4 2 5 6 7
## 283 -1 -4 -2 0 -1
## 284 -2 1 3 1 -3
## 285 -1 -2 -2 0 -1
## 286 -3 -2 2 0 -1
## 287 3 3 -1 2 2
## 288 0 0 0 -2 -2
## 289 2 2 1 0 0
## 290 -2 -6 -3 0 -4
## 291 1 0 -1 1 -1
## 292 -3 0 1 -2 2
## 293 -2 3 1 -1 2
## 294 1 3 3 0 2
## 295 3 3 3 2 3
## 296 0 0 2 -1 -1
## 297 0 1 -1 -1 0
## 298 -1 -1 0 2 -1
## 299 -1 0 -1 -2 0
## 300 1 0 0 -1 0
##
## $overprediction
## 1 2 3 4 5
## 1 0.23529412 0.11764706 0.23529412 0.05882353 0.23529412
## 2 0.50000000 0.37500000 0.31250000 0.50000000 0.31250000
## 3 0.46666667 0.20000000 0.33333333 0.46666667 0.40000000
## 4 0.53333333 0.40000000 0.33333333 0.26666667 0.26666667
## 5 0.50000000 0.44444444 0.22222222 0.55555556 0.61111111
## 6 0.00000000 0.40000000 0.20000000 0.40000000 0.30000000
## 7 0.40000000 0.33333333 0.33333333 0.40000000 0.46666667
## 8 0.40000000 0.40000000 0.53333333 0.33333333 0.33333333
## 9 0.10000000 0.20000000 0.20000000 0.20000000 0.20000000
## 10 0.26666667 0.46666667 0.40000000 0.40000000 0.13333333
## 11 0.55000000 0.35000000 0.40000000 0.45000000 0.50000000
## 12 0.12500000 0.25000000 0.25000000 0.25000000 0.25000000
## 13 0.20000000 0.20000000 0.20000000 0.00000000 0.20000000
## 14 0.30769231 0.30769231 0.23076923 0.30769231 0.30769231
## 15 0.44444444 0.33333333 0.55555556 0.33333333 0.33333333
## 16 0.50000000 0.20000000 0.40000000 0.40000000 0.50000000
## 17 0.35714286 0.42857143 0.50000000 0.07142857 0.21428571
## 18 0.30769231 0.30769231 0.38461538 0.38461538 0.30769231
## 19 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 20 0.53846154 0.53846154 0.46153846 0.53846154 0.46153846
## 21 0.41666667 0.50000000 0.50000000 0.41666667 0.16666667
## 22 0.46153846 0.53846154 0.53846154 0.53846154 0.46153846
## 23 0.43750000 0.37500000 0.43750000 0.37500000 0.50000000
## 24 0.40000000 0.30000000 0.30000000 0.30000000 0.30000000
## 25 0.37500000 0.18750000 0.31250000 0.56250000 0.25000000
## 26 0.21428571 0.07142857 0.35714286 0.14285714 0.14285714
## 27 0.35000000 0.10000000 0.25000000 0.35000000 0.30000000
## 28 0.46153846 0.30769231 0.38461538 0.23076923 0.38461538
## 29 0.25000000 0.16666667 0.25000000 0.08333333 0.25000000
## 30 0.42857143 0.35714286 0.50000000 0.35714286 0.57142857
## 31 0.40000000 0.50000000 0.40000000 0.40000000 0.40000000
## 32 0.33333333 0.22222222 0.11111111 0.33333333 0.22222222
## 33 0.23076923 0.23076923 0.15384615 0.30769231 0.23076923
## 34 0.46153846 0.46153846 0.46153846 0.30769231 0.38461538
## 35 0.20000000 0.10000000 0.10000000 0.10000000 0.10000000
## 36 0.58333333 0.33333333 0.33333333 0.41666667 0.41666667
## 37 0.14285714 0.14285714 0.00000000 0.00000000 0.28571429
## 38 0.38461538 0.53846154 0.38461538 0.46153846 0.46153846
## 39 0.50000000 0.30000000 0.40000000 0.30000000 0.40000000
## 40 0.20000000 0.30000000 0.10000000 0.20000000 0.30000000
## 41 0.22222222 0.11111111 0.11111111 0.11111111 0.22222222
## 42 0.40000000 0.20000000 0.20000000 0.40000000 0.30000000
## 43 0.33333333 0.25000000 0.33333333 0.16666667 0.16666667
## 44 0.20000000 0.20000000 0.10000000 0.10000000 0.30000000
## 45 0.10000000 0.20000000 0.10000000 0.20000000 0.40000000
## 46 0.33333333 0.33333333 0.25000000 0.25000000 0.08333333
## 47 0.14285714 0.28571429 0.35714286 0.28571429 0.35714286
## 48 0.33333333 0.33333333 0.16666667 0.33333333 0.41666667
## 49 0.33333333 0.41666667 0.16666667 0.16666667 0.25000000
## 50 0.12500000 0.12500000 0.00000000 0.25000000 0.00000000
## 51 0.11111111 0.11111111 0.00000000 0.00000000 0.11111111
## 52 0.20000000 0.33333333 0.26666667 0.33333333 0.46666667
## 53 0.36363636 0.00000000 0.27272727 0.36363636 0.36363636
## 54 0.12500000 0.12500000 0.00000000 0.37500000 0.12500000
## 55 0.26666667 0.26666667 0.40000000 0.40000000 0.46666667
## 56 0.25000000 0.50000000 0.37500000 0.50000000 0.31250000
## 57 0.36363636 0.18181818 0.45454545 0.27272727 0.45454545
## 58 0.41666667 0.08333333 0.41666667 0.33333333 0.25000000
## 59 0.33333333 0.11111111 0.33333333 0.11111111 0.33333333
## 60 0.42857143 0.28571429 0.14285714 0.07142857 0.21428571
## 61 0.00000000 0.40000000 0.30000000 0.20000000 0.20000000
## 62 0.09090909 0.09090909 0.18181818 0.09090909 0.09090909
## 63 0.27272727 0.09090909 0.18181818 0.18181818 0.09090909
## 64 0.28571429 0.07142857 0.07142857 0.35714286 0.28571429
## 65 0.10000000 0.10000000 0.40000000 0.00000000 0.20000000
## 66 0.22222222 0.11111111 0.00000000 0.11111111 0.11111111
## 67 0.22222222 0.33333333 0.11111111 0.44444444 0.22222222
## 68 0.11111111 0.33333333 0.33333333 0.11111111 0.33333333
## 69 0.18181818 0.18181818 0.09090909 0.18181818 0.36363636
## 70 0.12500000 0.12500000 0.00000000 0.12500000 0.00000000
## 71 0.50000000 0.21428571 0.35714286 0.42857143 0.42857143
## 72 0.40000000 0.30000000 0.30000000 0.30000000 0.30000000
## 73 0.11111111 0.11111111 0.11111111 0.22222222 0.11111111
## 74 0.36363636 0.36363636 0.09090909 0.09090909 0.36363636
## 75 0.40000000 0.45000000 0.45000000 0.40000000 0.25000000
## 76 0.16666667 0.16666667 0.00000000 0.00000000 0.16666667
## 77 0.25000000 0.25000000 0.25000000 0.25000000 0.12500000
## 78 0.22222222 0.11111111 0.22222222 0.22222222 0.11111111
## 79 0.33333333 0.27777778 0.44444444 0.38888889 0.27777778
## 80 0.15384615 0.30769231 0.38461538 0.23076923 0.15384615
## 81 0.12500000 0.00000000 0.12500000 0.25000000 0.12500000
## 82 0.25000000 0.33333333 0.00000000 0.33333333 0.16666667
## 83 0.00000000 0.37500000 0.12500000 0.00000000 0.37500000
## 84 0.22222222 0.33333333 0.16666667 0.05555556 0.16666667
## 85 0.23529412 0.23529412 0.29411765 0.35294118 0.29411765
## 86 0.00000000 0.10000000 0.10000000 0.10000000 0.00000000
## 87 0.22222222 0.22222222 0.33333333 0.33333333 0.22222222
## 88 0.10000000 0.10000000 0.30000000 0.20000000 0.10000000
## 89 0.33333333 0.41666667 0.25000000 0.41666667 0.25000000
## 90 0.27272727 0.27272727 0.36363636 0.00000000 0.18181818
## 91 0.20000000 0.20000000 0.30000000 0.30000000 0.20000000
## 92 0.44444444 0.11111111 0.11111111 0.22222222 0.44444444
## 93 0.00000000 0.00000000 0.37500000 0.37500000 0.12500000
## 94 0.42857143 0.42857143 0.28571429 0.28571429 0.35714286
## 95 0.50000000 0.50000000 0.25000000 0.25000000 0.25000000
## 96 0.10000000 0.10000000 0.10000000 0.00000000 0.10000000
## 97 0.38461538 0.46153846 0.15384615 0.46153846 0.38461538
## 98 0.18181818 0.18181818 0.18181818 0.18181818 0.09090909
## 99 0.11111111 0.44444444 0.00000000 0.11111111 0.11111111
## 100 0.00000000 0.16666667 0.25000000 0.16666667 0.33333333
## 101 0.15384615 0.46153846 0.30769231 0.30769231 0.38461538
## 102 0.08333333 0.16666667 0.08333333 0.16666667 0.16666667
## 103 0.25000000 0.08333333 0.08333333 0.50000000 0.16666667
## 104 0.25000000 0.25000000 0.00000000 0.25000000 0.25000000
## 105 0.07692308 0.23076923 0.07692308 0.30769231 0.00000000
## 106 0.07692308 0.23076923 0.15384615 0.00000000 0.07692308
## 107 0.14285714 0.07142857 0.14285714 0.28571429 0.28571429
## 108 0.20000000 0.40000000 0.10000000 0.10000000 0.20000000
## 109 0.33333333 0.22222222 0.33333333 0.11111111 0.11111111
## 110 0.30000000 0.35000000 0.30000000 0.45000000 0.65000000
## 111 0.08333333 0.25000000 0.16666667 0.25000000 0.16666667
## 112 0.50000000 0.30000000 0.20000000 0.10000000 0.10000000
## 113 0.16666667 0.16666667 0.33333333 0.00000000 0.16666667
## 114 0.58823529 0.35294118 0.47058824 0.35294118 0.35294118
## 115 0.41666667 0.25000000 0.16666667 0.50000000 0.25000000
## 116 0.26315789 0.42105263 0.31578947 0.31578947 0.26315789
## 117 0.33333333 0.22222222 0.11111111 0.00000000 0.11111111
## 118 0.14285714 0.00000000 0.14285714 0.00000000 0.14285714
## 119 0.23529412 0.17647059 0.35294118 0.23529412 0.17647059
## 120 0.41176471 0.29411765 0.35294118 0.17647059 0.47058824
## 121 0.35714286 0.42857143 0.42857143 0.42857143 0.28571429
## 122 0.11111111 0.11111111 0.33333333 0.33333333 0.33333333
## 123 0.27272727 0.18181818 0.27272727 0.18181818 0.09090909
## 124 0.00000000 0.14285714 0.14285714 0.28571429 0.14285714
## 125 0.22222222 0.27777778 0.22222222 0.22222222 0.22222222
## 126 0.41666667 0.25000000 0.41666667 0.16666667 0.25000000
## 127 0.11111111 0.11111111 0.22222222 0.11111111 0.00000000
## 128 0.22222222 0.11111111 0.22222222 0.44444444 0.11111111
## 129 0.10000000 0.00000000 0.00000000 0.00000000 0.10000000
## 130 0.50000000 0.40000000 0.20000000 0.30000000 0.30000000
## 131 0.40000000 0.10000000 0.10000000 0.10000000 0.20000000
## 132 0.10000000 0.20000000 0.30000000 0.10000000 0.10000000
## 133 0.20000000 0.13333333 0.26666667 0.40000000 0.13333333
## 134 0.40000000 0.53333333 0.53333333 0.40000000 0.26666667
## 135 0.11111111 0.33333333 0.11111111 0.22222222 0.22222222
## 136 0.08333333 0.16666667 0.16666667 0.00000000 0.25000000
## 137 0.09090909 0.18181818 0.36363636 0.18181818 0.09090909
## 138 0.46153846 0.38461538 0.00000000 0.30769231 0.07692308
## 139 0.31250000 0.25000000 0.31250000 0.31250000 0.31250000
## 140 0.23529412 0.17647059 0.29411765 0.17647059 0.23529412
## 141 0.08333333 0.16666667 0.16666667 0.00000000 0.16666667
## 142 0.25000000 0.08333333 0.16666667 0.16666667 0.16666667
## 143 0.31250000 0.37500000 0.37500000 0.06250000 0.18750000
## 144 0.10000000 0.00000000 0.00000000 0.00000000 0.30000000
## 145 0.40000000 0.33333333 0.13333333 0.33333333 0.26666667
## 146 0.13333333 0.20000000 0.13333333 0.20000000 0.13333333
## 147 0.12500000 0.18750000 0.12500000 0.25000000 0.06250000
## 148 0.16666667 0.25000000 0.08333333 0.16666667 0.16666667
## 149 0.30000000 0.00000000 0.20000000 0.10000000 0.20000000
## 150 0.16666667 0.38888889 0.33333333 0.11111111 0.38888889
## 151 0.42857143 0.14285714 0.21428571 0.14285714 0.28571429
## 152 0.18750000 0.18750000 0.18750000 0.43750000 0.50000000
## 153 0.18181818 0.09090909 0.00000000 0.00000000 0.00000000
## 154 0.17647059 0.29411765 0.23529412 0.17647059 0.17647059
## 155 0.13333333 0.20000000 0.13333333 0.20000000 0.33333333
## 156 0.15000000 0.30000000 0.10000000 0.10000000 0.25000000
## 157 0.15000000 0.15000000 0.15000000 0.25000000 0.25000000
## 158 0.09090909 0.09090909 0.27272727 0.00000000 0.18181818
## 159 0.18181818 0.27272727 0.36363636 0.18181818 0.09090909
## 160 0.05882353 0.17647059 0.23529412 0.17647059 0.17647059
## 161 0.37500000 0.25000000 0.31250000 0.25000000 0.18750000
## 162 0.00000000 0.13333333 0.20000000 0.26666667 0.20000000
## 163 0.06666667 0.20000000 0.06666667 0.06666667 0.13333333
## 164 0.12500000 0.25000000 0.31250000 0.25000000 0.18750000
## 165 0.06250000 0.18750000 0.00000000 0.12500000 0.06250000
## 166 0.22222222 0.38888889 0.27777778 0.44444444 0.22222222
## 167 0.15384615 0.23076923 0.23076923 0.30769231 0.23076923
## 168 0.38888889 0.16666667 0.11111111 0.16666667 0.22222222
## 169 0.10526316 0.21052632 0.31578947 0.21052632 0.31578947
## 170 0.00000000 0.07692308 0.07692308 0.00000000 0.07692308
## 171 0.06250000 0.25000000 0.25000000 0.25000000 0.18750000
## 172 0.40000000 0.26666667 0.20000000 0.20000000 0.33333333
## 173 0.20000000 0.20000000 0.20000000 0.10000000 0.20000000
## 174 0.22222222 0.16666667 0.33333333 0.11111111 0.22222222
## 175 0.08333333 0.08333333 0.16666667 0.25000000 0.25000000
## 176 0.14285714 0.21428571 0.21428571 0.21428571 0.14285714
## 177 0.05882353 0.23529412 0.11764706 0.17647059 0.17647059
## 178 0.27272727 0.27272727 0.09090909 0.27272727 0.18181818
## 179 0.16666667 0.25000000 0.08333333 0.00000000 0.08333333
## 180 0.23529412 0.35294118 0.23529412 0.17647059 0.35294118
## 181 0.20000000 0.10000000 0.25000000 0.10000000 0.20000000
## 182 0.07142857 0.21428571 0.21428571 0.14285714 0.07142857
## 183 0.40000000 0.20000000 0.00000000 0.06666667 0.00000000
## 184 0.00000000 0.20000000 0.06666667 0.06666667 0.06666667
## 185 0.33333333 0.26666667 0.20000000 0.06666667 0.20000000
## 186 0.21052632 0.21052632 0.10526316 0.21052632 0.10526316
## 187 0.26666667 0.20000000 0.40000000 0.13333333 0.13333333
## 188 0.11764706 0.17647059 0.11764706 0.17647059 0.29411765
## 189 0.06666667 0.00000000 0.20000000 0.20000000 0.13333333
## 190 0.08333333 0.33333333 0.16666667 0.50000000 0.33333333
## 191 0.06666667 0.13333333 0.00000000 0.13333333 0.33333333
## 192 0.05882353 0.11764706 0.35294118 0.41176471 0.17647059
## 193 0.21052632 0.00000000 0.26315789 0.10526316 0.26315789
## 194 0.14285714 0.21428571 0.07142857 0.21428571 0.07142857
## 195 0.30769231 0.00000000 0.07692308 0.30769231 0.07692308
## 196 0.26315789 0.15789474 0.10526316 0.31578947 0.21052632
## 197 0.00000000 0.08333333 0.08333333 0.16666667 0.25000000
## 198 0.16666667 0.22222222 0.27777778 0.05555556 0.11111111
## 199 0.22222222 0.16666667 0.22222222 0.27777778 0.11111111
## 200 0.11111111 0.22222222 0.38888889 0.27777778 0.11111111
## 201 0.16666667 0.05555556 0.22222222 0.16666667 0.11111111
## 202 0.16666667 0.00000000 0.25000000 0.08333333 0.08333333
## 203 0.25000000 0.43750000 0.12500000 0.25000000 0.37500000
## 204 0.12500000 0.12500000 0.00000000 0.18750000 0.06250000
## 205 0.17647059 0.00000000 0.05882353 0.11764706 0.35294118
## 206 0.05555556 0.11111111 0.16666667 0.27777778 0.16666667
## 207 0.00000000 0.00000000 0.14285714 0.00000000 0.00000000
## 208 0.00000000 0.26666667 0.13333333 0.00000000 0.13333333
## 209 0.28571429 0.14285714 0.07142857 0.07142857 0.14285714
## 210 0.21052632 0.21052632 0.10526316 0.10526316 0.26315789
## 211 0.11111111 0.11111111 0.11111111 0.11111111 0.11111111
## 212 0.06250000 0.12500000 0.06250000 0.12500000 0.06250000
## 213 0.06666667 0.00000000 0.06666667 0.13333333 0.13333333
## 214 0.17647059 0.11764706 0.17647059 0.23529412 0.23529412
## 215 0.20000000 0.26666667 0.13333333 0.00000000 0.13333333
## 216 0.06250000 0.12500000 0.12500000 0.25000000 0.25000000
## 217 0.18750000 0.31250000 0.31250000 0.12500000 0.37500000
## 218 0.06666667 0.06666667 0.13333333 0.06666667 0.13333333
## 219 0.20000000 0.20000000 0.26666667 0.06666667 0.06666667
## 220 0.13333333 0.06666667 0.06666667 0.06666667 0.26666667
## 221 0.05882353 0.11764706 0.11764706 0.11764706 0.17647059
## 222 0.05882353 0.35294118 0.17647059 0.35294118 0.00000000
## 223 0.10526316 0.15789474 0.15789474 0.15789474 0.00000000
## 224 0.06250000 0.25000000 0.06250000 0.18750000 0.12500000
## 225 0.06250000 0.00000000 0.18750000 0.00000000 0.12500000
## 226 0.00000000 0.00000000 0.07142857 0.07142857 0.07142857
## 227 0.28571429 0.14285714 0.21428571 0.00000000 0.21428571
## 228 0.15789474 0.05263158 0.21052632 0.21052632 0.15789474
## 229 0.26315789 0.10526316 0.21052632 0.21052632 0.10526316
## 230 0.06250000 0.06250000 0.31250000 0.00000000 0.12500000
## 231 0.06666667 0.06666667 0.00000000 0.20000000 0.06666667
## 232 0.13333333 0.00000000 0.13333333 0.33333333 0.06666667
## 233 0.06250000 0.12500000 0.12500000 0.06250000 0.12500000
## 234 0.05882353 0.11764706 0.23529412 0.05882353 0.17647059
## 235 0.15789474 0.26315789 0.21052632 0.21052632 0.26315789
## 236 0.29411765 0.23529412 0.17647059 0.29411765 0.29411765
## 237 0.22222222 0.22222222 0.16666667 0.22222222 0.16666667
## 238 0.15789474 0.26315789 0.26315789 0.15789474 0.15789474
## 239 0.23529412 0.11764706 0.11764706 0.05882353 0.29411765
## 240 0.16666667 0.16666667 0.22222222 0.16666667 0.05555556
## 241 0.15789474 0.05263158 0.36842105 0.15789474 0.10526316
## 242 0.26315789 0.26315789 0.10526316 0.15789474 0.21052632
## 243 0.00000000 0.00000000 0.17647059 0.11764706 0.11764706
## 244 0.11764706 0.17647059 0.05882353 0.17647059 0.05882353
## 245 0.10000000 0.20000000 0.15000000 0.20000000 0.20000000
## 246 0.05263158 0.05263158 0.05263158 0.10526316 0.05263158
## 247 0.10526316 0.05263158 0.05263158 0.10526316 0.21052632
## 248 0.11764706 0.17647059 0.17647059 0.17647059 0.11764706
## 249 0.06250000 0.06250000 0.00000000 0.06250000 0.12500000
## 250 0.11764706 0.17647059 0.00000000 0.11764706 0.05882353
## 251 0.11764706 0.11764706 0.17647059 0.17647059 0.11764706
## 252 0.10000000 0.15000000 0.10000000 0.10000000 0.00000000
## 253 0.05263158 0.21052632 0.15789474 0.15789474 0.10526316
## 254 0.21052632 0.15789474 0.10526316 0.26315789 0.21052632
## 255 0.22222222 0.27777778 0.27777778 0.11111111 0.11111111
## 256 0.00000000 0.16666667 0.11111111 0.11111111 0.22222222
## 257 0.18750000 0.18750000 0.00000000 0.12500000 0.18750000
## 258 0.16666667 0.11111111 0.11111111 0.16666667 0.22222222
## 259 0.16666667 0.22222222 0.05555556 0.11111111 0.16666667
## 260 0.05555556 0.00000000 0.05555556 0.11111111 0.11111111
## 261 0.16666667 0.22222222 0.16666667 0.27777778 0.11111111
## 262 0.10000000 0.25000000 0.30000000 0.05000000 0.10000000
## 263 0.10000000 0.00000000 0.05000000 0.20000000 0.10000000
## 264 0.15789474 0.10526316 0.15789474 0.15789474 0.10526316
## 265 0.35294118 0.05882353 0.00000000 0.17647059 0.05882353
## 266 0.05263158 0.21052632 0.10526316 0.15789474 0.10526316
## 267 0.05555556 0.11111111 0.11111111 0.05555556 0.00000000
## 268 0.11111111 0.05555556 0.22222222 0.11111111 0.11111111
## 269 0.10526316 0.21052632 0.05263158 0.15789474 0.26315789
## 270 0.15789474 0.10526316 0.21052632 0.10526316 0.21052632
## 271 0.21052632 0.21052632 0.15789474 0.10526316 0.21052632
## 272 0.05263158 0.21052632 0.21052632 0.05263158 0.10526316
## 273 0.16666667 0.11111111 0.16666667 0.27777778 0.11111111
## 274 0.10526316 0.21052632 0.21052632 0.21052632 0.10526316
## 275 0.16666667 0.11111111 0.05555556 0.16666667 0.11111111
## 276 0.42857143 0.35714286 0.57142857 0.35714286 0.42857143
## 277 0.25000000 0.25000000 0.33333333 0.41666667 0.08333333
## 278 0.30769231 0.53846154 0.38461538 0.23076923 0.30769231
## 279 0.30769231 0.53846154 0.61538462 0.30769231 0.38461538
## 280 0.00000000 0.11111111 0.11111111 0.00000000 0.22222222
## 281 0.05263158 0.05263158 0.10526316 0.10526316 0.10526316
## 282 0.15384615 0.23076923 0.15384615 0.00000000 0.00000000
## 283 0.11111111 0.22222222 0.16666667 0.11111111 0.11111111
## 284 0.28571429 0.14285714 0.07142857 0.28571429 0.28571429
## 285 0.17647059 0.23529412 0.29411765 0.11764706 0.05882353
## 286 0.23529412 0.23529412 0.05882353 0.17647059 0.11764706
## 287 0.00000000 0.06666667 0.20000000 0.13333333 0.06666667
## 288 0.17647059 0.11764706 0.11764706 0.17647059 0.23529412
## 289 0.12500000 0.06250000 0.12500000 0.12500000 0.12500000
## 290 0.16666667 0.33333333 0.22222222 0.11111111 0.22222222
## 291 0.11764706 0.17647059 0.17647059 0.11764706 0.11764706
## 292 0.23529412 0.17647059 0.05882353 0.17647059 0.00000000
## 293 0.25000000 0.00000000 0.06250000 0.18750000 0.00000000
## 294 0.12500000 0.00000000 0.06250000 0.12500000 0.06250000
## 295 0.00000000 0.06250000 0.00000000 0.06250000 0.06250000
## 296 0.05882353 0.11764706 0.05882353 0.11764706 0.11764706
## 297 0.17647059 0.05882353 0.11764706 0.11764706 0.11764706
## 298 0.30000000 0.30000000 0.40000000 0.10000000 0.20000000
## 299 0.05555556 0.05555556 0.05555556 0.16666667 0.05555556
## 300 0.00000000 0.05555556 0.11111111 0.16666667 0.05555556
##
## $underprediction
## 1 2 3 4 5
## 1 0.66666667 0.66666667 0.66666667 0.66666667 0.33333333
## 2 0.25000000 0.25000000 0.00000000 0.00000000 0.00000000
## 3 0.40000000 0.20000000 0.00000000 0.00000000 0.20000000
## 4 0.00000000 0.20000000 0.20000000 0.00000000 0.40000000
## 5 0.00000000 0.50000000 0.00000000 0.00000000 0.00000000
## 6 0.30000000 0.20000000 0.30000000 0.10000000 0.20000000
## 7 0.00000000 0.80000000 0.40000000 0.20000000 0.20000000
## 8 0.00000000 0.00000000 0.00000000 0.00000000 0.20000000
## 9 0.50000000 0.30000000 0.60000000 0.60000000 0.40000000
## 10 0.40000000 0.20000000 0.40000000 0.20000000 0.20000000
## 11 NaN NaN NaN NaN NaN
## 12 0.25000000 0.16666667 0.25000000 0.08333333 0.16666667
## 13 0.20000000 0.10000000 0.10000000 0.20000000 0.20000000
## 14 0.00000000 0.00000000 0.28571429 0.14285714 0.42857143
## 15 0.09090909 0.18181818 0.18181818 0.00000000 0.45454545
## 16 0.20000000 0.30000000 0.20000000 0.00000000 0.20000000
## 17 0.33333333 0.00000000 0.50000000 0.16666667 0.16666667
## 18 0.14285714 0.14285714 0.14285714 0.14285714 0.14285714
## 19 0.33333333 0.26666667 0.20000000 0.26666667 0.13333333
## 20 0.28571429 0.14285714 0.00000000 0.14285714 0.14285714
## 21 0.25000000 0.12500000 0.37500000 0.12500000 0.12500000
## 22 0.42857143 0.28571429 0.28571429 0.14285714 0.00000000
## 23 0.25000000 0.25000000 0.25000000 0.25000000 0.00000000
## 24 0.50000000 0.50000000 0.70000000 0.60000000 0.20000000
## 25 0.25000000 0.00000000 0.00000000 0.00000000 0.50000000
## 26 0.50000000 0.50000000 0.66666667 0.33333333 0.50000000
## 27 NaN NaN NaN NaN NaN
## 28 0.14285714 0.28571429 0.42857143 0.57142857 0.42857143
## 29 0.50000000 0.62500000 0.62500000 0.37500000 0.50000000
## 30 0.16666667 0.00000000 0.33333333 0.00000000 0.33333333
## 31 0.20000000 0.20000000 0.20000000 0.00000000 0.30000000
## 32 0.18181818 0.09090909 0.36363636 0.54545455 0.09090909
## 33 0.28571429 0.14285714 0.14285714 0.42857143 0.28571429
## 34 0.00000000 0.00000000 0.14285714 0.42857143 0.14285714
## 35 0.00000000 0.50000000 0.50000000 0.30000000 0.30000000
## 36 0.25000000 0.00000000 0.37500000 0.37500000 0.12500000
## 37 0.38461538 0.38461538 0.38461538 0.30769231 0.30769231
## 38 0.28571429 0.14285714 0.00000000 0.28571429 0.28571429
## 39 0.50000000 0.20000000 0.20000000 0.40000000 0.50000000
## 40 0.30000000 0.30000000 0.30000000 0.10000000 0.50000000
## 41 0.36363636 0.18181818 0.36363636 0.36363636 0.27272727
## 42 0.70000000 0.50000000 0.60000000 0.50000000 0.20000000
## 43 0.12500000 0.37500000 0.50000000 0.37500000 0.25000000
## 44 0.40000000 0.40000000 0.30000000 0.70000000 0.50000000
## 45 0.40000000 0.50000000 0.20000000 0.30000000 0.10000000
## 46 0.25000000 0.25000000 0.25000000 0.25000000 0.25000000
## 47 0.50000000 0.33333333 0.16666667 0.50000000 0.50000000
## 48 0.12500000 0.50000000 0.37500000 0.62500000 0.37500000
## 49 0.62500000 0.37500000 0.25000000 0.12500000 0.37500000
## 50 0.41666667 0.16666667 0.66666667 0.33333333 0.16666667
## 51 0.72727273 0.54545455 0.54545455 0.54545455 0.54545455
## 52 0.20000000 0.60000000 0.60000000 0.40000000 0.20000000
## 53 0.11111111 0.44444444 0.33333333 0.22222222 0.33333333
## 54 0.33333333 0.33333333 0.41666667 0.08333333 0.50000000
## 55 0.20000000 0.40000000 0.20000000 0.20000000 0.20000000
## 56 0.50000000 0.00000000 0.25000000 0.00000000 0.25000000
## 57 0.22222222 0.44444444 0.33333333 0.33333333 0.55555556
## 58 0.50000000 0.37500000 0.12500000 0.25000000 0.25000000
## 59 0.27272727 0.27272727 0.09090909 0.18181818 0.18181818
## 60 0.83333333 0.16666667 0.50000000 0.66666667 0.83333333
## 61 0.40000000 0.30000000 0.40000000 0.30000000 0.10000000
## 62 0.33333333 0.33333333 0.44444444 0.22222222 0.44444444
## 63 0.66666667 0.44444444 0.88888889 0.66666667 0.44444444
## 64 0.16666667 0.50000000 0.33333333 0.33333333 0.33333333
## 65 0.80000000 0.40000000 0.70000000 0.60000000 0.60000000
## 66 0.63636364 0.45454545 0.54545455 0.54545455 0.81818182
## 67 0.54545455 0.36363636 0.54545455 0.63636364 0.45454545
## 68 0.18181818 0.27272727 0.09090909 0.27272727 0.09090909
## 69 0.55555556 0.55555556 0.66666667 0.55555556 0.66666667
## 70 0.41666667 0.50000000 0.50000000 0.66666667 0.50000000
## 71 0.33333333 0.33333333 0.33333333 0.16666667 0.33333333
## 72 0.50000000 0.40000000 0.40000000 0.30000000 0.40000000
## 73 0.09090909 0.36363636 0.63636364 0.27272727 0.27272727
## 74 0.44444444 0.33333333 0.22222222 0.55555556 0.77777778
## 75 NaN NaN NaN NaN NaN
## 76 0.42857143 0.28571429 0.50000000 0.57142857 0.35714286
## 77 0.25000000 0.66666667 0.41666667 0.33333333 0.41666667
## 78 0.27272727 0.27272727 0.45454545 0.36363636 0.27272727
## 79 0.50000000 1.00000000 0.50000000 0.00000000 1.00000000
## 80 0.71428571 0.57142857 0.57142857 0.28571429 0.85714286
## 81 0.25000000 0.58333333 0.41666667 0.50000000 0.75000000
## 82 0.25000000 0.75000000 0.62500000 0.37500000 0.37500000
## 83 0.58333333 0.41666667 0.50000000 0.58333333 0.58333333
## 84 0.50000000 1.00000000 1.00000000 0.50000000 1.00000000
## 85 0.33333333 0.66666667 0.33333333 0.33333333 0.66666667
## 86 0.80000000 0.90000000 0.60000000 0.70000000 0.60000000
## 87 0.45454545 0.63636364 0.72727273 0.72727273 0.63636364
## 88 0.40000000 0.50000000 0.70000000 0.70000000 0.40000000
## 89 0.37500000 0.62500000 0.62500000 0.50000000 0.62500000
## 90 0.33333333 0.44444444 0.88888889 0.77777778 0.88888889
## 91 0.50000000 0.80000000 0.50000000 0.50000000 0.50000000
## 92 0.63636364 0.27272727 0.63636364 0.27272727 0.27272727
## 93 0.50000000 0.33333333 0.16666667 0.58333333 0.33333333
## 94 0.00000000 0.16666667 0.50000000 0.33333333 0.16666667
## 95 0.50000000 0.50000000 0.41666667 0.41666667 0.41666667
## 96 0.50000000 0.60000000 0.50000000 0.60000000 0.80000000
## 97 0.42857143 0.28571429 0.28571429 0.71428571 0.28571429
## 98 0.55555556 0.55555556 0.44444444 0.55555556 0.44444444
## 99 0.72727273 0.81818182 0.54545455 0.72727273 0.63636364
## 100 0.75000000 0.25000000 0.50000000 0.87500000 0.75000000
## 101 0.71428571 0.57142857 0.71428571 0.42857143 1.00000000
## 102 0.62500000 0.37500000 0.50000000 0.75000000 0.62500000
## 103 0.37500000 0.62500000 0.37500000 0.37500000 0.75000000
## 104 0.58333333 0.75000000 0.33333333 0.50000000 0.58333333
## 105 0.85714286 0.42857143 0.71428571 0.42857143 0.71428571
## 106 0.85714286 0.71428571 0.71428571 0.71428571 0.57142857
## 107 0.66666667 0.66666667 0.66666667 0.50000000 0.66666667
## 108 0.70000000 0.60000000 0.50000000 0.70000000 0.20000000
## 109 0.72727273 0.72727273 0.63636364 0.54545455 0.63636364
## 110 NaN NaN NaN NaN NaN
## 111 0.75000000 0.75000000 0.50000000 0.75000000 0.37500000
## 112 0.70000000 0.70000000 0.40000000 0.60000000 0.70000000
## 113 0.87500000 0.87500000 0.87500000 0.75000000 0.50000000
## 114 0.33333333 0.33333333 0.33333333 0.66666667 0.33333333
## 115 0.50000000 0.12500000 0.87500000 0.50000000 0.62500000
## 116 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 117 0.54545455 0.54545455 0.45454545 0.45454545 0.81818182
## 118 0.46153846 0.46153846 0.46153846 0.53846154 0.46153846
## 119 0.33333333 1.00000000 0.00000000 0.66666667 0.00000000
## 120 1.00000000 1.00000000 0.66666667 1.00000000 0.66666667
## 121 0.16666667 0.83333333 0.50000000 0.66666667 0.83333333
## 122 0.45454545 0.45454545 0.45454545 0.90909091 0.45454545
## 123 0.66666667 0.66666667 0.66666667 0.66666667 0.77777778
## 124 1.00000000 0.66666667 0.66666667 0.83333333 0.83333333
## 125 1.00000000 0.50000000 1.00000000 0.50000000 1.00000000
## 126 0.75000000 0.25000000 0.75000000 0.75000000 0.87500000
## 127 0.90909091 0.54545455 0.72727273 0.81818182 0.90909091
## 128 0.45454545 0.54545455 0.45454545 0.54545455 0.18181818
## 129 0.50000000 0.30000000 0.60000000 0.90000000 1.00000000
## 130 0.30000000 0.60000000 0.50000000 0.40000000 0.60000000
## 131 0.50000000 0.80000000 0.30000000 0.40000000 0.70000000
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## 134 0.60000000 0.40000000 0.40000000 0.40000000 0.40000000
## 135 0.54545455 0.90909091 0.54545455 0.72727273 0.81818182
## 136 0.62500000 0.62500000 0.62500000 0.75000000 0.50000000
## 137 0.55555556 0.44444444 0.66666667 0.44444444 0.77777778
## 138 0.57142857 0.42857143 0.71428571 0.28571429 0.85714286
## 139 1.00000000 1.00000000 1.00000000 0.50000000 0.50000000
## 140 0.33333333 0.33333333 1.00000000 0.00000000 0.33333333
## 141 0.75000000 0.87500000 0.50000000 0.62500000 0.62500000
## 142 0.87500000 0.87500000 0.62500000 0.62500000 0.62500000
## 143 0.50000000 0.25000000 0.50000000 0.75000000 0.50000000
## 144 0.90000000 0.90000000 0.60000000 0.80000000 0.70000000
## 145 0.60000000 0.60000000 0.60000000 0.20000000 0.80000000
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## 148 0.62500000 0.75000000 0.62500000 0.87500000 0.62500000
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## 150 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
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## 153 0.44444444 0.33333333 0.66666667 0.77777778 0.66666667
## 154 0.66666667 0.33333333 0.33333333 0.66666667 0.00000000
## 155 0.80000000 0.80000000 0.60000000 0.60000000 0.60000000
## 156 NaN NaN NaN NaN NaN
## 157 NaN NaN NaN NaN NaN
## 158 0.77777778 0.44444444 0.55555556 0.55555556 0.33333333
## 159 0.88888889 0.77777778 0.77777778 0.77777778 0.44444444
## 160 0.33333333 0.66666667 0.00000000 1.00000000 0.66666667
## 161 0.75000000 0.50000000 0.00000000 0.25000000 1.00000000
## 162 0.60000000 0.60000000 0.40000000 0.60000000 0.20000000
## 163 0.80000000 0.60000000 1.00000000 0.60000000 0.60000000
## 164 0.75000000 0.50000000 0.75000000 0.75000000 0.50000000
## 165 0.50000000 0.75000000 1.00000000 1.00000000 0.75000000
## 166 0.50000000 1.00000000 0.00000000 0.50000000 1.00000000
## 167 0.28571429 0.57142857 0.71428571 0.42857143 0.57142857
## 168 0.00000000 0.50000000 0.00000000 1.00000000 1.00000000
## 169 1.00000000 1.00000000 0.00000000 1.00000000 1.00000000
## 170 0.85714286 0.57142857 0.42857143 0.57142857 1.00000000
## 171 0.50000000 0.50000000 0.25000000 1.00000000 0.25000000
## 172 0.60000000 0.60000000 0.60000000 0.40000000 0.40000000
## 173 0.60000000 0.60000000 0.50000000 0.60000000 0.70000000
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## 175 0.75000000 0.75000000 0.75000000 0.75000000 0.75000000
## 176 0.50000000 0.33333333 0.66666667 0.66666667 0.66666667
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## 178 0.66666667 0.77777778 0.66666667 0.55555556 0.77777778
## 179 0.50000000 0.62500000 1.00000000 0.50000000 0.87500000
## 180 0.66666667 0.66666667 0.66666667 0.33333333 0.66666667
## 181 NaN NaN NaN NaN NaN
## 182 0.83333333 0.83333333 0.50000000 1.00000000 0.83333333
## 183 0.60000000 0.80000000 0.40000000 0.80000000 0.40000000
## 184 0.80000000 0.60000000 0.60000000 0.40000000 0.40000000
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## 187 0.60000000 0.60000000 0.40000000 0.60000000 0.80000000
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## 189 0.80000000 0.80000000 1.00000000 0.80000000 0.60000000
## 190 0.87500000 0.87500000 0.37500000 0.62500000 0.75000000
## 191 0.80000000 1.00000000 0.40000000 0.80000000 0.80000000
## 192 0.66666667 0.66666667 1.00000000 1.00000000 1.00000000
## 193 0.00000000 1.00000000 0.00000000 1.00000000 0.00000000
## 194 0.66666667 0.66666667 0.50000000 0.83333333 0.50000000
## 195 0.57142857 0.42857143 0.42857143 0.57142857 0.42857143
## 196 0.00000000 0.00000000 1.00000000 1.00000000 1.00000000
## 197 0.50000000 0.37500000 0.62500000 0.37500000 0.62500000
## 198 1.00000000 1.00000000 0.50000000 0.50000000 0.50000000
## 199 0.50000000 1.00000000 1.00000000 1.00000000 0.50000000
## 200 0.50000000 1.00000000 0.50000000 0.00000000 0.00000000
## 201 0.00000000 0.50000000 0.00000000 0.50000000 1.00000000
## 202 0.62500000 0.62500000 0.50000000 0.75000000 0.37500000
## 203 0.75000000 1.00000000 0.50000000 0.75000000 1.00000000
## 204 0.75000000 0.25000000 0.25000000 0.25000000 0.75000000
## 205 0.33333333 0.66666667 0.66666667 1.00000000 0.33333333
## 206 1.00000000 0.50000000 0.50000000 1.00000000 1.00000000
## 207 0.83333333 0.66666667 0.33333333 0.50000000 0.83333333
## 208 0.60000000 0.60000000 0.80000000 1.00000000 0.80000000
## 209 0.83333333 0.66666667 0.66666667 0.50000000 0.66666667
## 210 0.00000000 0.00000000 0.00000000 0.00000000 1.00000000
## 211 1.00000000 0.50000000 0.00000000 0.00000000 1.00000000
## 212 0.25000000 0.25000000 0.50000000 0.75000000 0.25000000
## 213 0.80000000 0.80000000 0.80000000 0.80000000 1.00000000
## 214 0.00000000 0.33333333 0.33333333 0.33333333 0.00000000
## 215 0.60000000 0.60000000 0.80000000 1.00000000 0.80000000
## 216 0.50000000 0.75000000 0.50000000 1.00000000 0.75000000
## 217 1.00000000 0.50000000 0.75000000 0.75000000 1.00000000
## 218 0.40000000 0.60000000 0.60000000 0.40000000 0.80000000
## 219 0.80000000 0.60000000 0.60000000 0.40000000 0.80000000
## 220 0.20000000 0.80000000 0.20000000 0.20000000 0.60000000
## 221 0.33333333 0.00000000 0.33333333 0.33333333 0.66666667
## 222 1.00000000 0.66666667 0.33333333 0.33333333 0.33333333
## 223 1.00000000 0.00000000 1.00000000 1.00000000 1.00000000
## 224 0.75000000 0.75000000 0.75000000 0.50000000 0.50000000
## 225 0.75000000 0.00000000 0.75000000 0.75000000 0.50000000
## 226 0.50000000 0.33333333 0.16666667 0.50000000 0.66666667
## 227 0.50000000 0.83333333 0.33333333 0.50000000 0.83333333
## 228 1.00000000 1.00000000 1.00000000 0.00000000 0.00000000
## 229 1.00000000 0.00000000 1.00000000 0.00000000 0.00000000
## 230 0.50000000 0.75000000 0.50000000 0.50000000 0.25000000
## 231 0.60000000 0.40000000 0.80000000 0.80000000 1.00000000
## 232 0.80000000 0.80000000 0.20000000 0.60000000 0.80000000
## 233 0.50000000 0.75000000 0.50000000 0.50000000 0.75000000
## 234 1.00000000 1.00000000 0.66666667 0.66666667 0.00000000
## 235 1.00000000 0.00000000 1.00000000 1.00000000 1.00000000
## 236 0.33333333 0.66666667 0.66666667 0.66666667 0.33333333
## 237 1.00000000 0.00000000 0.50000000 1.00000000 1.00000000
## 238 1.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 239 0.66666667 0.66666667 0.66666667 0.66666667 0.66666667
## 240 0.50000000 0.50000000 0.00000000 1.00000000 0.50000000
## 241 1.00000000 1.00000000 0.00000000 1.00000000 1.00000000
## 242 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## 243 0.33333333 0.66666667 1.00000000 1.00000000 0.66666667
## 244 0.33333333 0.66666667 1.00000000 0.33333333 0.66666667
## 245 NaN NaN NaN NaN NaN
## 246 1.00000000 0.00000000 1.00000000 0.00000000 0.00000000
## 247 0.00000000 1.00000000 0.00000000 0.00000000 1.00000000
## 248 0.66666667 0.33333333 0.33333333 0.33333333 0.66666667
## 249 0.25000000 0.75000000 0.50000000 0.75000000 0.75000000
## 250 0.00000000 0.00000000 0.00000000 0.33333333 0.66666667
## 251 1.00000000 0.66666667 0.66666667 1.00000000 0.66666667
## 252 NaN NaN NaN NaN NaN
## 253 1.00000000 1.00000000 1.00000000 1.00000000 0.00000000
## 254 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000
## 255 1.00000000 0.00000000 0.00000000 0.50000000 0.00000000
## 256 0.50000000 0.00000000 0.50000000 0.00000000 0.50000000
## 257 1.00000000 0.25000000 1.00000000 0.75000000 0.75000000
## 258 0.50000000 0.50000000 0.50000000 0.50000000 0.50000000
## 259 0.00000000 0.00000000 0.00000000 0.50000000 1.00000000
## 260 0.00000000 1.00000000 0.00000000 1.00000000 0.50000000
## 261 1.00000000 0.50000000 1.00000000 1.00000000 0.50000000
## 262 NaN NaN NaN NaN NaN
## 263 NaN NaN NaN NaN NaN
## 264 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 265 0.00000000 0.66666667 0.33333333 0.33333333 0.00000000
## 266 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 267 0.00000000 0.00000000 0.50000000 0.00000000 0.50000000
## 268 0.00000000 0.00000000 0.50000000 0.00000000 0.00000000
## 269 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 270 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 271 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 272 1.00000000 1.00000000 0.00000000 0.00000000 1.00000000
## 273 0.50000000 0.50000000 1.00000000 0.00000000 1.00000000
## 274 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 275 0.50000000 0.50000000 0.00000000 0.00000000 0.50000000
## 276 0.66666667 0.33333333 0.33333333 0.50000000 0.16666667
## 277 0.50000000 0.62500000 0.62500000 0.62500000 0.50000000
## 278 0.00000000 0.00000000 0.00000000 0.00000000 0.28571429
## 279 0.71428571 0.57142857 0.42857143 0.85714286 0.42857143
## 280 0.90909091 0.90909091 0.72727273 0.72727273 0.90909091
## 281 1.00000000 1.00000000 1.00000000 1.00000000 1.00000000
## 282 0.85714286 0.71428571 1.00000000 0.85714286 1.00000000
## 283 0.50000000 0.00000000 0.50000000 1.00000000 0.50000000
## 284 0.33333333 0.50000000 0.66666667 0.83333333 0.16666667
## 285 0.66666667 0.66666667 1.00000000 0.66666667 0.00000000
## 286 0.33333333 0.66666667 1.00000000 1.00000000 0.33333333
## 287 0.60000000 0.80000000 0.40000000 0.80000000 0.60000000
## 288 1.00000000 0.66666667 0.66666667 0.33333333 0.66666667
## 289 1.00000000 0.75000000 0.75000000 0.50000000 0.50000000
## 290 0.50000000 0.00000000 0.50000000 1.00000000 0.00000000
## 291 1.00000000 1.00000000 0.66666667 1.00000000 0.33333333
## 292 0.33333333 1.00000000 0.66666667 0.33333333 0.66666667
## 293 0.50000000 0.75000000 0.50000000 0.50000000 0.50000000
## 294 0.75000000 0.75000000 1.00000000 0.50000000 0.75000000
## 295 0.75000000 1.00000000 0.75000000 0.75000000 1.00000000
## 296 0.33333333 0.66666667 1.00000000 0.33333333 0.33333333
## 297 1.00000000 0.66666667 0.33333333 0.33333333 0.66666667
## 298 0.20000000 0.20000000 0.40000000 0.30000000 0.10000000
## 299 0.00000000 0.50000000 0.00000000 0.50000000 0.50000000
## 300 0.50000000 0.50000000 1.00000000 1.00000000 0.50000000
##
## $prediction.success
## 1 2 3 4 5
## 1 0.70 0.80 0.70 0.85 0.75
## 2 0.55 0.65 0.75 0.60 0.75
## 3 0.55 0.80 0.75 0.65 0.65
## 4 0.60 0.65 0.70 0.80 0.70
## 5 0.55 0.55 0.80 0.50 0.45
## 6 0.85 0.70 0.75 0.75 0.75
## 7 0.70 0.55 0.65 0.65 0.60
## 8 0.70 0.70 0.60 0.75 0.70
## 9 0.70 0.75 0.60 0.60 0.70
## 10 0.70 0.60 0.60 0.65 0.85
## 11 0.45 0.65 0.60 0.55 0.50
## 12 0.80 0.80 0.75 0.85 0.80
## 13 0.80 0.85 0.85 0.90 0.80
## 14 0.80 0.80 0.75 0.75 0.65
## 15 0.75 0.75 0.65 0.85 0.60
## 16 0.65 0.75 0.70 0.80 0.65
## 17 0.65 0.70 0.50 0.90 0.80
## 18 0.75 0.75 0.70 0.70 0.75
## 19 0.75 0.80 0.85 0.80 0.90
## 20 0.55 0.60 0.70 0.60 0.65
## 21 0.65 0.65 0.55 0.70 0.85
## 22 0.55 0.55 0.55 0.60 0.70
## 23 0.60 0.65 0.60 0.65 0.60
## 24 0.55 0.60 0.50 0.55 0.75
## 25 0.65 0.85 0.75 0.55 0.70
## 26 0.70 0.80 0.55 0.80 0.75
## 27 0.65 0.90 0.75 0.65 0.70
## 28 0.65 0.70 0.60 0.65 0.60
## 29 0.65 0.65 0.60 0.80 0.65
## 30 0.65 0.75 0.55 0.75 0.50
## 31 0.70 0.65 0.70 0.80 0.65
## 32 0.75 0.85 0.75 0.55 0.85
## 33 0.75 0.80 0.85 0.65 0.75
## 34 0.70 0.70 0.65 0.65 0.70
## 35 0.90 0.70 0.70 0.80 0.80
## 36 0.55 0.80 0.65 0.60 0.70
## 37 0.70 0.70 0.75 0.80 0.70
## 38 0.65 0.60 0.75 0.60 0.60
## 39 0.50 0.75 0.70 0.65 0.55
## 40 0.75 0.70 0.80 0.85 0.60
## 41 0.70 0.85 0.75 0.75 0.75
## 42 0.45 0.65 0.60 0.55 0.75
## 43 0.75 0.70 0.60 0.75 0.80
## 44 0.70 0.70 0.80 0.60 0.60
## 45 0.75 0.65 0.85 0.75 0.75
## 46 0.70 0.70 0.75 0.75 0.85
## 47 0.75 0.70 0.70 0.65 0.60
## 48 0.75 0.60 0.75 0.55 0.60
## 49 0.55 0.60 0.80 0.85 0.70
## 50 0.70 0.85 0.60 0.70 0.90
## 51 0.55 0.65 0.70 0.70 0.65
## 52 0.80 0.60 0.65 0.65 0.60
## 53 0.75 0.80 0.70 0.70 0.65
## 54 0.75 0.75 0.75 0.80 0.65
## 55 0.75 0.70 0.65 0.65 0.60
## 56 0.70 0.60 0.65 0.60 0.70
## 57 0.70 0.70 0.60 0.70 0.50
## 58 0.55 0.80 0.70 0.70 0.75
## 59 0.70 0.80 0.80 0.85 0.75
## 60 0.45 0.75 0.75 0.75 0.60
## 61 0.80 0.65 0.65 0.75 0.85
## 62 0.80 0.80 0.70 0.85 0.75
## 63 0.55 0.75 0.50 0.60 0.75
## 64 0.75 0.80 0.85 0.65 0.70
## 65 0.55 0.75 0.45 0.70 0.60
## 66 0.55 0.70 0.70 0.65 0.50
## 67 0.60 0.65 0.65 0.45 0.65
## 68 0.85 0.70 0.80 0.80 0.80
## 69 0.65 0.65 0.65 0.65 0.50
## 70 0.70 0.65 0.70 0.55 0.70
## 71 0.55 0.75 0.65 0.65 0.60
## 72 0.55 0.65 0.65 0.70 0.65
## 73 0.90 0.75 0.60 0.75 0.80
## 74 0.60 0.65 0.85 0.70 0.45
## 75 0.60 0.55 0.55 0.60 0.75
## 76 0.65 0.75 0.65 0.60 0.70
## 77 0.75 0.50 0.65 0.70 0.70
## 78 0.75 0.80 0.65 0.70 0.80
## 79 0.65 0.65 0.55 0.65 0.65
## 80 0.65 0.60 0.55 0.75 0.60
## 81 0.80 0.65 0.70 0.60 0.50
## 82 0.75 0.50 0.75 0.65 0.75
## 83 0.65 0.60 0.65 0.65 0.50
## 84 0.75 0.60 0.75 0.90 0.75
## 85 0.75 0.70 0.70 0.65 0.65
## 86 0.60 0.50 0.65 0.60 0.70
## 87 0.65 0.55 0.45 0.45 0.55
## 88 0.75 0.70 0.50 0.55 0.75
## 89 0.65 0.50 0.60 0.55 0.60
## 90 0.70 0.65 0.40 0.65 0.50
## 91 0.65 0.50 0.60 0.60 0.65
## 92 0.45 0.80 0.60 0.75 0.65
## 93 0.70 0.80 0.75 0.50 0.75
## 94 0.70 0.65 0.65 0.70 0.70
## 95 0.50 0.50 0.65 0.65 0.65
## 96 0.70 0.65 0.70 0.70 0.55
## 97 0.60 0.60 0.80 0.45 0.65
## 98 0.65 0.65 0.70 0.65 0.75
## 99 0.55 0.35 0.70 0.55 0.60
## 100 0.70 0.80 0.65 0.55 0.50
## 101 0.65 0.50 0.55 0.65 0.40
## 102 0.70 0.75 0.75 0.60 0.65
## 103 0.70 0.70 0.80 0.55 0.60
## 104 0.55 0.45 0.80 0.60 0.55
## 105 0.65 0.70 0.70 0.65 0.75
## 106 0.65 0.60 0.65 0.75 0.75
## 107 0.70 0.75 0.70 0.65 0.60
## 108 0.55 0.50 0.70 0.60 0.80
## 109 0.45 0.50 0.50 0.65 0.60
## 110 0.70 0.65 0.70 0.55 0.35
## 111 0.65 0.55 0.70 0.55 0.75
## 112 0.40 0.50 0.70 0.65 0.60
## 113 0.55 0.55 0.45 0.70 0.70
## 114 0.45 0.65 0.55 0.60 0.65
## 115 0.55 0.80 0.55 0.50 0.60
## 116 0.75 0.60 0.70 0.70 0.75
## 117 0.55 0.60 0.70 0.75 0.50
## 118 0.65 0.70 0.65 0.65 0.65
## 119 0.75 0.70 0.70 0.70 0.85
## 120 0.50 0.60 0.60 0.70 0.50
## 121 0.70 0.45 0.55 0.50 0.55
## 122 0.70 0.70 0.60 0.35 0.60
## 123 0.55 0.60 0.55 0.60 0.60
## 124 0.70 0.70 0.70 0.55 0.65
## 125 0.70 0.70 0.70 0.75 0.70
## 126 0.45 0.75 0.45 0.60 0.50
## 127 0.45 0.65 0.50 0.50 0.50
## 128 0.65 0.65 0.65 0.50 0.85
## 129 0.70 0.85 0.70 0.55 0.45
## 130 0.60 0.50 0.65 0.65 0.55
## 131 0.55 0.55 0.80 0.75 0.55
## 132 0.60 0.60 0.55 0.55 0.60
## 133 0.65 0.80 0.65 0.50 0.75
## 134 0.55 0.50 0.50 0.60 0.70
## 135 0.65 0.35 0.65 0.50 0.45
## 136 0.70 0.65 0.65 0.70 0.65
## 137 0.70 0.70 0.50 0.70 0.60
## 138 0.50 0.60 0.75 0.70 0.65
## 139 0.55 0.60 0.55 0.65 0.65
## 140 0.75 0.80 0.60 0.85 0.75
## 141 0.65 0.55 0.70 0.75 0.65
## 142 0.50 0.60 0.65 0.65 0.65
## 143 0.65 0.65 0.60 0.80 0.75
## 144 0.50 0.55 0.70 0.60 0.50
## 145 0.55 0.60 0.75 0.70 0.60
## 146 0.70 0.75 0.70 0.65 0.75
## 147 0.85 0.85 0.70 0.65 0.85
## 148 0.65 0.55 0.70 0.55 0.65
## 149 0.55 0.65 0.45 0.65 0.55
## 150 0.80 0.60 0.65 0.85 0.60
## 151 0.50 0.70 0.70 0.70 0.65
## 152 0.75 0.70 0.75 0.60 0.50
## 153 0.70 0.80 0.70 0.65 0.70
## 154 0.75 0.70 0.75 0.75 0.85
## 155 0.70 0.65 0.75 0.70 0.60
## 156 0.85 0.70 0.90 0.90 0.75
## 157 0.85 0.85 0.85 0.75 0.75
## 158 0.60 0.75 0.60 0.75 0.75
## 159 0.50 0.50 0.45 0.55 0.75
## 160 0.90 0.75 0.80 0.70 0.75
## 161 0.55 0.70 0.75 0.75 0.65
## 162 0.85 0.75 0.75 0.65 0.80
## 163 0.75 0.70 0.70 0.80 0.75
## 164 0.75 0.70 0.60 0.65 0.75
## 165 0.85 0.70 0.80 0.70 0.80
## 166 0.75 0.55 0.75 0.55 0.70
## 167 0.80 0.65 0.60 0.65 0.65
## 168 0.65 0.80 0.90 0.75 0.70
## 169 0.85 0.75 0.70 0.75 0.65
## 170 0.70 0.75 0.80 0.80 0.60
## 171 0.85 0.70 0.75 0.60 0.80
## 172 0.55 0.65 0.70 0.75 0.65
## 173 0.60 0.60 0.65 0.65 0.55
## 174 0.80 0.75 0.60 0.80 0.70
## 175 0.65 0.65 0.60 0.55 0.55
## 176 0.75 0.75 0.65 0.65 0.70
## 177 0.95 0.70 0.90 0.80 0.75
## 178 0.55 0.50 0.65 0.60 0.55
## 179 0.70 0.60 0.55 0.80 0.60
## 180 0.70 0.60 0.70 0.80 0.60
## 181 0.80 0.90 0.75 0.90 0.80
## 182 0.70 0.60 0.70 0.60 0.70
## 183 0.55 0.65 0.90 0.75 0.90
## 184 0.80 0.70 0.80 0.85 0.85
## 185 0.65 0.70 0.65 0.75 0.70
## 186 0.75 0.80 0.85 0.80 0.90
## 187 0.65 0.70 0.60 0.75 0.70
## 188 0.80 0.75 0.80 0.80 0.60
## 189 0.75 0.80 0.60 0.65 0.75
## 190 0.60 0.45 0.75 0.45 0.50
## 191 0.75 0.65 0.90 0.70 0.55
## 192 0.85 0.80 0.55 0.50 0.70
## 193 0.80 0.95 0.75 0.85 0.75
## 194 0.70 0.65 0.80 0.60 0.80
## 195 0.60 0.85 0.80 0.60 0.80
## 196 0.75 0.85 0.85 0.65 0.75
## 197 0.80 0.80 0.70 0.75 0.60
## 198 0.75 0.70 0.70 0.90 0.85
## 199 0.75 0.75 0.70 0.65 0.85
## 200 0.85 0.70 0.60 0.75 0.90
## 201 0.85 0.90 0.80 0.80 0.80
## 202 0.65 0.75 0.65 0.65 0.80
## 203 0.65 0.45 0.80 0.65 0.50
## 204 0.75 0.85 0.95 0.80 0.80
## 205 0.80 0.90 0.85 0.75 0.65
## 206 0.85 0.85 0.80 0.65 0.75
## 207 0.75 0.80 0.80 0.85 0.75
## 208 0.85 0.65 0.70 0.75 0.70
## 209 0.55 0.70 0.75 0.80 0.70
## 210 0.80 0.80 0.90 0.90 0.70
## 211 0.80 0.85 0.90 0.90 0.80
## 212 0.90 0.85 0.85 0.75 0.90
## 213 0.75 0.80 0.75 0.70 0.65
## 214 0.85 0.85 0.80 0.75 0.80
## 215 0.70 0.65 0.70 0.75 0.70
## 216 0.85 0.75 0.80 0.60 0.65
## 217 0.65 0.65 0.60 0.75 0.50
## 218 0.85 0.80 0.75 0.85 0.70
## 219 0.65 0.70 0.65 0.85 0.75
## 220 0.85 0.75 0.90 0.90 0.65
## 221 0.90 0.90 0.85 0.85 0.75
## 222 0.80 0.60 0.80 0.65 0.95
## 223 0.85 0.85 0.80 0.80 0.95
## 224 0.80 0.65 0.80 0.75 0.80
## 225 0.80 1.00 0.70 0.85 0.80
## 226 0.85 0.90 0.90 0.80 0.75
## 227 0.65 0.65 0.75 0.85 0.60
## 228 0.80 0.90 0.75 0.80 0.85
## 229 0.70 0.90 0.75 0.80 0.90
## 230 0.85 0.80 0.65 0.90 0.85
## 231 0.80 0.85 0.80 0.65 0.70
## 232 0.70 0.80 0.85 0.60 0.75
## 233 0.85 0.75 0.80 0.85 0.75
## 234 0.80 0.75 0.70 0.85 0.85
## 235 0.80 0.75 0.75 0.75 0.70
## 236 0.70 0.70 0.75 0.65 0.70
## 237 0.70 0.80 0.80 0.70 0.75
## 238 0.80 0.75 0.75 0.85 0.85
## 239 0.70 0.80 0.80 0.85 0.65
## 240 0.80 0.80 0.80 0.75 0.90
## 241 0.80 0.90 0.65 0.80 0.85
## 242 0.70 0.70 0.85 0.80 0.75
## 243 0.95 0.90 0.70 0.75 0.80
## 244 0.85 0.75 0.80 0.80 0.85
## 245 0.90 0.80 0.85 0.80 0.80
## 246 0.90 0.95 0.90 0.90 0.95
## 247 0.90 0.90 0.95 0.90 0.75
## 248 0.80 0.80 0.80 0.80 0.80
## 249 0.90 0.80 0.90 0.80 0.75
## 250 0.90 0.85 1.00 0.85 0.85
## 251 0.75 0.80 0.75 0.70 0.80
## 252 0.90 0.85 0.90 0.90 1.00
## 253 0.90 0.75 0.80 0.80 0.90
## 254 0.80 0.85 0.90 0.70 0.80
## 255 0.70 0.75 0.75 0.85 0.90
## 256 0.95 0.85 0.85 0.90 0.75
## 257 0.65 0.80 0.80 0.75 0.70
## 258 0.80 0.85 0.85 0.80 0.75
## 259 0.85 0.80 0.95 0.85 0.75
## 260 0.95 0.90 0.95 0.80 0.85
## 261 0.75 0.75 0.75 0.65 0.85
## 262 0.90 0.75 0.70 0.95 0.90
## 263 0.90 1.00 0.95 0.80 0.90
## 264 0.85 0.90 0.85 0.85 0.90
## 265 0.70 0.85 0.95 0.80 0.95
## 266 0.95 0.80 0.90 0.85 0.90
## 267 0.95 0.90 0.85 0.95 0.95
## 268 0.90 0.95 0.75 0.90 0.90
## 269 0.90 0.80 0.95 0.85 0.75
## 270 0.85 0.90 0.80 0.90 0.80
## 271 0.80 0.80 0.85 0.90 0.80
## 272 0.90 0.75 0.80 0.95 0.85
## 273 0.80 0.85 0.75 0.75 0.80
## 274 0.90 0.80 0.80 0.80 0.90
## 275 0.80 0.85 0.95 0.85 0.85
## 276 0.50 0.65 0.50 0.60 0.65
## 277 0.65 0.60 0.55 0.50 0.75
## 278 0.80 0.65 0.75 0.85 0.70
## 279 0.55 0.45 0.45 0.50 0.60
## 280 0.50 0.45 0.55 0.60 0.40
## 281 0.90 0.90 0.85 0.85 0.85
## 282 0.60 0.60 0.55 0.70 0.65
## 283 0.85 0.80 0.80 0.80 0.85
## 284 0.70 0.75 0.75 0.55 0.75
## 285 0.75 0.70 0.60 0.80 0.95
## 286 0.75 0.70 0.80 0.70 0.85
## 287 0.85 0.75 0.75 0.70 0.80
## 288 0.70 0.80 0.80 0.80 0.70
## 289 0.70 0.80 0.75 0.80 0.80
## 290 0.80 0.70 0.75 0.80 0.80
## 291 0.75 0.70 0.75 0.75 0.85
## 292 0.75 0.70 0.85 0.80 0.90
## 293 0.70 0.85 0.85 0.75 0.90
## 294 0.75 0.85 0.75 0.80 0.80
## 295 0.85 0.75 0.85 0.80 0.75
## 296 0.90 0.80 0.80 0.85 0.85
## 297 0.70 0.85 0.85 0.85 0.80
## 298 0.75 0.75 0.60 0.80 0.85
## 299 0.95 0.90 0.95 0.80 0.90
## 300 0.95 0.90 0.80 0.75 0.90
##
## $sensitivity
## 1 2 3 4 5
## 1 0.2000000 0.3333333 0.2000000 0.5000000 0.3333333
## 2 0.2727273 0.3333333 0.4444444 0.3333333 0.4444444
## 3 0.3000000 0.5714286 0.5000000 0.4166667 0.4000000
## 4 0.3846154 0.4000000 0.4444444 0.5555556 0.4285714
## 5 0.1818182 0.1111111 0.3333333 0.1666667 0.1538462
## 6 1.0000000 0.6666667 0.7777778 0.6923077 0.7272727
## 7 0.4545455 0.1666667 0.3750000 0.4000000 0.3636364
## 8 0.4545455 0.4545455 0.3846154 0.5000000 0.4444444
## 9 0.8333333 0.7777778 0.6666667 0.6666667 0.7500000
## 10 0.4285714 0.3636364 0.3333333 0.4000000 0.6666667
## 11 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 12 0.9000000 0.8333333 0.8181818 0.8461538 0.8333333
## 13 0.8000000 0.8181818 0.8181818 1.0000000 0.8000000
## 14 0.6363636 0.6363636 0.6250000 0.6000000 0.5000000
## 15 0.7142857 0.7500000 0.6428571 0.7857143 0.6666667
## 16 0.6153846 0.7777778 0.6666667 0.7142857 0.6153846
## 17 0.4444444 0.5000000 0.3000000 0.8333333 0.6250000
## 18 0.6000000 0.6000000 0.5454545 0.5454545 0.6000000
## 19 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 20 0.4166667 0.4615385 0.5384615 0.4615385 0.5000000
## 21 0.5454545 0.5384615 0.4545455 0.5833333 0.7777778
## 22 0.4000000 0.4166667 0.4166667 0.4615385 0.5384615
## 23 0.3000000 0.3333333 0.3000000 0.3333333 0.3333333
## 24 0.5555556 0.6250000 0.5000000 0.5714286 0.7272727
## 25 0.3333333 0.5714286 0.4444444 0.3076923 0.3333333
## 26 0.5000000 0.7500000 0.2857143 0.6666667 0.6000000
## 27 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 28 0.5000000 0.5555556 0.4444444 0.5000000 0.4444444
## 29 0.5714286 0.6000000 0.5000000 0.8333333 0.5714286
## 30 0.4545455 0.5454545 0.3636364 0.5454545 0.3333333
## 31 0.6666667 0.6153846 0.6666667 0.7142857 0.6363636
## 32 0.7500000 0.8333333 0.8750000 0.6250000 0.8333333
## 33 0.6250000 0.6666667 0.7500000 0.5000000 0.6250000
## 34 0.5384615 0.5384615 0.5000000 0.5000000 0.5454545
## 35 0.8333333 0.8333333 0.8333333 0.8750000 0.8750000
## 36 0.4615385 0.6666667 0.5555556 0.5000000 0.5833333
## 37 0.8888889 0.8888889 1.0000000 1.0000000 0.8181818
## 38 0.5000000 0.4615385 0.5833333 0.4545455 0.4545455
## 39 0.5000000 0.7272727 0.6666667 0.6666667 0.5555556
## 40 0.7777778 0.7000000 0.8750000 0.8181818 0.6250000
## 41 0.7777778 0.9000000 0.8750000 0.8750000 0.8000000
## 42 0.4285714 0.7142857 0.6666667 0.5555556 0.7272727
## 43 0.6363636 0.6250000 0.5000000 0.7142857 0.7500000
## 44 0.7500000 0.7500000 0.8750000 0.7500000 0.6250000
## 45 0.8571429 0.7142857 0.8888889 0.7777778 0.6923077
## 46 0.6000000 0.6000000 0.6666667 0.6666667 0.8571429
## 47 0.6000000 0.5000000 0.5000000 0.4285714 0.3750000
## 48 0.6363636 0.5000000 0.7142857 0.4285714 0.5000000
## 49 0.4285714 0.5000000 0.7500000 0.7777778 0.6250000
## 50 0.8750000 0.9090909 1.0000000 0.8000000 1.0000000
## 51 0.7500000 0.8333333 1.0000000 1.0000000 0.8333333
## 52 0.5714286 0.2857143 0.3333333 0.3750000 0.3636364
## 53 0.6666667 1.0000000 0.6666667 0.6363636 0.6000000
## 54 0.8888889 0.8888889 1.0000000 0.7857143 0.8571429
## 55 0.5000000 0.4285714 0.4000000 0.4000000 0.3636364
## 56 0.3333333 0.3333333 0.3333333 0.3333333 0.3750000
## 57 0.6363636 0.7142857 0.5454545 0.6666667 0.4444444
## 58 0.4444444 0.8333333 0.5833333 0.6000000 0.6666667
## 59 0.7272727 0.8888889 0.7692308 0.9000000 0.7500000
## 60 0.1428571 0.5555556 0.6000000 0.6666667 0.2500000
## 61 1.0000000 0.6363636 0.6666667 0.7777778 0.8181818
## 62 0.8571429 0.8571429 0.7142857 0.8750000 0.8333333
## 63 0.5000000 0.8333333 0.3333333 0.6000000 0.8333333
## 64 0.5555556 0.7500000 0.8000000 0.4444444 0.5000000
## 65 0.6666667 0.8571429 0.4285714 1.0000000 0.6666667
## 66 0.6666667 0.8571429 1.0000000 0.8333333 0.6666667
## 67 0.7142857 0.7000000 0.8333333 0.5000000 0.7500000
## 68 0.9000000 0.7272727 0.7692308 0.8888889 0.7692308
## 69 0.6666667 0.6666667 0.7500000 0.6666667 0.4285714
## 70 0.8750000 0.8571429 1.0000000 0.8000000 1.0000000
## 71 0.3636364 0.5714286 0.4444444 0.4545455 0.4000000
## 72 0.5555556 0.6666667 0.6666667 0.7000000 0.6666667
## 73 0.9090909 0.8750000 0.8000000 0.8000000 0.8888889
## 74 0.5555556 0.6000000 0.8750000 0.8000000 0.3333333
## 75 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 76 0.8888889 0.9090909 1.0000000 1.0000000 0.9000000
## 77 0.8181818 0.6666667 0.7777778 0.8000000 0.8750000
## 78 0.8000000 0.8888889 0.7500000 0.7777778 0.8888889
## 79 0.1428571 0.0000000 0.1111111 0.2222222 0.0000000
## 80 0.5000000 0.4285714 0.3750000 0.6250000 0.3333333
## 81 0.9000000 1.0000000 0.8750000 0.7500000 0.7500000
## 82 0.6666667 0.3333333 1.0000000 0.5555556 0.7142857
## 83 1.0000000 0.7000000 0.8571429 1.0000000 0.6250000
## 84 0.2000000 0.0000000 0.0000000 0.5000000 0.0000000
## 85 0.3333333 0.2000000 0.2857143 0.2500000 0.1666667
## 86 1.0000000 0.5000000 0.8000000 0.7500000 1.0000000
## 87 0.7500000 0.6666667 0.5000000 0.5000000 0.6666667
## 88 0.8571429 0.8333333 0.5000000 0.6000000 0.8571429
## 89 0.5555556 0.3750000 0.5000000 0.4444444 0.5000000
## 90 0.6666667 0.6250000 0.2000000 1.0000000 0.3333333
## 91 0.7142857 0.5000000 0.6250000 0.6250000 0.7142857
## 92 0.5000000 0.8888889 0.8000000 0.8000000 0.6666667
## 93 1.0000000 1.0000000 0.7692308 0.6250000 0.8888889
## 94 0.5000000 0.4545455 0.4285714 0.5000000 0.5000000
## 95 0.6000000 0.6000000 0.7777778 0.7777778 0.7777778
## 96 0.8333333 0.8000000 0.8333333 1.0000000 0.6666667
## 97 0.4444444 0.4545455 0.7142857 0.2500000 0.5000000
## 98 0.6666667 0.6666667 0.7142857 0.6666667 0.8333333
## 99 0.7500000 0.3333333 1.0000000 0.7500000 0.8000000
## 100 1.0000000 0.7500000 0.5714286 0.3333333 0.3333333
## 101 0.5000000 0.3333333 0.3333333 0.5000000 0.0000000
## 102 0.7500000 0.7142857 0.8000000 0.5000000 0.6000000
## 103 0.6250000 0.7500000 0.8333333 0.4545455 0.5000000
## 104 0.7142857 0.6000000 1.0000000 0.7500000 0.7142857
## 105 0.5000000 0.5714286 0.6666667 0.5000000 1.0000000
## 106 0.5000000 0.4000000 0.5000000 1.0000000 0.7500000
## 107 0.5000000 0.6666667 0.5000000 0.4285714 0.3333333
## 108 0.6000000 0.5000000 0.8333333 0.7500000 0.8000000
## 109 0.5000000 0.6000000 0.5714286 0.8333333 0.8000000
## 110 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 111 0.6666667 0.4000000 0.6666667 0.4000000 0.7142857
## 112 0.3750000 0.5000000 0.7500000 0.8000000 0.7500000
## 113 0.3333333 0.3333333 0.2000000 1.0000000 0.6666667
## 114 0.1666667 0.2500000 0.2000000 0.1428571 0.2500000
## 115 0.4444444 0.7000000 0.3333333 0.4000000 0.5000000
## 116 0.1666667 0.1111111 0.1428571 0.1428571 0.1666667
## 117 0.6250000 0.7142857 0.8571429 1.0000000 0.6666667
## 118 0.8750000 1.0000000 0.8750000 1.0000000 0.8750000
## 119 0.3333333 0.0000000 0.3333333 0.2000000 0.5000000
## 120 0.0000000 0.0000000 0.1428571 0.0000000 0.1111111
## 121 0.5000000 0.1428571 0.3333333 0.2500000 0.2000000
## 122 0.8571429 0.8571429 0.6666667 0.2500000 0.6666667
## 123 0.5000000 0.6000000 0.5000000 0.6000000 0.6666667
## 124 NaN 0.5000000 0.5000000 0.2000000 0.3333333
## 125 0.0000000 0.1666667 0.0000000 0.2000000 0.0000000
## 126 0.2857143 0.6666667 0.2857143 0.5000000 0.2500000
## 127 0.5000000 0.8333333 0.6000000 0.6666667 1.0000000
## 128 0.7500000 0.8333333 0.7500000 0.5555556 0.9000000
## 129 0.8333333 1.0000000 1.0000000 1.0000000 0.0000000
## 130 0.5833333 0.5000000 0.7142857 0.6666667 0.5714286
## 131 0.5555556 0.6666667 0.8750000 0.8571429 0.6000000
## 132 0.7500000 0.6666667 0.5714286 0.6666667 0.7500000
## 133 0.2500000 0.6000000 0.3333333 0.1428571 0.5000000
## 134 0.2500000 0.2727273 0.2727273 0.3333333 0.4285714
## 135 0.8333333 0.2500000 0.8333333 0.6000000 0.5000000
## 136 0.7500000 0.6000000 0.6000000 1.0000000 0.5714286
## 137 0.8000000 0.7142857 0.4285714 0.7142857 0.6666667
## 138 0.3333333 0.4444444 1.0000000 0.5555556 0.5000000
## 139 0.0000000 0.0000000 0.0000000 0.2857143 0.2857143
## 140 0.3333333 0.4000000 0.0000000 0.5000000 0.3333333
## 141 0.6666667 0.3333333 0.6666667 1.0000000 0.6000000
## 142 0.2500000 0.5000000 0.6000000 0.6000000 0.6000000
## 143 0.2857143 0.3333333 0.2500000 0.5000000 0.4000000
## 144 0.5000000 1.0000000 1.0000000 1.0000000 0.5000000
## 145 0.2500000 0.2857143 0.5000000 0.4444444 0.2000000
## 146 0.3333333 0.5000000 0.3333333 0.2500000 0.5000000
## 147 0.6000000 0.5714286 0.0000000 0.2000000 0.6666667
## 148 0.6000000 0.4000000 0.7500000 0.3333333 0.6000000
## 149 0.5714286 1.0000000 0.3333333 0.8000000 0.6000000
## 150 0.2500000 0.1250000 0.1428571 0.3333333 0.1250000
## 151 0.2500000 0.5000000 0.5000000 0.5000000 0.4285714
## 152 0.4000000 0.2500000 0.4000000 0.3000000 0.2000000
## 153 0.7142857 0.8571429 1.0000000 1.0000000 1.0000000
## 154 0.2500000 0.2857143 0.3333333 0.2500000 0.5000000
## 155 0.3333333 0.2500000 0.5000000 0.4000000 0.2857143
## 156 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 157 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 158 0.6666667 0.8333333 0.5714286 1.0000000 0.7500000
## 159 0.3333333 0.4000000 0.3333333 0.5000000 0.8333333
## 160 0.6666667 0.2500000 0.4285714 0.0000000 0.2500000
## 161 0.1428571 0.3333333 0.4444444 0.4285714 0.0000000
## 162 1.0000000 0.5000000 0.5000000 0.3333333 0.5714286
## 163 0.5000000 0.4000000 0.0000000 0.6666667 0.5000000
## 164 0.3333333 0.3333333 0.1666667 0.2000000 0.4000000
## 165 0.6666667 0.2500000 NaN 0.0000000 0.5000000
## 166 0.2000000 0.0000000 0.2857143 0.1111111 0.0000000
## 167 0.7142857 0.5000000 0.4000000 0.5000000 0.5000000
## 168 0.2222222 0.2500000 0.5000000 0.0000000 0.0000000
## 169 0.0000000 0.0000000 0.1428571 0.0000000 0.0000000
## 170 1.0000000 0.7500000 0.8000000 1.0000000 0.0000000
## 171 0.6666667 0.3333333 0.4285714 0.0000000 0.5000000
## 172 0.2500000 0.3333333 0.4000000 0.5000000 0.3750000
## 173 0.6666667 0.6666667 0.7142857 0.8000000 0.6000000
## 174 0.3333333 0.0000000 0.0000000 0.0000000 0.0000000
## 175 0.6666667 0.6666667 0.5000000 0.4000000 0.4000000
## 176 0.6000000 0.5714286 0.4000000 0.4000000 0.5000000
## 177 0.7500000 0.2000000 0.6000000 0.4000000 0.2500000
## 178 0.5000000 0.4000000 0.7500000 0.5714286 0.5000000
## 179 0.6666667 0.5000000 0.0000000 1.0000000 0.5000000
## 180 0.2000000 0.1428571 0.2000000 0.4000000 0.1428571
## 181 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 182 0.5000000 0.2500000 0.5000000 0.0000000 0.5000000
## 183 0.2500000 0.2500000 1.0000000 0.5000000 1.0000000
## 184 1.0000000 0.4000000 0.6666667 0.7500000 0.7500000
## 185 0.3750000 0.4285714 0.2500000 0.5000000 0.4000000
## 186 0.0000000 0.2000000 0.0000000 0.2000000 0.3333333
## 187 0.3333333 0.4000000 0.3333333 0.5000000 0.3333333
## 188 0.3333333 0.2500000 0.3333333 0.4000000 0.0000000
## 189 0.5000000 1.0000000 0.0000000 0.2500000 0.5000000
## 190 0.5000000 0.2000000 0.7142857 0.3333333 0.3333333
## 191 0.5000000 0.0000000 1.0000000 0.3333333 0.1666667
## 192 0.5000000 0.3333333 0.0000000 0.0000000 0.0000000
## 193 0.2000000 NaN 0.1666667 0.0000000 0.1666667
## 194 0.5000000 0.4000000 0.7500000 0.2500000 0.7500000
## 195 0.4285714 1.0000000 0.8000000 0.4285714 0.8000000
## 196 0.1666667 0.2500000 0.0000000 0.0000000 0.0000000
## 197 1.0000000 0.8333333 0.7500000 0.7142857 0.5000000
## 198 0.0000000 0.0000000 0.1666667 0.5000000 0.3333333
## 199 0.2000000 0.0000000 0.0000000 0.0000000 0.3333333
## 200 0.3333333 0.0000000 0.1250000 0.2857143 0.5000000
## 201 0.4000000 0.5000000 0.3333333 0.2500000 0.0000000
## 202 0.6000000 1.0000000 0.5714286 0.6666667 0.8333333
## 203 0.2000000 0.0000000 0.5000000 0.2000000 0.0000000
## 204 0.3333333 0.6000000 1.0000000 0.5000000 0.5000000
## 205 0.4000000 1.0000000 0.5000000 0.0000000 0.2500000
## 206 0.0000000 0.3333333 0.2500000 0.0000000 0.0000000
## 207 1.0000000 1.0000000 0.6666667 1.0000000 1.0000000
## 208 1.0000000 0.3333333 0.3333333 NaN 0.3333333
## 209 0.2000000 0.5000000 0.6666667 0.7500000 0.5000000
## 210 0.2000000 0.2000000 0.3333333 0.3333333 0.0000000
## 211 0.0000000 0.3333333 0.5000000 0.5000000 0.0000000
## 212 0.7500000 0.6000000 0.6666667 0.3333333 0.7500000
## 213 0.5000000 1.0000000 0.5000000 0.3333333 0.0000000
## 214 0.5000000 0.5000000 0.4000000 0.3333333 0.4285714
## 215 0.4000000 0.3333333 0.3333333 NaN 0.3333333
## 216 0.6666667 0.3333333 0.5000000 0.0000000 0.2000000
## 217 0.0000000 0.2857143 0.1666667 0.3333333 0.0000000
## 218 0.7500000 0.6666667 0.5000000 0.7500000 0.3333333
## 219 0.2500000 0.4000000 0.3333333 0.7500000 0.5000000
## 220 0.6666667 0.5000000 0.8000000 0.8000000 0.3333333
## 221 0.6666667 0.6000000 0.5000000 0.5000000 0.2500000
## 222 0.0000000 0.1428571 0.4000000 0.2500000 1.0000000
## 223 0.0000000 0.2500000 0.0000000 0.0000000 NaN
## 224 0.5000000 0.2000000 0.5000000 0.4000000 0.5000000
## 225 0.5000000 1.0000000 0.2500000 1.0000000 0.5000000
## 226 1.0000000 1.0000000 0.8333333 0.7500000 0.6666667
## 227 0.4285714 0.3333333 0.5714286 1.0000000 0.2500000
## 228 0.0000000 0.0000000 0.0000000 0.2000000 0.2500000
## 229 0.0000000 0.3333333 0.0000000 0.2000000 0.3333333
## 230 0.6666667 0.5000000 0.2857143 1.0000000 0.6000000
## 231 0.6666667 0.7500000 1.0000000 0.2500000 0.0000000
## 232 0.3333333 1.0000000 0.6666667 0.2857143 0.5000000
## 233 0.6666667 0.3333333 0.5000000 0.6666667 0.3333333
## 234 0.0000000 0.0000000 0.2000000 0.5000000 0.5000000
## 235 0.0000000 0.1666667 0.0000000 0.0000000 0.0000000
## 236 0.2857143 0.2000000 0.2500000 0.1666667 0.2857143
## 237 0.0000000 0.3333333 0.2500000 0.0000000 0.0000000
## 238 0.0000000 0.1666667 0.1666667 0.2500000 0.2500000
## 239 0.2000000 0.3333333 0.3333333 0.5000000 0.1666667
## 240 0.2500000 0.2500000 0.3333333 0.0000000 0.5000000
## 241 0.0000000 0.0000000 0.1250000 0.0000000 0.0000000
## 242 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 243 1.0000000 1.0000000 0.0000000 0.0000000 0.3333333
## 244 0.5000000 0.2500000 0.0000000 0.4000000 0.5000000
## 245 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 246 0.0000000 0.5000000 0.0000000 0.3333333 0.5000000
## 247 0.3333333 0.0000000 0.5000000 0.3333333 0.0000000
## 248 0.3333333 0.4000000 0.4000000 0.4000000 0.3333333
## 249 0.7500000 0.5000000 1.0000000 0.5000000 0.3333333
## 250 0.6000000 0.5000000 1.0000000 0.5000000 0.5000000
## 251 0.0000000 0.3333333 0.2500000 0.0000000 0.3333333
## 252 0.0000000 0.0000000 0.0000000 0.0000000 NaN
## 253 0.0000000 0.0000000 0.0000000 0.0000000 0.3333333
## 254 0.2000000 0.2500000 0.3333333 0.0000000 0.2000000
## 255 0.0000000 0.2857143 0.2857143 0.3333333 0.5000000
## 256 1.0000000 0.4000000 0.3333333 0.5000000 0.2000000
## 257 0.0000000 0.5000000 NaN 0.3333333 0.2500000
## 258 0.2500000 0.3333333 0.3333333 0.2500000 0.2000000
## 259 0.4000000 0.3333333 0.6666667 0.3333333 0.0000000
## 260 0.6666667 NaN 0.6666667 0.0000000 0.3333333
## 261 0.0000000 0.2000000 0.0000000 0.0000000 0.3333333
## 262 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 263 0.0000000 NaN 0.0000000 0.0000000 0.0000000
## 264 0.2500000 0.3333333 0.2500000 0.2500000 0.3333333
## 265 0.3333333 0.5000000 1.0000000 0.4000000 0.7500000
## 266 0.5000000 0.2000000 0.3333333 0.2500000 0.3333333
## 267 0.6666667 0.5000000 0.3333333 0.6666667 1.0000000
## 268 0.5000000 0.6666667 0.2000000 0.5000000 0.5000000
## 269 0.3333333 0.2000000 0.5000000 0.2500000 0.1666667
## 270 0.2500000 0.3333333 0.2000000 0.3333333 0.2000000
## 271 0.2000000 0.2000000 0.2500000 0.3333333 0.2000000
## 272 0.0000000 0.0000000 0.2000000 0.5000000 0.0000000
## 273 0.2500000 0.3333333 0.0000000 0.2857143 0.0000000
## 274 0.3333333 0.2000000 0.2000000 0.2000000 0.3333333
## 275 0.2500000 0.3333333 0.6666667 0.4000000 0.3333333
## 276 0.2500000 0.4444444 0.3333333 0.3750000 0.4545455
## 277 0.5714286 0.5000000 0.4285714 0.3750000 0.8000000
## 278 0.6363636 0.5000000 0.5833333 0.7000000 0.5555556
## 279 0.3333333 0.3000000 0.3333333 0.2000000 0.4444444
## 280 1.0000000 0.5000000 0.7500000 1.0000000 0.3333333
## 281 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 282 0.3333333 0.4000000 0.0000000 1.0000000 NaN
## 283 0.3333333 0.3333333 0.2500000 0.0000000 0.3333333
## 284 0.5000000 0.6000000 0.6666667 0.2000000 0.5555556
## 285 0.2500000 0.2000000 0.0000000 0.3333333 0.7500000
## 286 0.3333333 0.2000000 0.0000000 0.0000000 0.5000000
## 287 1.0000000 0.5000000 0.5000000 0.3333333 0.6666667
## 288 0.0000000 0.3333333 0.3333333 0.4000000 0.2000000
## 289 0.0000000 0.5000000 0.3333333 0.5000000 0.5000000
## 290 0.2500000 0.2500000 0.2000000 0.0000000 0.3333333
## 291 0.0000000 0.0000000 0.2500000 0.0000000 0.5000000
## 292 0.3333333 0.0000000 0.5000000 0.4000000 1.0000000
## 293 0.3333333 1.0000000 0.6666667 0.4000000 1.0000000
## 294 0.3333333 1.0000000 0.0000000 0.5000000 0.5000000
## 295 1.0000000 0.0000000 1.0000000 0.5000000 0.0000000
## 296 0.6666667 0.3333333 0.0000000 0.5000000 0.5000000
## 297 0.0000000 0.5000000 0.5000000 0.5000000 0.3333333
## 298 0.7272727 0.7272727 0.6000000 0.8750000 0.8181818
## 299 0.6666667 0.5000000 0.6666667 0.2500000 0.5000000
## 300 1.0000000 0.5000000 0.0000000 0.0000000 0.5000000
##
## $specificity
## 1 2 3 4 5
## 1 0.8666667 0.8823529 0.8666667 0.8888889 0.9285714
## 2 0.8888889 0.9090909 1.0000000 1.0000000 1.0000000
## 3 0.8000000 0.9230769 1.0000000 1.0000000 0.9000000
## 4 1.0000000 0.9000000 0.9090909 1.0000000 0.8461538
## 5 1.0000000 0.9090909 1.0000000 1.0000000 1.0000000
## 6 0.7692308 0.7500000 0.7272727 0.8571429 0.7777778
## 7 1.0000000 0.7142857 0.8333333 0.9000000 0.8888889
## 8 1.0000000 1.0000000 1.0000000 1.0000000 0.9090909
## 9 0.6428571 0.7272727 0.5714286 0.5714286 0.6666667
## 10 0.8461538 0.8888889 0.8181818 0.9000000 0.9285714
## 11 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 12 0.7000000 0.7500000 0.6666667 0.8571429 0.7500000
## 13 0.8000000 0.8888889 0.8888889 0.8333333 0.8000000
## 14 1.0000000 1.0000000 0.8333333 0.9000000 0.7500000
## 15 0.8333333 0.7500000 0.6666667 1.0000000 0.5454545
## 16 0.7142857 0.7272727 0.7500000 1.0000000 0.7142857
## 17 0.8181818 1.0000000 0.7000000 0.9285714 0.9166667
## 18 0.9000000 0.9000000 0.8888889 0.8888889 0.9000000
## 19 0.5000000 0.5555556 0.6250000 0.5555556 0.7142857
## 20 0.7500000 0.8571429 1.0000000 0.8571429 0.8750000
## 21 0.7777778 0.8571429 0.6666667 0.8750000 0.9090909
## 22 0.7000000 0.7500000 0.7500000 0.8571429 1.0000000
## 23 0.9000000 0.9090909 0.9000000 0.9090909 1.0000000
## 24 0.5454545 0.5833333 0.5000000 0.5384615 0.7777778
## 25 0.9090909 1.0000000 1.0000000 1.0000000 0.8571429
## 26 0.7857143 0.8125000 0.6923077 0.8571429 0.8000000
## 27 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 28 0.8750000 0.8181818 0.7272727 0.7142857 0.7272727
## 29 0.6923077 0.6666667 0.6428571 0.7857143 0.6923077
## 30 0.8888889 1.0000000 0.7777778 1.0000000 0.7500000
## 31 0.7500000 0.7142857 0.7500000 1.0000000 0.6666667
## 32 0.7500000 0.8750000 0.6666667 0.5000000 0.8750000
## 33 0.8333333 0.9090909 0.9166667 0.7500000 0.8333333
## 34 1.0000000 1.0000000 0.8750000 0.7500000 0.8888889
## 35 1.0000000 0.6428571 0.6428571 0.7500000 0.7500000
## 36 0.7142857 1.0000000 0.7272727 0.7000000 0.8750000
## 37 0.5454545 0.5454545 0.5833333 0.6363636 0.5555556
## 38 0.8000000 0.8571429 1.0000000 0.7777778 0.7777778
## 39 0.5000000 0.7777778 0.7500000 0.6363636 0.5454545
## 40 0.7272727 0.7000000 0.7500000 0.8888889 0.5833333
## 41 0.6363636 0.8000000 0.6666667 0.6666667 0.7000000
## 42 0.4615385 0.6153846 0.5714286 0.5454545 0.7777778
## 43 0.8888889 0.7500000 0.6666667 0.7692308 0.8333333
## 44 0.6666667 0.6666667 0.7500000 0.5625000 0.5833333
## 45 0.6923077 0.6153846 0.8181818 0.7272727 0.8571429
## 46 0.8000000 0.8000000 0.8181818 0.8181818 0.8461538
## 47 0.8000000 0.8333333 0.9000000 0.7692308 0.7500000
## 48 0.8888889 0.6666667 0.7692308 0.6153846 0.7000000
## 49 0.6153846 0.7000000 0.8333333 0.9090909 0.7500000
## 50 0.5833333 0.7777778 0.5000000 0.6000000 0.8000000
## 51 0.5000000 0.5714286 0.6000000 0.6000000 0.5714286
## 52 0.9230769 0.7692308 0.7857143 0.8333333 0.8888889
## 53 0.8750000 0.7333333 0.7272727 0.7777778 0.7000000
## 54 0.6363636 0.6363636 0.6153846 0.8333333 0.5384615
## 55 0.9166667 0.8461538 0.9000000 0.9000000 0.8888889
## 56 0.8571429 1.0000000 0.9090909 1.0000000 0.9166667
## 57 0.7777778 0.6923077 0.6666667 0.7272727 0.5454545
## 58 0.6363636 0.7857143 0.8750000 0.8000000 0.8181818
## 59 0.6666667 0.7272727 0.8571429 0.8000000 0.7500000
## 60 0.6153846 0.9090909 0.8000000 0.7647059 0.6875000
## 61 0.7142857 0.6666667 0.6363636 0.7272727 0.8888889
## 62 0.7692308 0.7692308 0.6923077 0.8333333 0.7142857
## 63 0.5714286 0.7142857 0.5294118 0.6000000 0.7142857
## 64 0.9090909 0.8125000 0.8666667 0.8181818 0.8333333
## 65 0.5294118 0.6923077 0.4615385 0.6250000 0.5714286
## 66 0.5000000 0.6153846 0.6000000 0.5714286 0.4705882
## 67 0.5384615 0.6000000 0.5714286 0.4166667 0.5833333
## 68 0.8000000 0.6666667 0.8571429 0.7272727 0.8571429
## 69 0.6428571 0.6428571 0.6250000 0.6428571 0.5384615
## 70 0.5833333 0.5384615 0.5714286 0.4666667 0.5714286
## 71 0.7777778 0.8461538 0.8181818 0.8888889 0.8000000
## 72 0.5454545 0.6363636 0.6363636 0.7000000 0.6363636
## 73 0.8888889 0.6666667 0.5333333 0.7000000 0.7272727
## 74 0.6363636 0.7000000 0.8333333 0.6666667 0.5000000
## 75 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 76 0.4545455 0.5555556 0.4615385 0.4285714 0.5000000
## 77 0.6666667 0.4285714 0.5454545 0.6000000 0.5833333
## 78 0.7000000 0.7272727 0.5833333 0.6363636 0.7272727
## 79 0.9230769 0.8666667 0.9090909 1.0000000 0.8666667
## 80 0.6875000 0.6923077 0.6666667 0.8333333 0.6470588
## 81 0.7000000 0.5333333 0.5833333 0.5000000 0.4375000
## 82 0.8181818 0.5714286 0.7058824 0.7272727 0.7692308
## 83 0.5333333 0.5000000 0.5384615 0.5333333 0.4166667
## 84 0.9333333 0.8571429 0.8823529 0.9444444 0.8823529
## 85 0.9285714 0.8666667 0.9230769 0.9166667 0.8571429
## 86 0.5555556 0.5000000 0.6000000 0.5625000 0.6250000
## 87 0.5833333 0.5000000 0.4285714 0.4285714 0.5000000
## 88 0.6923077 0.6428571 0.5000000 0.5333333 0.6923077
## 89 0.7272727 0.5833333 0.6428571 0.6363636 0.6428571
## 90 0.7272727 0.6666667 0.4666667 0.6111111 0.5294118
## 91 0.6153846 0.5000000 0.5833333 0.5833333 0.6153846
## 92 0.4166667 0.7272727 0.5333333 0.7000000 0.6250000
## 93 0.5714286 0.6666667 0.7142857 0.4166667 0.6363636
## 94 1.0000000 0.8888889 0.7692308 0.8333333 0.9000000
## 95 0.4000000 0.4000000 0.5454545 0.5454545 0.5454545
## 96 0.6428571 0.6000000 0.6428571 0.6250000 0.5294118
## 97 0.7272727 0.7777778 0.8461538 0.5833333 0.8000000
## 98 0.6428571 0.6428571 0.6923077 0.6428571 0.7142857
## 99 0.5000000 0.3571429 0.6000000 0.5000000 0.5333333
## 100 0.6666667 0.8333333 0.6923077 0.5882353 0.5714286
## 101 0.6875000 0.6363636 0.6428571 0.7500000 0.5333333
## 102 0.6875000 0.7692308 0.7333333 0.6250000 0.6666667
## 103 0.7500000 0.6875000 0.7857143 0.6666667 0.6250000
## 104 0.4615385 0.4000000 0.6666667 0.5000000 0.4615385
## 105 0.6666667 0.7692308 0.7058824 0.7500000 0.7222222
## 106 0.6666667 0.6666667 0.6875000 0.7222222 0.7500000
## 107 0.7500000 0.7647059 0.7500000 0.7692308 0.7142857
## 108 0.5333333 0.5000000 0.6428571 0.5625000 0.8000000
## 109 0.4285714 0.4666667 0.4615385 0.5714286 0.5333333
## 110 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 111 0.6470588 0.6000000 0.7142857 0.6000000 0.7692308
## 112 0.4166667 0.5000000 0.6666667 0.6000000 0.5625000
## 113 0.5882353 0.5882353 0.5333333 0.6666667 0.7142857
## 114 0.8750000 0.9166667 0.9000000 0.8461538 0.9166667
## 115 0.6363636 0.9000000 0.5882353 0.6000000 0.6428571
## 116 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 117 0.5000000 0.5384615 0.6153846 0.6428571 0.4705882
## 118 0.5000000 0.5384615 0.5000000 0.5000000 0.5000000
## 119 0.9285714 0.8235294 1.0000000 0.8666667 1.0000000
## 120 0.7692308 0.8000000 0.8461538 0.8235294 0.8181818
## 121 0.9000000 0.6153846 0.7272727 0.6666667 0.6666667
## 122 0.6153846 0.6153846 0.5454545 0.3750000 0.5454545
## 123 0.5714286 0.6000000 0.5714286 0.6000000 0.5882353
## 124 0.7000000 0.7500000 0.7500000 0.6666667 0.7058824
## 125 0.8750000 0.9285714 0.8750000 0.9333333 0.8750000
## 126 0.5384615 0.8181818 0.5384615 0.6250000 0.5625000
## 127 0.4444444 0.5714286 0.4666667 0.4705882 0.4736842
## 128 0.5833333 0.5714286 0.5833333 0.4545455 0.8000000
## 129 0.6428571 0.7692308 0.6250000 0.5263158 0.4736842
## 130 0.6250000 0.5000000 0.6153846 0.6363636 0.5384615
## 131 0.5454545 0.5294118 0.7500000 0.6923077 0.5333333
## 132 0.5625000 0.5714286 0.5384615 0.5294118 0.5625000
## 133 0.7500000 0.8666667 0.7857143 0.6923077 0.8125000
## 134 0.7500000 0.7777778 0.7777778 0.8181818 0.8461538
## 135 0.5714286 0.3750000 0.5714286 0.4666667 0.4375000
## 136 0.6875000 0.6666667 0.6666667 0.6666667 0.6923077
## 137 0.6666667 0.6923077 0.5384615 0.6923077 0.5882353
## 138 0.6363636 0.7272727 0.7222222 0.8181818 0.6666667
## 139 0.7333333 0.7500000 0.7333333 0.8461538 0.8461538
## 140 0.9285714 0.9333333 0.8000000 1.0000000 0.9285714
## 141 0.6470588 0.5882353 0.7142857 0.7058824 0.6666667
## 142 0.5625000 0.6111111 0.6666667 0.6666667 0.6666667
## 143 0.8461538 0.9090909 0.8333333 0.8333333 0.8666667
## 144 0.5000000 0.5263158 0.6250000 0.5555556 0.5000000
## 145 0.7500000 0.7692308 0.8125000 0.9090909 0.7333333
## 146 0.7647059 0.8571429 0.7647059 0.7500000 0.8125000
## 147 0.9333333 1.0000000 0.7777778 0.8000000 0.8823529
## 148 0.6666667 0.6000000 0.6875000 0.5882353 0.6666667
## 149 0.5384615 0.5882353 0.4705882 0.6000000 0.5333333
## 150 0.9375000 0.9166667 0.9230769 0.9411765 0.9166667
## 151 0.6666667 0.7500000 0.7857143 0.7500000 0.7692308
## 152 0.8666667 0.8125000 0.8666667 0.9000000 0.8000000
## 153 0.6923077 0.7692308 0.6470588 0.6111111 0.6470588
## 154 0.8750000 0.9230769 0.9285714 0.8750000 1.0000000
## 155 0.7647059 0.7500000 0.8125000 0.8000000 0.7692308
## 156 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 157 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 158 0.5882353 0.7142857 0.6153846 0.6875000 0.7500000
## 159 0.5294118 0.5333333 0.5000000 0.5625000 0.7142857
## 160 0.9411765 0.8750000 1.0000000 0.8235294 0.8750000
## 161 0.7692308 0.8571429 1.0000000 0.9230769 0.7647059
## 162 0.8333333 0.8125000 0.8571429 0.7857143 0.9230769
## 163 0.7777778 0.8000000 0.7368421 0.8235294 0.8125000
## 164 0.8235294 0.8571429 0.7857143 0.8000000 0.8666667
## 165 0.8823529 0.8125000 0.8000000 0.7777778 0.8333333
## 166 0.9333333 0.8461538 1.0000000 0.9090909 0.8750000
## 167 0.8461538 0.7142857 0.6666667 0.7500000 0.7142857
## 168 1.0000000 0.9375000 1.0000000 0.8823529 0.8750000
## 169 0.9444444 0.9375000 1.0000000 0.9375000 0.9285714
## 170 0.6842105 0.7500000 0.8000000 0.7647059 0.6315789
## 171 0.8823529 0.8571429 0.9230769 0.7500000 0.9285714
## 172 0.7500000 0.7857143 0.8000000 0.8571429 0.8333333
## 173 0.5714286 0.5714286 0.6153846 0.6000000 0.5333333
## 174 1.0000000 0.8823529 0.8571429 0.8888889 0.8750000
## 175 0.6470588 0.6470588 0.6250000 0.6000000 0.6000000
## 176 0.8000000 0.8461538 0.7333333 0.7333333 0.7500000
## 177 1.0000000 0.8666667 1.0000000 0.9333333 0.8750000
## 178 0.5714286 0.5333333 0.6250000 0.6153846 0.5625000
## 179 0.7142857 0.6428571 0.5789474 0.7500000 0.6111111
## 180 0.8666667 0.8461538 0.8666667 0.9333333 0.8461538
## 181 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 182 0.7222222 0.6875000 0.7857143 0.6666667 0.7222222
## 183 0.7500000 0.7500000 0.8823529 0.7777778 0.8823529
## 184 0.7894737 0.8000000 0.8235294 0.8750000 0.8750000
## 185 0.8333333 0.8461538 0.7500000 0.7777778 0.8000000
## 186 0.9375000 1.0000000 0.9444444 1.0000000 1.0000000
## 187 0.7857143 0.8000000 0.8181818 0.8125000 0.7647059
## 188 0.8823529 0.8750000 0.8823529 0.9333333 0.8000000
## 189 0.7777778 0.7894737 0.7058824 0.7500000 0.8125000
## 190 0.6111111 0.5333333 0.7692308 0.5454545 0.5714286
## 191 0.7777778 0.7222222 0.8823529 0.7647059 0.7142857
## 192 0.8888889 0.8823529 0.7857143 0.7692308 0.8235294
## 193 1.0000000 0.9500000 1.0000000 0.9444444 1.0000000
## 194 0.7500000 0.7333333 0.8125000 0.6875000 0.8125000
## 195 0.6923077 0.8125000 0.8000000 0.6923077 0.8000000
## 196 1.0000000 1.0000000 0.9444444 0.9285714 0.9375000
## 197 0.7500000 0.7857143 0.6875000 0.7692308 0.6428571
## 198 0.8823529 0.8750000 0.9285714 0.9444444 0.9411765
## 199 0.9333333 0.8823529 0.8750000 0.8666667 0.9411765
## 200 0.9411765 0.8750000 0.9166667 1.0000000 1.0000000
## 201 1.0000000 0.9444444 1.0000000 0.9375000 0.8888889
## 202 0.6666667 0.7058824 0.6923077 0.6470588 0.7857143
## 203 0.8000000 0.6923077 0.8750000 0.8000000 0.7142857
## 204 0.8235294 0.9333333 0.9411765 0.9285714 0.8333333
## 205 0.9333333 0.8947368 0.8888889 0.8333333 0.9166667
## 206 0.8947368 0.9411765 0.9375000 0.8666667 0.8823529
## 207 0.7368421 0.7777778 0.8571429 0.8235294 0.7368421
## 208 0.8333333 0.7857143 0.7647059 0.7500000 0.7647059
## 209 0.6666667 0.7500000 0.7647059 0.8125000 0.7500000
## 210 1.0000000 1.0000000 1.0000000 1.0000000 0.9333333
## 211 0.8888889 0.9411765 1.0000000 1.0000000 0.8888889
## 212 0.9375000 0.9333333 0.8823529 0.8235294 0.9375000
## 213 0.7777778 0.7894737 0.7777778 0.7647059 0.7222222
## 214 1.0000000 0.9375000 0.9333333 0.9285714 1.0000000
## 215 0.8000000 0.7857143 0.7647059 0.7500000 0.7647059
## 216 0.8823529 0.8235294 0.8750000 0.7500000 0.8000000
## 217 0.7647059 0.8461538 0.7857143 0.8235294 0.7142857
## 218 0.8750000 0.8235294 0.8125000 0.8750000 0.7647059
## 219 0.7500000 0.8000000 0.7857143 0.8750000 0.7777778
## 220 0.9285714 0.7777778 0.9333333 0.9333333 0.7857143
## 221 0.9411765 1.0000000 0.9375000 0.9375000 0.8750000
## 222 0.8421053 0.8461538 0.9333333 0.9166667 0.9444444
## 223 0.9444444 1.0000000 0.9411765 0.9411765 0.9500000
## 224 0.8333333 0.8000000 0.8333333 0.8666667 0.8750000
## 225 0.8333333 1.0000000 0.8125000 0.8421053 0.8750000
## 226 0.8235294 0.8750000 0.9285714 0.8125000 0.7647059
## 227 0.7692308 0.7058824 0.8461538 0.8235294 0.6875000
## 228 0.9411765 0.9473684 0.9375000 1.0000000 1.0000000
## 229 0.9333333 1.0000000 0.9375000 1.0000000 1.0000000
## 230 0.8823529 0.8333333 0.8461538 0.8888889 0.9333333
## 231 0.8235294 0.8750000 0.7894737 0.7500000 0.7368421
## 232 0.7647059 0.7894737 0.9285714 0.7692308 0.7777778
## 233 0.8823529 0.8235294 0.8750000 0.8823529 0.8235294
## 234 0.8421053 0.8333333 0.8666667 0.8888889 1.0000000
## 235 0.9411765 1.0000000 0.9375000 0.9375000 0.9333333
## 236 0.9230769 0.8666667 0.8750000 0.8571429 0.9230769
## 237 0.8750000 1.0000000 0.9375000 0.8750000 0.8823529
## 238 0.9411765 1.0000000 1.0000000 1.0000000 1.0000000
## 239 0.8666667 0.8823529 0.8823529 0.8888889 0.8571429
## 240 0.9375000 0.9375000 1.0000000 0.8823529 0.9444444
## 241 0.9411765 0.9473684 1.0000000 0.9411765 0.9444444
## 242 0.9333333 0.9333333 0.9444444 0.9411765 0.9375000
## 243 0.9444444 0.8947368 0.8235294 0.8333333 0.8823529
## 244 0.9375000 0.8750000 0.8421053 0.9333333 0.8888889
## 245 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 246 0.9473684 1.0000000 0.9473684 1.0000000 1.0000000
## 247 1.0000000 0.9473684 1.0000000 1.0000000 0.9375000
## 248 0.8823529 0.9333333 0.9333333 0.9333333 0.8823529
## 249 0.9375000 0.8333333 0.8888889 0.8333333 0.8235294
## 250 1.0000000 1.0000000 1.0000000 0.9375000 0.8888889
## 251 0.8333333 0.8823529 0.8750000 0.8235294 0.8823529
## 252 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 253 0.9473684 0.9375000 0.9411765 0.9411765 1.0000000
## 254 1.0000000 1.0000000 1.0000000 0.9333333 1.0000000
## 255 0.8750000 1.0000000 1.0000000 0.9411765 1.0000000
## 256 0.9473684 1.0000000 0.9411765 1.0000000 0.9333333
## 257 0.7647059 0.9285714 0.8000000 0.8235294 0.8125000
## 258 0.9375000 0.9411765 0.9411765 0.9375000 0.9333333
## 259 1.0000000 1.0000000 1.0000000 0.9411765 0.8823529
## 260 1.0000000 0.9000000 1.0000000 0.8888889 0.9411765
## 261 0.8823529 0.9333333 0.8823529 0.8666667 0.9411765
## 262 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 263 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 264 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 265 1.0000000 0.8888889 0.9444444 0.9333333 1.0000000
## 266 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 267 1.0000000 1.0000000 0.9411765 1.0000000 0.9473684
## 268 1.0000000 1.0000000 0.9333333 1.0000000 1.0000000
## 269 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 270 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 271 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 272 0.9473684 0.9375000 1.0000000 1.0000000 0.9444444
## 273 0.9375000 0.9411765 0.8823529 1.0000000 0.8888889
## 274 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## 275 0.9375000 0.9411765 1.0000000 1.0000000 0.9411765
## 276 0.6666667 0.8181818 0.7500000 0.7500000 0.8888889
## 277 0.6923077 0.6428571 0.6153846 0.5833333 0.7333333
## 278 1.0000000 1.0000000 1.0000000 1.0000000 0.8181818
## 279 0.6428571 0.6000000 0.6250000 0.6000000 0.7272727
## 280 0.4736842 0.4444444 0.5000000 0.5294118 0.4117647
## 281 0.9473684 0.9473684 0.9444444 0.9444444 0.9444444
## 282 0.6470588 0.6666667 0.6111111 0.6842105 0.6500000
## 283 0.9411765 1.0000000 0.9375000 0.8888889 0.9411765
## 284 0.8333333 0.8000000 0.7647059 0.6666667 0.9090909
## 285 0.8750000 0.8666667 0.8000000 0.8823529 1.0000000
## 286 0.9285714 0.8666667 0.8421053 0.8235294 0.9375000
## 287 0.8333333 0.7777778 0.8571429 0.7647059 0.8235294
## 288 0.8235294 0.8823529 0.8823529 0.9333333 0.8666667
## 289 0.7777778 0.8333333 0.8235294 0.8750000 0.8750000
## 290 0.9375000 1.0000000 0.9333333 0.8888889 1.0000000
## 291 0.8333333 0.8235294 0.8750000 0.8333333 0.9375000
## 292 0.9285714 0.8235294 0.8888889 0.9333333 0.8947368
## 293 0.8571429 0.8421053 0.8823529 0.8666667 0.8888889
## 294 0.8235294 0.8421053 0.7894737 0.8750000 0.8333333
## 295 0.8421053 0.7894737 0.8421053 0.8333333 0.7894737
## 296 0.9411765 0.8823529 0.8421053 0.9375000 0.9375000
## 297 0.8235294 0.8888889 0.9375000 0.9375000 0.8823529
## 298 0.7777778 0.7777778 0.6000000 0.7500000 0.8888889
## 299 1.0000000 0.9444444 1.0000000 0.9375000 0.9444444
## 300 0.9473684 0.9444444 0.8888889 0.8823529 0.9444444
##
## $kappa
## 1 2 3 4 5
## 1 0.07692308 0.21568627 0.07692308 0.31818182 0.30555556
## 2 0.15094340 0.25531915 0.46808511 0.28571429 0.46808511
## 3 0.10000000 0.52941176 0.50000000 0.36363636 0.30000000
## 4 0.30434783 0.30000000 0.36842105 0.57894737 0.29411765
## 5 0.16666667 0.02173913 0.41176471 0.13793103 0.11290323
## 6 0.70000000 0.40000000 0.50000000 0.50000000 0.50000000
## 7 0.42857143 -0.12500000 0.22222222 0.30000000 0.23809524
## 8 0.42857143 0.42857143 0.30434783 0.50000000 0.36842105
## 9 0.40000000 0.50000000 0.20000000 0.20000000 0.40000000
## 10 0.29411765 0.23809524 0.15789474 0.30000000 0.62500000
## 11 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 12 0.60000000 0.58333333 0.48979592 0.68085106 0.58333333
## 13 0.60000000 0.70000000 0.70000000 0.80000000 0.60000000
## 14 0.61165049 0.61165049 0.46808511 0.50000000 0.25531915
## 15 0.47916667 0.48979592 0.27083333 0.68750000 0.20792079
## 16 0.30000000 0.50000000 0.40000000 0.60000000 0.30000000
## 17 0.27083333 0.44444444 0.00000000 0.76190476 0.56521739
## 18 0.50000000 0.50000000 0.41747573 0.41747573 0.50000000
## 19 0.50000000 0.57894737 0.66666667 0.57894737 0.76470588
## 20 0.15094340 0.26605505 0.44954128 0.26605505 0.33962264
## 21 0.31372549 0.33962264 0.11764706 0.42307692 0.69387755
## 22 0.10000000 0.15094340 0.15094340 0.26605505 0.44954128
## 23 0.20000000 0.25531915 0.20000000 0.25531915 0.28571429
## 24 0.10000000 0.20000000 0.00000000 0.10000000 0.50000000
## 25 0.25531915 0.63414634 0.46808511 0.23728814 0.21052632
## 26 0.28571429 0.47368421 -0.02272727 0.52380952 0.37500000
## 27 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 28 0.33962264 0.38144330 0.17525773 0.20454545 0.17525773
## 29 0.25531915 0.22222222 0.13043478 0.56521739 0.25531915
## 30 0.32692308 0.51923077 0.13461538 0.51923077 0.07407407
## 31 0.40000000 0.30000000 0.40000000 0.60000000 0.30000000
## 32 0.48979592 0.69387755 0.50980392 0.11764706 0.69387755
## 33 0.46808511 0.58762887 0.68085106 0.25531915 0.46808511
## 34 0.44954128 0.44954128 0.33962264 0.25531915 0.41747573
## 35 0.80000000 0.40000000 0.40000000 0.60000000 0.60000000
## 36 0.15094340 0.61538462 0.28571429 0.20000000 0.42307692
## 37 0.41747573 0.41747573 0.52830189 0.61165049 0.38144330
## 38 0.30000000 0.26605505 0.52830189 0.22330097 0.22330097
## 39 0.00000000 0.50000000 0.40000000 0.30000000 0.10000000
## 40 0.50000000 0.40000000 0.60000000 0.70000000 0.20000000
## 41 0.40594059 0.70000000 0.50980392 0.50980392 0.50000000
## 42 -0.10000000 0.30000000 0.20000000 0.10000000 0.50000000
## 43 0.50980392 0.37500000 0.16666667 0.46808511 0.58333333
## 44 0.40000000 0.40000000 0.60000000 0.20000000 0.20000000
## 45 0.50000000 0.30000000 0.70000000 0.50000000 0.50000000
## 46 0.40000000 0.40000000 0.48979592 0.48979592 0.68085106
## 47 0.37500000 0.34782609 0.40000000 0.20454545 0.13043478
## 48 0.50980392 0.16666667 0.46808511 0.04255319 0.20000000
## 49 0.04255319 0.20000000 0.58333333 0.69387755 0.37500000
## 50 0.42307692 0.69387755 0.28571429 0.40000000 0.80000000
## 51 0.15094340 0.32692308 0.42857143 0.42857143 0.32692308
## 52 0.52941176 0.05882353 0.12500000 0.22222222 0.23809524
## 53 0.50980392 0.57894737 0.39393939 0.40594059 0.30000000
## 54 0.50980392 0.50980392 0.52830189 0.56521739 0.33962264
## 55 0.44444444 0.29411765 0.30000000 0.30000000 0.23809524
## 56 0.21052632 0.28571429 0.25531915 0.28571429 0.31818182
## 57 0.40594059 0.38144330 0.20792079 0.39393939 -0.01010101
## 58 0.08163265 0.56521739 0.42307692 0.40000000 0.48979592
## 59 0.39393939 0.60396040 0.58762887 0.70000000 0.48979592
## 60 -0.25000000 0.47916667 0.37500000 0.30555556 -0.05263158
## 61 0.60000000 0.30000000 0.30000000 0.50000000 0.70000000
## 62 0.58762887 0.58762887 0.38144330 0.69387755 0.47916667
## 63 0.06250000 0.47916667 -0.07526882 0.15789474 0.47916667
## 64 0.47916667 0.47368421 0.62500000 0.27083333 0.34782609
## 65 0.10000000 0.50000000 -0.10000000 0.40000000 0.20000000
## 66 0.13461538 0.41747573 0.42857143 0.32692308 0.06542056
## 67 0.22330097 0.30000000 0.32692308 -0.07843137 0.31372549
## 68 0.70000000 0.39393939 0.58762887 0.60396040 0.58762887
## 69 0.27083333 0.27083333 0.25531915 0.27083333 -0.03092784
## 70 0.42307692 0.33962264 0.44444444 0.18181818 0.44444444
## 71 0.13461538 0.43181818 0.27083333 0.32692308 0.20000000
## 72 0.10000000 0.30000000 0.30000000 0.40000000 0.30000000
## 73 0.79797980 0.50980392 0.23809524 0.50000000 0.60396040
## 74 0.19191919 0.30000000 0.69387755 0.36842105 -0.14583333
## 75 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 76 0.32692308 0.47916667 0.37500000 0.31034483 0.40000000
## 77 0.48979592 0.07407407 0.31372549 0.40000000 0.42307692
## 78 0.50000000 0.60396040 0.31372549 0.40594059 0.60396040
## 79 0.07894737 -0.16666667 0.02173913 0.23913043 -0.16666667
## 80 0.14634146 0.12087912 0.04255319 0.46808511 -0.01265823
## 81 0.60000000 0.36363636 0.42307692 0.23076923 0.10714286
## 82 0.48979592 -0.08695652 0.41860465 0.28571429 0.46808511
## 83 0.36363636 0.20000000 0.33962264 0.36363636 0.03846154
## 84 0.16666667 -0.17647059 -0.13636364 0.44444444 -0.13636364
## 85 0.30555556 0.07692308 0.24050633 0.18604651 0.02777778
## 86 0.20000000 0.00000000 0.30000000 0.20000000 0.40000000
## 87 0.31372549 0.13461538 -0.05769231 -0.05769231 0.13461538
## 88 0.50000000 0.40000000 0.00000000 0.10000000 0.50000000
## 89 0.28571429 -0.04166667 0.13043478 0.08163265 0.13043478
## 90 0.39393939 0.28571429 -0.26315789 0.23913043 -0.07526882
## 91 0.30000000 0.00000000 0.20000000 0.20000000 0.30000000
## 92 -0.07843137 0.60396040 0.23809524 0.50000000 0.28571429
## 93 0.44444444 0.61538462 0.46808511 0.03846154 0.50980392
## 94 0.44444444 0.32692308 0.20454545 0.34782609 0.40000000
## 95 0.00000000 0.00000000 0.31372549 0.31372549 0.31372549
## 96 0.40000000 0.30000000 0.40000000 0.40000000 0.10000000
## 97 0.17525773 0.22330097 0.56043956 -0.17021277 0.30000000
## 98 0.27083333 0.27083333 0.38144330 0.27083333 0.47916667
## 99 0.15094340 -0.25000000 0.42857143 0.15094340 0.23809524
## 100 0.28571429 0.58333333 0.25531915 -0.04651163 -0.08695652
## 101 0.14634146 -0.03092784 -0.02272727 0.25531915 -0.41176471
## 102 0.31818182 0.46808511 0.44444444 0.09090909 0.22222222
## 103 0.37500000 0.31818182 0.56521739 0.11764706 0.09090909
## 104 0.15094340 0.00000000 0.61538462 0.23076923 0.15094340
## 105 0.07894737 0.34065934 0.24050633 0.25531915 0.34210526
## 106 0.07894737 0.05882353 0.14634146 0.34210526 0.39024390
## 107 0.21052632 0.30555556 0.21052632 0.20454545 0.04761905
## 108 0.10000000 0.00000000 0.40000000 0.20000000 0.60000000
## 109 -0.05769231 0.04761905 0.02912621 0.32692308 0.23809524
## 110 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 111 0.18604651 0.00000000 0.34782609 0.00000000 0.46808511
## 112 -0.20000000 0.00000000 0.40000000 0.30000000 0.20000000
## 113 -0.04651163 -0.04651163 -0.22222222 0.28571429 0.34782609
## 114 0.03508772 0.18604651 0.10000000 -0.01265823 0.18604651
## 115 0.08163265 0.60000000 -0.04651163 0.00000000 0.13043478
## 116 0.21875000 0.12087912 0.17808219 0.17808219 0.21875000
## 117 0.11764706 0.22330097 0.41747573 0.51923077 0.06542056
## 118 0.33962264 0.44954128 0.33962264 0.37500000 0.33962264
## 119 0.30555556 -0.17647059 0.35483871 0.07692308 0.58333333
## 120 -0.26582278 -0.23076923 -0.01265823 -0.17647059 -0.07526882
## 121 0.40000000 -0.25000000 0.06250000 -0.08695652 -0.12500000
## 122 0.41747573 0.41747573 0.20792079 -0.22641509 0.20792079
## 123 0.06250000 0.15789474 0.06250000 0.15789474 0.13978495
## 124 0.00000000 0.21052632 0.21052632 -0.12500000 0.02777778
## 125 -0.15384615 0.11764706 -0.15384615 0.16666667 -0.15384615
## 126 -0.17021277 0.48979592 -0.17021277 0.09090909 -0.13636364
## 127 -0.01851852 0.32692308 0.04761905 0.06542056 0.08256881
## 128 0.31372549 0.32692308 0.31372549 0.00990099 0.70000000
## 129 0.40000000 0.70000000 0.40000000 0.10000000 -0.10000000
## 130 0.20000000 0.00000000 0.30000000 0.30000000 0.10000000
## 131 0.10000000 0.10000000 0.60000000 0.50000000 0.10000000
## 132 0.20000000 0.20000000 0.10000000 0.10000000 0.20000000
## 133 0.00000000 0.46666667 0.12500000 -0.17647059 0.28571429
## 134 0.00000000 0.04761905 0.04761905 0.15789474 0.29411765
## 135 0.32692308 -0.22641509 0.32692308 0.04761905 -0.03773585
## 136 0.31818182 0.22222222 0.22222222 0.28571429 0.25531915
## 137 0.36842105 0.38144330 -0.03092784 0.38144330 0.13978495
## 138 -0.03092784 0.17525773 0.34210526 0.38144330 0.07894737
## 139 -0.28571429 -0.25000000 -0.28571429 0.14634146 0.14634146
## 140 0.30555556 0.38461538 -0.23076923 0.58333333 0.30555556
## 141 0.18604651 -0.04651163 0.34782609 0.41860465 0.22222222
## 142 -0.13636364 0.04761905 0.22222222 0.22222222 0.22222222
## 143 0.14634146 0.25531915 0.09090909 0.23076923 0.28571429
## 144 0.00000000 0.10000000 0.40000000 0.20000000 0.00000000
## 145 0.00000000 0.05882353 0.28571429 0.36842105 -0.06666667
## 146 0.07692308 0.37500000 0.07692308 0.00000000 0.28571429
## 147 0.57142857 0.63414634 -0.15384615 0.00000000 0.48275862
## 148 0.22222222 0.00000000 0.31818182 -0.04651163 0.22222222
## 149 0.10000000 0.30000000 -0.10000000 0.30000000 0.10000000
## 150 0.23076923 0.04761905 0.07894737 0.31818182 0.04761905
## 151 -0.08695652 0.21052632 0.28571429 0.21052632 0.20454545
## 152 0.28571429 0.06250000 0.28571429 0.20000000 0.00000000
## 153 0.38144330 0.58762887 0.35483871 0.23913043 0.35483871
## 154 0.13793103 0.24050633 0.30555556 0.13793103 0.58333333
## 155 0.07692308 0.00000000 0.28571429 0.20000000 0.05882353
## 156 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 157 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 158 0.13978495 0.47916667 0.17525773 0.46808511 0.48979592
## 159 -0.07526882 -0.05263158 -0.14583333 0.04255319 0.47916667
## 160 0.60784314 0.13793103 0.49367089 -0.17647059 0.13793103
## 161 -0.09756098 0.21052632 0.46808511 0.39024390 -0.20689655
## 162 0.50000000 0.28571429 0.37500000 0.12500000 0.52941176
## 163 0.16666667 0.20000000 -0.09090909 0.38461538 0.28571429
## 164 0.13793103 0.21052632 -0.05263158 0.00000000 0.28571429
## 165 0.48275862 0.06250000 0.00000000 -0.15384615 0.23076923
## 166 0.16666667 -0.18421053 0.34210526 0.02173913 -0.15384615
## 167 0.56043956 0.20454545 0.05882353 0.25531915 0.20454545
## 168 0.23913043 0.23076923 0.61538462 -0.13636364 -0.15384615
## 169 -0.07142857 -0.08695652 0.17808219 -0.08695652 -0.09375000
## 170 0.17808219 0.39024390 0.52941176 0.49367089 -0.09589041
## 171 0.48275862 0.21052632 0.39024390 -0.25000000 0.47368421
## 172 0.00000000 0.12500000 0.20000000 0.37500000 0.22222222
## 173 0.20000000 0.20000000 0.30000000 0.30000000 0.10000000
## 174 0.41176471 -0.13636364 -0.17647059 -0.11111111 -0.15384615
## 175 0.18604651 0.18604651 0.09090909 0.00000000 0.00000000
## 176 0.37500000 0.43181818 0.12500000 0.12500000 0.21052632
## 177 0.82758621 0.07692308 0.69230769 0.38461538 0.13793103
## 178 0.06250000 -0.05263158 0.25531915 0.17525773 0.04255319
## 179 0.34782609 0.13043478 -0.09756098 0.54545455 0.04761905
## 180 0.07692308 -0.01265823 0.07692308 0.38461538 -0.01265823
## 181 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 182 0.11764706 -0.05263158 0.28571429 -0.17647059 0.11764706
## 183 0.00000000 0.00000000 0.69230769 0.16666667 0.69230769
## 184 0.27272727 0.20000000 0.38461538 0.57142857 0.57142857
## 185 0.22222222 0.29411765 0.00000000 0.16666667 0.20000000
## 186 -0.08695652 0.27272727 -0.07142857 0.27272727 0.45945946
## 187 0.12500000 0.20000000 0.15789474 0.28571429 0.07692308
## 188 0.21568627 0.13793103 0.21568627 0.38461538 -0.23076923
## 189 0.16666667 0.27272727 -0.23076923 0.00000000 0.28571429
## 190 0.04761905 -0.22222222 0.46808511 -0.12244898 -0.08695652
## 191 0.16666667 -0.16666667 0.69230769 0.07692308 -0.12500000
## 192 0.31818182 0.21568627 -0.25000000 -0.26582278 -0.17647059
## 193 0.27272727 0.00000000 0.21875000 -0.07142857 0.21875000
## 194 0.21052632 0.12500000 0.47368421 -0.05263158 0.47368421
## 195 0.12087912 0.63414634 0.52941176 0.12087912 0.52941176
## 196 0.21875000 0.34782609 -0.07142857 -0.09375000 -0.08695652
## 197 0.54545455 0.56521739 0.31818182 0.46808511 0.13043478
## 198 -0.13636364 -0.15384615 0.11764706 0.44444444 0.31818182
## 199 0.16666667 -0.13636364 -0.15384615 -0.16666667 0.31818182
## 200 0.31818182 -0.15384615 0.04761905 0.34210526 0.61538462
## 201 0.50000000 0.44444444 0.41176471 0.23076923 -0.11111111
## 202 0.22222222 0.41860465 0.25531915 0.18604651 0.56521739
## 203 0.00000000 -0.34146341 0.37500000 0.00000000 -0.31578947
## 204 0.13793103 0.57142857 0.82758621 0.47368421 0.23076923
## 205 0.38461538 0.45945946 0.31818182 -0.13636364 0.18604651
## 206 -0.07142857 0.31818182 0.23076923 -0.16666667 -0.13636364
## 207 0.21875000 0.41176471 0.52380952 0.58333333 0.21875000
## 208 0.50000000 0.12500000 0.07692308 0.00000000 0.07692308
## 209 -0.12500000 0.21052632 0.30555556 0.47368421 0.21052632
## 210 0.27272727 0.27272727 0.45945946 0.45945946 -0.09090909
## 211 -0.11111111 0.31818182 0.61538462 0.61538462 -0.11111111
## 212 0.68750000 0.57142857 0.48275862 0.13793103 0.68750000
## 213 0.16666667 0.27272727 0.16666667 0.07692308 -0.16666667
## 214 0.58333333 0.48275862 0.38461538 0.30555556 0.49367089
## 215 0.20000000 0.12500000 0.07692308 0.00000000 0.07692308
## 216 0.48275862 0.13793103 0.37500000 -0.25000000 0.00000000
## 217 -0.20689655 0.14634146 -0.05263158 0.13793103 -0.31578947
## 218 0.57142857 0.38461538 0.28571429 0.57142857 0.07692308
## 219 0.00000000 0.20000000 0.12500000 0.57142857 0.16666667
## 220 0.62500000 0.16666667 0.73333333 0.73333333 0.12500000
## 221 0.60784314 0.69230769 0.48275862 0.48275862 0.13793103
## 222 -0.08108108 -0.01265823 0.38461538 0.18604651 0.77272727
## 223 -0.07142857 0.34782609 -0.08108108 -0.08108108 0.00000000
## 224 0.23076923 0.00000000 0.23076923 0.28571429 0.37500000
## 225 0.23076923 1.00000000 0.06250000 0.34782609 0.37500000
## 226 0.58333333 0.73684211 0.76190476 0.47368421 0.30555556
## 227 0.20454545 0.02777778 0.43181818 0.58333333 -0.05263158
## 228 -0.08108108 -0.05263158 -0.08695652 0.27272727 0.34782609
## 229 -0.09090909 0.45945946 -0.08695652 0.27272727 0.45945946
## 230 0.48275862 0.23076923 0.14634146 0.61538462 0.57142857
## 231 0.38461538 0.57142857 0.27272727 0.00000000 -0.09090909
## 232 0.07692308 0.27272727 0.62500000 0.05882353 0.16666667
## 233 0.48275862 0.13793103 0.37500000 0.48275862 0.13793103
## 234 -0.08108108 -0.13636364 0.07692308 0.31818182 0.58333333
## 235 -0.08108108 0.21875000 -0.08695652 -0.08695652 -0.09090909
## 236 0.24050633 0.07692308 0.13793103 0.02777778 0.24050633
## 237 -0.15384615 0.41176471 0.23076923 -0.15384615 -0.13636364
## 238 -0.08108108 0.21875000 0.21875000 0.34782609 0.34782609
## 239 0.07692308 0.21568627 0.21568627 0.31818182 0.02777778
## 240 0.23076923 0.23076923 0.41176471 -0.13636364 0.44444444
## 241 -0.08108108 -0.05263158 0.14634146 -0.08108108 -0.07142857
## 242 -0.09090909 -0.09090909 -0.07142857 -0.08108108 -0.08695652
## 243 0.77272727 0.45945946 -0.17647059 -0.13636364 0.21568627
## 244 0.48275862 0.13793103 -0.08108108 0.38461538 0.31818182
## 245 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 246 -0.05263158 0.64285714 -0.05263158 0.45945946 0.64285714
## 247 0.45945946 -0.05263158 0.64285714 0.45945946 -0.08695652
## 248 0.21568627 0.38461538 0.38461538 0.38461538 0.21568627
## 249 0.68750000 0.23076923 0.61538462 0.23076923 0.13793103
## 250 0.69230769 0.58333333 1.00000000 0.48275862 0.31818182
## 251 -0.13636364 0.21568627 0.13793103 -0.17647059 0.21568627
## 252 0.00000000 0.00000000 0.00000000 0.00000000 NaN
## 253 -0.05263158 -0.08695652 -0.08108108 -0.08108108 0.45945946
## 254 0.27272727 0.34782609 0.45945946 -0.09090909 0.27272727
## 255 -0.15384615 0.34210526 0.34210526 0.31818182 0.61538462
## 256 0.64285714 0.50000000 0.31818182 0.61538462 0.16666667
## 257 -0.20689655 0.47368421 0.00000000 0.13793103 0.06250000
## 258 0.23076923 0.31818182 0.31818182 0.23076923 0.16666667
## 259 0.50000000 0.41176471 0.77272727 0.31818182 -0.13636364
## 260 0.77272727 0.00000000 0.77272727 -0.11111111 0.31818182
## 261 -0.13636364 0.16666667 -0.13636364 -0.16666667 0.31818182
## 262 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 263 0.00000000 NaN 0.00000000 0.00000000 0.00000000
## 264 0.34782609 0.45945946 0.34782609 0.34782609 0.45945946
## 265 0.35483871 0.31818182 0.77272727 0.38461538 0.82758621
## 266 0.64285714 0.27272727 0.45945946 0.34782609 0.45945946
## 267 0.77272727 0.61538462 0.31818182 0.77272727 0.64285714
## 268 0.61538462 0.77272727 0.16666667 0.61538462 0.61538462
## 269 0.45945946 0.27272727 0.64285714 0.34782609 0.21875000
## 270 0.34782609 0.45945946 0.27272727 0.45945946 0.27272727
## 271 0.27272727 0.27272727 0.34782609 0.45945946 0.27272727
## 272 -0.05263158 -0.08695652 0.27272727 0.64285714 -0.07142857
## 273 0.23076923 0.31818182 -0.13636364 0.34210526 -0.11111111
## 274 0.45945946 0.27272727 0.27272727 0.27272727 0.45945946
## 275 0.23076923 0.31818182 0.77272727 0.50000000 0.31818182
## 276 -0.08695652 0.27083333 0.07407407 0.13043478 0.32692308
## 277 0.25531915 0.13043478 0.04255319 -0.04166667 0.44444444
## 278 0.61165049 0.37500000 0.52830189 0.70000000 0.38144330
## 279 -0.02272727 -0.10000000 -0.03773585 -0.17647059 0.17525773
## 280 0.08256881 -0.01851852 0.15094340 0.25233645 -0.12149533
## 281 -0.05263158 -0.05263158 -0.07142857 -0.07142857 -0.07142857
## 282 -0.01265823 0.05882353 -0.18421053 0.17808219 0.00000000
## 283 0.31818182 0.41176471 0.23076923 -0.11111111 0.31818182
## 284 0.34782609 0.37500000 0.30555556 -0.12500000 0.47916667
## 285 0.13793103 0.07692308 -0.23076923 0.21568627 0.82758621
## 286 0.30555556 0.07692308 -0.08108108 -0.17647059 0.48275862
## 287 0.50000000 0.16666667 0.37500000 0.07692308 0.38461538
## 288 -0.17647059 0.21568627 0.21568627 0.38461538 0.07692308
## 289 -0.15384615 0.23076923 0.13793103 0.37500000 0.37500000
## 290 0.23076923 0.28571429 0.16666667 -0.11111111 0.41176471
## 291 -0.13636364 -0.17647059 0.13793103 -0.13636364 0.48275862
## 292 0.30555556 -0.17647059 0.31818182 0.38461538 0.45945946
## 293 0.21052632 0.34782609 0.48275862 0.28571429 0.61538462
## 294 0.13793103 0.34782609 -0.08695652 0.37500000 0.23076923
## 295 0.34782609 -0.08695652 0.34782609 0.23076923 -0.08695652
## 296 0.60784314 0.21568627 -0.08108108 0.48275862 0.48275862
## 297 -0.17647059 0.31818182 0.48275862 0.48275862 0.21568627
## 298 0.50000000 0.50000000 0.20000000 0.60000000 0.70000000
## 299 0.77272727 0.44444444 0.77272727 0.23076923 0.44444444
## 300 0.64285714 0.44444444 -0.11111111 -0.13636364 0.44444444
##
## $TSS
## 1 2 3 4 5
## 1 0.06666667 0.21568627 0.06666667 0.38888889 0.26190476
## 2 0.16161616 0.24242424 0.44444444 0.33333333 0.44444444
## 3 0.10000000 0.49450549 0.50000000 0.41666667 0.30000000
## 4 0.38461538 0.30000000 0.35353535 0.55555556 0.27472527
## 5 0.18181818 0.02020202 0.33333333 0.16666667 0.15384615
## 6 0.76923077 0.41666667 0.50505051 0.54945055 0.50505051
## 7 0.45454545 -0.11904762 0.20833333 0.30000000 0.25252525
## 8 0.45454545 0.45454545 0.38461538 0.50000000 0.35353535
## 9 0.47619048 0.50505051 0.23809524 0.23809524 0.41666667
## 10 0.27472527 0.25252525 0.15151515 0.30000000 0.59523810
## 11 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 12 0.60000000 0.58333333 0.48484848 0.70329670 0.58333333
## 13 0.60000000 0.70707071 0.70707071 0.83333333 0.60000000
## 14 0.63636364 0.63636364 0.45833333 0.50000000 0.25000000
## 15 0.54761905 0.50000000 0.30952381 0.78571429 0.21212121
## 16 0.32967033 0.50505051 0.41666667 0.71428571 0.32967033
## 17 0.26262626 0.50000000 0.00000000 0.76190476 0.54166667
## 18 0.50000000 0.50000000 0.43434343 0.43434343 0.50000000
## 19 0.50000000 0.55555556 0.62500000 0.55555556 0.71428571
## 20 0.16666667 0.31868132 0.53846154 0.31868132 0.37500000
## 21 0.32323232 0.39560440 0.12121212 0.45833333 0.68686869
## 22 0.10000000 0.16666667 0.16666667 0.31868132 0.53846154
## 23 0.20000000 0.24242424 0.20000000 0.24242424 0.33333333
## 24 0.10101010 0.20833333 0.00000000 0.10989011 0.50505051
## 25 0.24242424 0.57142857 0.44444444 0.30769231 0.19047619
## 26 0.28571429 0.56250000 -0.02197802 0.52380952 0.40000000
## 27 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 28 0.37500000 0.37373737 0.17171717 0.21428571 0.17171717
## 29 0.26373626 0.26666667 0.14285714 0.61904762 0.26373626
## 30 0.34343434 0.54545455 0.14141414 0.54545455 0.08333333
## 31 0.41666667 0.32967033 0.41666667 0.71428571 0.30303030
## 32 0.50000000 0.70833333 0.54166667 0.12500000 0.70833333
## 33 0.45833333 0.57575758 0.66666667 0.25000000 0.45833333
## 34 0.53846154 0.53846154 0.37500000 0.25000000 0.43434343
## 35 0.83333333 0.47619048 0.47619048 0.62500000 0.62500000
## 36 0.17582418 0.66666667 0.28282828 0.20000000 0.45833333
## 37 0.43434343 0.43434343 0.58333333 0.63636364 0.37373737
## 38 0.30000000 0.31868132 0.58333333 0.23232323 0.23232323
## 39 0.00000000 0.50505051 0.41666667 0.30303030 0.10101010
## 40 0.50505051 0.40000000 0.62500000 0.70707071 0.20833333
## 41 0.41414141 0.70000000 0.54166667 0.54166667 0.50000000
## 42 -0.10989011 0.32967033 0.23809524 0.10101010 0.50505051
## 43 0.52525253 0.37500000 0.16666667 0.48351648 0.58333333
## 44 0.41666667 0.41666667 0.62500000 0.31250000 0.20833333
## 45 0.54945055 0.32967033 0.70707071 0.50505051 0.54945055
## 46 0.40000000 0.40000000 0.48484848 0.48484848 0.70329670
## 47 0.40000000 0.33333333 0.40000000 0.19780220 0.12500000
## 48 0.52525253 0.16666667 0.48351648 0.04395604 0.20000000
## 49 0.04395604 0.20000000 0.58333333 0.68686869 0.37500000
## 50 0.45833333 0.68686869 0.50000000 0.40000000 0.80000000
## 51 0.25000000 0.40476190 0.60000000 0.60000000 0.40476190
## 52 0.49450549 0.05494505 0.11904762 0.20833333 0.25252525
## 53 0.54166667 0.73333333 0.39393939 0.41414141 0.30000000
## 54 0.52525253 0.52525253 0.61538462 0.61904762 0.39560440
## 55 0.41666667 0.27472527 0.30000000 0.30000000 0.25252525
## 56 0.19047619 0.33333333 0.24242424 0.33333333 0.29166667
## 57 0.41414141 0.40659341 0.21212121 0.39393939 -0.01010101
## 58 0.08080808 0.61904762 0.45833333 0.40000000 0.48484848
## 59 0.39393939 0.61616162 0.62637363 0.70000000 0.50000000
## 60 -0.24175824 0.46464646 0.40000000 0.43137255 -0.06250000
## 61 0.71428571 0.30303030 0.30303030 0.50505051 0.70707071
## 62 0.62637363 0.62637363 0.40659341 0.70833333 0.54761905
## 63 0.07142857 0.54761905 -0.13725490 0.20000000 0.54761905
## 64 0.46464646 0.56250000 0.66666667 0.26262626 0.33333333
## 65 0.19607843 0.54945055 -0.10989011 0.62500000 0.23809524
## 66 0.16666667 0.47252747 0.60000000 0.40476190 0.13725490
## 67 0.25274725 0.30000000 0.40476190 -0.08333333 0.33333333
## 68 0.70000000 0.39393939 0.62637363 0.61616162 0.62637363
## 69 0.30952381 0.30952381 0.37500000 0.30952381 -0.03296703
## 70 0.45833333 0.39560440 0.57142857 0.26666667 0.57142857
## 71 0.14141414 0.41758242 0.26262626 0.34343434 0.20000000
## 72 0.10101010 0.30303030 0.30303030 0.40000000 0.30303030
## 73 0.79797980 0.54166667 0.33333333 0.50000000 0.61616162
## 74 0.19191919 0.30000000 0.70833333 0.46666667 -0.16666667
## 75 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 76 0.34343434 0.46464646 0.46153846 0.42857143 0.40000000
## 77 0.48484848 0.09523810 0.32323232 0.40000000 0.45833333
## 78 0.50000000 0.61616162 0.33333333 0.41414141 0.61616162
## 79 0.06593407 -0.13333333 0.02020202 0.22222222 -0.13333333
## 80 0.18750000 0.12087912 0.04166667 0.45833333 -0.01960784
## 81 0.60000000 0.53333333 0.45833333 0.25000000 0.18750000
## 82 0.48484848 -0.09523810 0.70588235 0.28282828 0.48351648
## 83 0.53333333 0.20000000 0.39560440 0.53333333 0.04166667
## 84 0.13333333 -0.14285714 -0.11764706 0.44444444 -0.11764706
## 85 0.26190476 0.06666667 0.20879121 0.16666667 0.02380952
## 86 0.55555556 0.00000000 0.40000000 0.31250000 0.62500000
## 87 0.33333333 0.16666667 -0.07142857 -0.07142857 0.16666667
## 88 0.54945055 0.47619048 0.00000000 0.13333333 0.54945055
## 89 0.28282828 -0.04166667 0.14285714 0.08080808 0.14285714
## 90 0.39393939 0.29166667 -0.33333333 0.61111111 -0.13725490
## 91 0.32967033 0.00000000 0.20833333 0.20833333 0.32967033
## 92 -0.08333333 0.61616162 0.33333333 0.50000000 0.29166667
## 93 0.57142857 0.66666667 0.48351648 0.04166667 0.52525253
## 94 0.50000000 0.34343434 0.19780220 0.33333333 0.40000000
## 95 0.00000000 0.00000000 0.32323232 0.32323232 0.32323232
## 96 0.47619048 0.40000000 0.47619048 0.62500000 0.19607843
## 97 0.17171717 0.23232323 0.56043956 -0.16666667 0.30000000
## 98 0.30952381 0.30952381 0.40659341 0.30952381 0.54761905
## 99 0.25000000 -0.30952381 0.60000000 0.25000000 0.33333333
## 100 0.66666667 0.58333333 0.26373626 -0.07843137 -0.09523810
## 101 0.18750000 -0.03030303 -0.02380952 0.25000000 -0.46666667
## 102 0.43750000 0.48351648 0.53333333 0.12500000 0.26666667
## 103 0.37500000 0.43750000 0.61904762 0.12121212 0.12500000
## 104 0.17582418 0.00000000 0.66666667 0.25000000 0.17582418
## 105 0.16666667 0.34065934 0.37254902 0.25000000 0.72222222
## 106 0.16666667 0.06666667 0.18750000 0.72222222 0.50000000
## 107 0.25000000 0.43137255 0.25000000 0.19780220 0.04761905
## 108 0.13333333 0.00000000 0.47619048 0.31250000 0.60000000
## 109 -0.07142857 0.06666667 0.03296703 0.40476190 0.33333333
## 110 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 111 0.31372549 0.00000000 0.38095238 0.00000000 0.48351648
## 112 -0.20833333 0.00000000 0.41666667 0.40000000 0.31250000
## 113 -0.07843137 -0.07843137 -0.26666667 0.66666667 0.38095238
## 114 0.04166667 0.16666667 0.10000000 -0.01098901 0.16666667
## 115 0.08080808 0.60000000 -0.07843137 0.00000000 0.14285714
## 116 0.16666667 0.11111111 0.14285714 0.14285714 0.16666667
## 117 0.12500000 0.25274725 0.47252747 0.64285714 0.13725490
## 118 0.37500000 0.53846154 0.37500000 0.50000000 0.37500000
## 119 0.26190476 -0.17647059 0.33333333 0.06666667 0.50000000
## 120 -0.23076923 -0.20000000 -0.01098901 -0.17647059 -0.07070707
## 121 0.40000000 -0.24175824 0.06060606 -0.08333333 -0.13333333
## 122 0.47252747 0.47252747 0.21212121 -0.37500000 0.21212121
## 123 0.07142857 0.20000000 0.07142857 0.20000000 0.25490196
## 124 NaN 0.25000000 0.25000000 -0.13333333 0.03921569
## 125 -0.12500000 0.09523810 -0.12500000 0.13333333 -0.12500000
## 126 -0.17582418 0.48484848 -0.17582418 0.12500000 -0.18750000
## 127 -0.05555556 0.40476190 0.06666667 0.13725490 0.47368421
## 128 0.33333333 0.40476190 0.33333333 0.01010101 0.70000000
## 129 0.47619048 0.76923077 0.62500000 0.52631579 -0.52631579
## 130 0.20833333 0.00000000 0.32967033 0.30303030 0.10989011
## 131 0.10101010 0.19607843 0.62500000 0.54945055 0.13333333
## 132 0.31250000 0.23809524 0.10989011 0.19607843 0.31250000
## 133 0.00000000 0.46666667 0.11904762 -0.16483516 0.31250000
## 134 0.00000000 0.05050505 0.05050505 0.15151515 0.27472527
## 135 0.40476190 -0.37500000 0.40476190 0.06666667 -0.06250000
## 136 0.43750000 0.26666667 0.26666667 0.66666667 0.26373626
## 137 0.46666667 0.40659341 -0.03296703 0.40659341 0.25490196
## 138 -0.03030303 0.17171717 0.72222222 0.37373737 0.16666667
## 139 -0.26666667 -0.25000000 -0.26666667 0.13186813 0.13186813
## 140 0.26190476 0.33333333 -0.20000000 0.50000000 0.26190476
## 141 0.31372549 -0.07843137 0.38095238 0.70588235 0.26666667
## 142 -0.18750000 0.11111111 0.26666667 0.26666667 0.26666667
## 143 0.13186813 0.24242424 0.08333333 0.33333333 0.26666667
## 144 0.00000000 0.52631579 0.62500000 0.55555556 0.00000000
## 145 0.00000000 0.05494505 0.31250000 0.35353535 -0.06666667
## 146 0.09803922 0.35714286 0.09803922 0.00000000 0.31250000
## 147 0.53333333 0.57142857 -0.22222222 0.00000000 0.54901961
## 148 0.26666667 0.00000000 0.43750000 -0.07843137 0.26666667
## 149 0.10989011 0.58823529 -0.19607843 0.40000000 0.13333333
## 150 0.18750000 0.04166667 0.06593407 0.27450980 0.04166667
## 151 -0.08333333 0.25000000 0.28571429 0.25000000 0.19780220
## 152 0.26666667 0.06250000 0.26666667 0.20000000 0.00000000
## 153 0.40659341 0.62637363 0.64705882 0.61111111 0.64705882
## 154 0.12500000 0.20879121 0.26190476 0.12500000 0.50000000
## 155 0.09803922 0.00000000 0.31250000 0.20000000 0.05494505
## 156 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 157 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 158 0.25490196 0.54761905 0.18681319 0.68750000 0.50000000
## 159 -0.13725490 -0.06666667 -0.16666667 0.06250000 0.54761905
## 160 0.60784314 0.12500000 0.42857143 -0.17647059 0.12500000
## 161 -0.08791209 0.19047619 0.44444444 0.35164835 -0.23529412
## 162 0.83333333 0.31250000 0.35714286 0.11904762 0.49450549
## 163 0.27777778 0.20000000 -0.26315789 0.49019608 0.31250000
## 164 0.15686275 0.19047619 -0.04761905 0.00000000 0.26666667
## 165 0.54901961 0.06250000 NaN -0.22222222 0.33333333
## 166 0.13333333 -0.15384615 0.28571429 0.02020202 -0.12500000
## 167 0.56043956 0.21428571 0.06666667 0.25000000 0.21428571
## 168 0.22222222 0.18750000 0.50000000 -0.11764706 -0.12500000
## 169 -0.05555556 -0.06250000 0.14285714 -0.06250000 -0.07142857
## 170 0.68421053 0.50000000 0.60000000 0.76470588 -0.36842105
## 171 0.54901961 0.19047619 0.35164835 -0.25000000 0.42857143
## 172 0.00000000 0.11904762 0.20000000 0.35714286 0.20833333
## 173 0.23809524 0.23809524 0.32967033 0.40000000 0.13333333
## 174 0.33333333 -0.11764706 -0.14285714 -0.11111111 -0.12500000
## 175 0.31372549 0.31372549 0.12500000 0.00000000 0.00000000
## 176 0.40000000 0.41758242 0.13333333 0.13333333 0.25000000
## 177 0.75000000 0.06666667 0.60000000 0.33333333 0.12500000
## 178 0.07142857 -0.06666667 0.37500000 0.18681319 0.06250000
## 179 0.38095238 0.14285714 -0.42105263 0.75000000 0.11111111
## 180 0.06666667 -0.01098901 0.06666667 0.33333333 -0.01098901
## 181 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 182 0.22222222 -0.06250000 0.28571429 -0.33333333 0.22222222
## 183 0.00000000 0.00000000 0.88235294 0.27777778 0.88235294
## 184 0.78947368 0.20000000 0.49019608 0.62500000 0.62500000
## 185 0.20833333 0.27472527 0.00000000 0.27777778 0.20000000
## 186 -0.06250000 0.20000000 -0.05555556 0.20000000 0.33333333
## 187 0.11904762 0.20000000 0.15151515 0.31250000 0.09803922
## 188 0.21568627 0.12500000 0.21568627 0.33333333 -0.20000000
## 189 0.27777778 0.78947368 -0.29411765 0.00000000 0.31250000
## 190 0.11111111 -0.26666667 0.48351648 -0.12121212 -0.09523810
## 191 0.27777778 -0.27777778 0.88235294 0.09803922 -0.11904762
## 192 0.38888889 0.21568627 -0.21428571 -0.23076923 -0.17647059
## 193 0.20000000 NaN 0.16666667 -0.05555556 0.16666667
## 194 0.25000000 0.13333333 0.56250000 -0.06250000 0.56250000
## 195 0.12087912 0.81250000 0.60000000 0.12087912 0.60000000
## 196 0.16666667 0.25000000 -0.05555556 -0.07142857 -0.06250000
## 197 0.75000000 0.61904762 0.43750000 0.48351648 0.14285714
## 198 -0.11764706 -0.12500000 0.09523810 0.44444444 0.27450980
## 199 0.13333333 -0.11764706 -0.12500000 -0.13333333 0.27450980
## 200 0.27450980 -0.12500000 0.04166667 0.28571429 0.50000000
## 201 0.40000000 0.44444444 0.33333333 0.18750000 -0.11111111
## 202 0.26666667 0.70588235 0.26373626 0.31372549 0.61904762
## 203 0.00000000 -0.30769231 0.37500000 0.00000000 -0.28571429
## 204 0.15686275 0.53333333 0.94117647 0.42857143 0.33333333
## 205 0.33333333 0.89473684 0.38888889 -0.16666667 0.16666667
## 206 -0.10526316 0.27450980 0.18750000 -0.13333333 -0.11764706
## 207 0.73684211 0.77777778 0.52380952 0.82352941 0.73684211
## 208 0.83333333 0.11904762 0.09803922 NaN 0.09803922
## 209 -0.13333333 0.25000000 0.43137255 0.56250000 0.25000000
## 210 0.20000000 0.20000000 0.33333333 0.33333333 -0.06666667
## 211 -0.11111111 0.27450980 0.50000000 0.50000000 -0.11111111
## 212 0.68750000 0.53333333 0.54901961 0.15686275 0.68750000
## 213 0.27777778 0.78947368 0.27777778 0.09803922 -0.27777778
## 214 0.50000000 0.43750000 0.33333333 0.26190476 0.42857143
## 215 0.20000000 0.11904762 0.09803922 NaN 0.09803922
## 216 0.54901961 0.15686275 0.37500000 -0.25000000 0.00000000
## 217 -0.23529412 0.13186813 -0.04761905 0.15686275 -0.28571429
## 218 0.62500000 0.49019608 0.31250000 0.62500000 0.09803922
## 219 0.00000000 0.20000000 0.11904762 0.62500000 0.27777778
## 220 0.59523810 0.27777778 0.73333333 0.73333333 0.11904762
## 221 0.60784314 0.60000000 0.43750000 0.43750000 0.12500000
## 222 -0.15789474 -0.01098901 0.33333333 0.16666667 0.94444444
## 223 -0.05555556 0.25000000 -0.05882353 -0.05882353 NaN
## 224 0.33333333 0.00000000 0.33333333 0.26666667 0.37500000
## 225 0.33333333 1.00000000 0.06250000 0.84210526 0.37500000
## 226 0.82352941 0.87500000 0.76190476 0.56250000 0.43137255
## 227 0.19780220 0.03921569 0.41758242 0.82352941 -0.06250000
## 228 -0.05882353 -0.05263158 -0.06250000 0.20000000 0.25000000
## 229 -0.06666667 0.33333333 -0.06250000 0.20000000 0.33333333
## 230 0.54901961 0.33333333 0.13186813 0.88888889 0.53333333
## 231 0.49019608 0.62500000 0.78947368 0.00000000 -0.26315789
## 232 0.09803922 0.78947368 0.59523810 0.05494505 0.27777778
## 233 0.54901961 0.15686275 0.37500000 0.54901961 0.15686275
## 234 -0.15789474 -0.16666667 0.06666667 0.38888889 0.50000000
## 235 -0.05882353 0.16666667 -0.06250000 -0.06250000 -0.06666667
## 236 0.20879121 0.06666667 0.12500000 0.02380952 0.20879121
## 237 -0.12500000 0.33333333 0.18750000 -0.12500000 -0.11764706
## 238 -0.05882353 0.16666667 0.16666667 0.25000000 0.25000000
## 239 0.06666667 0.21568627 0.21568627 0.38888889 0.02380952
## 240 0.18750000 0.18750000 0.33333333 -0.11764706 0.44444444
## 241 -0.05882353 -0.05263158 0.12500000 -0.05882353 -0.05555556
## 242 -0.06666667 -0.06666667 -0.05555556 -0.05882353 -0.06250000
## 243 0.94444444 0.89473684 -0.17647059 -0.16666667 0.21568627
## 244 0.43750000 0.12500000 -0.15789474 0.33333333 0.38888889
## 245 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 246 -0.05263158 0.50000000 -0.05263158 0.33333333 0.50000000
## 247 0.33333333 -0.05263158 0.50000000 0.33333333 -0.06250000
## 248 0.21568627 0.33333333 0.33333333 0.33333333 0.21568627
## 249 0.68750000 0.33333333 0.88888889 0.33333333 0.15686275
## 250 0.60000000 0.50000000 1.00000000 0.43750000 0.38888889
## 251 -0.16666667 0.21568627 0.12500000 -0.17647059 0.21568627
## 252 0.00000000 0.00000000 0.00000000 0.00000000 NaN
## 253 -0.05263158 -0.06250000 -0.05882353 -0.05882353 0.33333333
## 254 0.20000000 0.25000000 0.33333333 -0.06666667 0.20000000
## 255 -0.12500000 0.28571429 0.28571429 0.27450980 0.50000000
## 256 0.94736842 0.40000000 0.27450980 0.50000000 0.13333333
## 257 -0.23529412 0.42857143 NaN 0.15686275 0.06250000
## 258 0.18750000 0.27450980 0.27450980 0.18750000 0.13333333
## 259 0.40000000 0.33333333 0.66666667 0.27450980 -0.11764706
## 260 0.66666667 NaN 0.66666667 -0.11111111 0.27450980
## 261 -0.11764706 0.13333333 -0.11764706 -0.13333333 0.27450980
## 262 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 263 0.00000000 NaN 0.00000000 0.00000000 0.00000000
## 264 0.25000000 0.33333333 0.25000000 0.25000000 0.33333333
## 265 0.33333333 0.38888889 0.94444444 0.33333333 0.75000000
## 266 0.50000000 0.20000000 0.33333333 0.25000000 0.33333333
## 267 0.66666667 0.50000000 0.27450980 0.66666667 0.94736842
## 268 0.50000000 0.66666667 0.13333333 0.50000000 0.50000000
## 269 0.33333333 0.20000000 0.50000000 0.25000000 0.16666667
## 270 0.25000000 0.33333333 0.20000000 0.33333333 0.20000000
## 271 0.20000000 0.20000000 0.25000000 0.33333333 0.20000000
## 272 -0.05263158 -0.06250000 0.20000000 0.50000000 -0.05555556
## 273 0.18750000 0.27450980 -0.11764706 0.28571429 -0.11111111
## 274 0.33333333 0.20000000 0.20000000 0.20000000 0.33333333
## 275 0.18750000 0.27450980 0.66666667 0.40000000 0.27450980
## 276 -0.08333333 0.26262626 0.08333333 0.12500000 0.34343434
## 277 0.26373626 0.14285714 0.04395604 -0.04166667 0.53333333
## 278 0.63636364 0.50000000 0.58333333 0.70000000 0.37373737
## 279 -0.02380952 -0.10000000 -0.04166667 -0.20000000 0.17171717
## 280 0.47368421 -0.05555556 0.25000000 0.52941176 -0.25490196
## 281 -0.05263158 -0.05263158 -0.05555556 -0.05555556 -0.05555556
## 282 -0.01960784 0.06666667 -0.38888889 0.68421053 NaN
## 283 0.27450980 0.33333333 0.18750000 -0.11111111 0.27450980
## 284 0.33333333 0.40000000 0.43137255 -0.13333333 0.46464646
## 285 0.12500000 0.06666667 -0.20000000 0.21568627 0.75000000
## 286 0.26190476 0.06666667 -0.15789474 -0.17647059 0.43750000
## 287 0.83333333 0.27777778 0.35714286 0.09803922 0.49019608
## 288 -0.17647059 0.21568627 0.21568627 0.33333333 0.06666667
## 289 -0.22222222 0.33333333 0.15686275 0.37500000 0.37500000
## 290 0.18750000 0.25000000 0.13333333 -0.11111111 0.33333333
## 291 -0.16666667 -0.17647059 0.12500000 -0.16666667 0.43750000
## 292 0.26190476 -0.17647059 0.38888889 0.33333333 0.89473684
## 293 0.19047619 0.84210526 0.54901961 0.26666667 0.88888889
## 294 0.15686275 0.84210526 -0.21052632 0.37500000 0.33333333
## 295 0.84210526 -0.21052632 0.84210526 0.33333333 -0.21052632
## 296 0.60784314 0.21568627 -0.15789474 0.43750000 0.43750000
## 297 -0.17647059 0.38888889 0.43750000 0.43750000 0.21568627
## 298 0.50505051 0.50505051 0.20000000 0.62500000 0.70707071
## 299 0.66666667 0.44444444 0.66666667 0.18750000 0.44444444
## 300 0.94736842 0.44444444 -0.11111111 -0.11764706 0.44444444
##
## $similarity
## 1 2 3 4 5
## 1 0.2500000 0.3333333 0.2500000 0.4000000 0.4444444
## 2 0.4000000 0.4615385 0.6153846 0.5000000 0.6153846
## 3 0.4000000 0.6666667 0.6666667 0.5882353 0.5333333
## 4 0.5555556 0.5333333 0.5714286 0.7142857 0.5000000
## 5 0.3076923 0.1818182 0.5000000 0.2857143 0.2666667
## 6 0.8235294 0.7272727 0.7368421 0.7826087 0.7619048
## 7 0.6250000 0.1818182 0.4615385 0.5333333 0.5000000
## 8 0.6250000 0.6250000 0.5555556 0.6666667 0.5714286
## 9 0.6250000 0.7368421 0.5000000 0.5000000 0.6666667
## 10 0.5000000 0.5000000 0.4285714 0.5333333 0.7272727
## 11 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 12 0.8181818 0.8333333 0.7826087 0.8800000 0.8333333
## 13 0.8000000 0.8571429 0.8571429 0.8888889 0.8000000
## 14 0.7777778 0.7777778 0.6666667 0.7058824 0.5333333
## 15 0.8000000 0.7826087 0.7200000 0.8800000 0.6000000
## 16 0.6956522 0.7368421 0.7272727 0.8333333 0.6956522
## 17 0.5333333 0.6666667 0.3750000 0.8333333 0.7142857
## 18 0.7058824 0.7058824 0.6666667 0.6666667 0.7058824
## 19 0.8000000 0.8461538 0.8888889 0.8461538 0.9285714
## 20 0.5263158 0.6000000 0.7000000 0.6000000 0.6315789
## 21 0.6315789 0.6666667 0.5263158 0.7000000 0.8235294
## 22 0.4705882 0.5263158 0.5263158 0.6000000 0.7000000
## 23 0.4285714 0.4615385 0.4285714 0.4615385 0.5000000
## 24 0.5263158 0.5555556 0.3750000 0.4705882 0.7619048
## 25 0.4615385 0.7272727 0.6153846 0.4705882 0.4000000
## 26 0.5000000 0.6000000 0.3076923 0.6666667 0.5454545
## 27 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 28 0.6315789 0.6250000 0.5000000 0.4615385 0.5000000
## 29 0.5333333 0.4615385 0.4285714 0.7142857 0.5333333
## 30 0.5882353 0.7058824 0.4705882 0.7058824 0.4444444
## 31 0.7272727 0.6956522 0.7272727 0.8333333 0.6666667
## 32 0.7826087 0.8695652 0.7368421 0.5263158 0.8695652
## 33 0.6666667 0.7500000 0.8000000 0.5333333 0.6666667
## 34 0.7000000 0.7000000 0.6315789 0.5333333 0.6666667
## 35 0.9090909 0.6250000 0.6250000 0.7777778 0.7777778
## 36 0.5714286 0.8000000 0.5882353 0.5555556 0.7000000
## 37 0.7272727 0.7272727 0.7619048 0.8181818 0.7500000
## 38 0.5882353 0.6000000 0.7368421 0.5555556 0.5555556
## 39 0.5000000 0.7619048 0.7272727 0.6315789 0.5263158
## 40 0.7368421 0.7000000 0.7777778 0.8571429 0.5555556
## 41 0.7000000 0.8571429 0.7368421 0.7368421 0.7619048
## 42 0.3529412 0.5882353 0.5000000 0.5263158 0.7619048
## 43 0.7368421 0.6250000 0.5000000 0.6666667 0.7500000
## 44 0.6666667 0.6666667 0.7777778 0.4285714 0.5555556
## 45 0.7058824 0.5882353 0.8421053 0.7368421 0.7826087
## 46 0.6666667 0.6666667 0.7058824 0.7058824 0.8000000
## 47 0.5454545 0.5714286 0.6250000 0.4615385 0.4285714
## 48 0.7368421 0.5000000 0.6666667 0.4000000 0.5555556
## 49 0.4000000 0.5555556 0.7500000 0.8235294 0.6250000
## 50 0.7000000 0.8695652 0.5000000 0.7272727 0.9090909
## 51 0.4000000 0.5882353 0.6250000 0.6250000 0.5882353
## 52 0.6666667 0.3333333 0.3636364 0.4615385 0.5000000
## 53 0.7619048 0.7142857 0.6666667 0.7000000 0.6315789
## 54 0.7619048 0.7619048 0.7368421 0.8461538 0.6315789
## 55 0.6153846 0.5000000 0.5333333 0.5333333 0.5000000
## 56 0.4000000 0.5000000 0.4615385 0.5000000 0.5000000
## 57 0.7000000 0.6250000 0.6000000 0.6666667 0.4444444
## 58 0.4705882 0.7142857 0.7000000 0.6666667 0.7058824
## 59 0.7272727 0.8000000 0.8333333 0.8571429 0.7826087
## 60 0.1538462 0.6666667 0.5454545 0.4444444 0.2000000
## 61 0.7500000 0.6666667 0.6315789 0.7368421 0.8571429
## 62 0.7500000 0.7500000 0.6250000 0.8235294 0.6666667
## 63 0.4000000 0.6666667 0.1666667 0.4285714 0.6666667
## 64 0.6666667 0.6000000 0.7272727 0.5333333 0.5714286
## 65 0.3076923 0.7058824 0.3529412 0.5714286 0.5000000
## 66 0.4705882 0.6666667 0.6250000 0.5882353 0.2857143
## 67 0.5555556 0.6666667 0.5882353 0.4210526 0.6315789
## 68 0.8571429 0.7272727 0.8333333 0.8000000 0.8333333
## 69 0.5333333 0.5333333 0.4615385 0.5333333 0.3750000
## 70 0.7000000 0.6315789 0.6666667 0.4705882 0.6666667
## 71 0.4705882 0.6153846 0.5333333 0.5882353 0.5000000
## 72 0.5263158 0.6315789 0.6315789 0.7000000 0.6315789
## 73 0.9090909 0.7368421 0.5000000 0.7619048 0.8000000
## 74 0.5555556 0.6315789 0.8235294 0.5714286 0.2666667
## 75 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 76 0.6956522 0.8000000 0.6666667 0.6000000 0.7500000
## 77 0.7826087 0.4444444 0.6666667 0.7272727 0.7000000
## 78 0.7619048 0.8000000 0.6315789 0.7000000 0.8000000
## 79 0.2222222 0.0000000 0.1818182 0.3636364 0.0000000
## 80 0.3636364 0.4285714 0.4000000 0.6666667 0.2000000
## 81 0.8181818 0.5882353 0.7000000 0.6000000 0.3750000
## 82 0.7058824 0.2857143 0.5454545 0.5882353 0.6666667
## 83 0.5882353 0.6363636 0.6315789 0.5882353 0.5000000
## 84 0.2857143 0.0000000 0.0000000 0.5000000 0.0000000
## 85 0.4444444 0.2500000 0.4000000 0.3636364 0.2222222
## 86 0.3333333 0.1666667 0.5333333 0.4285714 0.5714286
## 87 0.6315789 0.4705882 0.3529412 0.3529412 0.4705882
## 88 0.7058824 0.6250000 0.3750000 0.4000000 0.7058824
## 89 0.5882353 0.3750000 0.4285714 0.4705882 0.4285714
## 90 0.6666667 0.5882353 0.1428571 0.3636364 0.1666667
## 91 0.5882353 0.2857143 0.5555556 0.5555556 0.5882353
## 92 0.4210526 0.8000000 0.5000000 0.7619048 0.6956522
## 93 0.6666667 0.8000000 0.8000000 0.5000000 0.7619048
## 94 0.6666667 0.5882353 0.4615385 0.5714286 0.6250000
## 95 0.5454545 0.5454545 0.6666667 0.6666667 0.6666667
## 96 0.6250000 0.5333333 0.6250000 0.5714286 0.3076923
## 97 0.5000000 0.5555556 0.7142857 0.2666667 0.5882353
## 98 0.5333333 0.5333333 0.6250000 0.5333333 0.6666667
## 99 0.4000000 0.2352941 0.6250000 0.4000000 0.5000000
## 100 0.4000000 0.7500000 0.5333333 0.1818182 0.2857143
## 101 0.3636364 0.3750000 0.3076923 0.5333333 0.0000000
## 102 0.5000000 0.6666667 0.6153846 0.3333333 0.4615385
## 103 0.6250000 0.5000000 0.7142857 0.5263158 0.3333333
## 104 0.5263158 0.3529412 0.8000000 0.6000000 0.5263158
## 105 0.2222222 0.5714286 0.4000000 0.5333333 0.4444444
## 106 0.2222222 0.3333333 0.3636364 0.4444444 0.5454545
## 107 0.4000000 0.4444444 0.4000000 0.4615385 0.3333333
## 108 0.4000000 0.4444444 0.6250000 0.4285714 0.8000000
## 109 0.3529412 0.3750000 0.4444444 0.5882353 0.5000000
## 110 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 111 0.3636364 0.3076923 0.5714286 0.3076923 0.6666667
## 112 0.3333333 0.3750000 0.6666667 0.5333333 0.4285714
## 113 0.1818182 0.1818182 0.1538462 0.4000000 0.5714286
## 114 0.2666667 0.3636364 0.3076923 0.2000000 0.3636364
## 115 0.4705882 0.7777778 0.1818182 0.4444444 0.4285714
## 116 0.2857143 0.2000000 0.2500000 0.2500000 0.2857143
## 117 0.5263158 0.5555556 0.6666667 0.7058824 0.2857143
## 118 0.6666667 0.7000000 0.6666667 0.6315789 0.6666667
## 119 0.4444444 0.0000000 0.5000000 0.2500000 0.6666667
## 120 0.0000000 0.0000000 0.2000000 0.0000000 0.1666667
## 121 0.6250000 0.1538462 0.4000000 0.2857143 0.1818182
## 122 0.6666667 0.6666667 0.6000000 0.1333333 0.6000000
## 123 0.4000000 0.4285714 0.4000000 0.4285714 0.3333333
## 124 0.0000000 0.4000000 0.4000000 0.1818182 0.2222222
## 125 0.0000000 0.2500000 0.0000000 0.2857143 0.0000000
## 126 0.2666667 0.7058824 0.2666667 0.3333333 0.1666667
## 127 0.1538462 0.5882353 0.3750000 0.2857143 0.1666667
## 128 0.6315789 0.5882353 0.6315789 0.5000000 0.8571429
## 129 0.6250000 0.8235294 0.5714286 0.1818182 0.0000000
## 130 0.6363636 0.4444444 0.5882353 0.6315789 0.4705882
## 131 0.5263158 0.3076923 0.7777778 0.7058824 0.4000000
## 132 0.4285714 0.5000000 0.4705882 0.3076923 0.4285714
## 133 0.2222222 0.6000000 0.3636364 0.1666667 0.4444444
## 134 0.3076923 0.3750000 0.3750000 0.4285714 0.5000000
## 135 0.5882353 0.1333333 0.5882353 0.3750000 0.2666667
## 136 0.5000000 0.4615385 0.4615385 0.4000000 0.5333333
## 137 0.5714286 0.6250000 0.3750000 0.6250000 0.3333333
## 138 0.3750000 0.5000000 0.4444444 0.6250000 0.2222222
## 139 0.0000000 0.0000000 0.0000000 0.3636364 0.3636364
## 140 0.4444444 0.5000000 0.0000000 0.6666667 0.4444444
## 141 0.3636364 0.1818182 0.5714286 0.5454545 0.4615385
## 142 0.1666667 0.2000000 0.4615385 0.4615385 0.4615385
## 143 0.3636364 0.4615385 0.3333333 0.3333333 0.4444444
## 144 0.1666667 0.1818182 0.5714286 0.3333333 0.3750000
## 145 0.3076923 0.3333333 0.4444444 0.5714286 0.2000000
## 146 0.2500000 0.5454545 0.2500000 0.2222222 0.4444444
## 147 0.6666667 0.7272727 0.0000000 0.2222222 0.5714286
## 148 0.4615385 0.3076923 0.5000000 0.1818182 0.4615385
## 149 0.4705882 0.4615385 0.1538462 0.5333333 0.4000000
## 150 0.3333333 0.2000000 0.2222222 0.4000000 0.2000000
## 151 0.2857143 0.4000000 0.5000000 0.4000000 0.4615385
## 152 0.4444444 0.2500000 0.4444444 0.4285714 0.2857143
## 153 0.6250000 0.7500000 0.5000000 0.3636364 0.5000000
## 154 0.2857143 0.4000000 0.4444444 0.2857143 0.6666667
## 155 0.2500000 0.2222222 0.4444444 0.4000000 0.3333333
## 156 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 157 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 158 0.3333333 0.6666667 0.5000000 0.6153846 0.7058824
## 159 0.1666667 0.2857143 0.2666667 0.3076923 0.6666667
## 160 0.6666667 0.2857143 0.6000000 0.0000000 0.2857143
## 161 0.1818182 0.4000000 0.6153846 0.5454545 0.0000000
## 162 0.5714286 0.4444444 0.5454545 0.3636364 0.6666667
## 163 0.2857143 0.4000000 0.0000000 0.5000000 0.4444444
## 164 0.2857143 0.4000000 0.2000000 0.2222222 0.4444444
## 165 0.5714286 0.2500000 0.0000000 0.0000000 0.3333333
## 166 0.2857143 0.0000000 0.4444444 0.1818182 0.0000000
## 167 0.7142857 0.4615385 0.3333333 0.5333333 0.4615385
## 168 0.3636364 0.3333333 0.6666667 0.0000000 0.0000000
## 169 0.0000000 0.0000000 0.2500000 0.0000000 0.0000000
## 170 0.2500000 0.5454545 0.6666667 0.6000000 0.0000000
## 171 0.5714286 0.4000000 0.5454545 0.0000000 0.6000000
## 172 0.3076923 0.3636364 0.4000000 0.5454545 0.4615385
## 173 0.5000000 0.5000000 0.5882353 0.5333333 0.4000000
## 174 0.5000000 0.0000000 0.0000000 0.0000000 0.0000000
## 175 0.3636364 0.3636364 0.3333333 0.3076923 0.3076923
## 176 0.5454545 0.6153846 0.3636364 0.3636364 0.4000000
## 177 0.8571429 0.2500000 0.7500000 0.5000000 0.2857143
## 178 0.4000000 0.2857143 0.4615385 0.5000000 0.3076923
## 179 0.5714286 0.4285714 0.0000000 0.6666667 0.2000000
## 180 0.2500000 0.2000000 0.2500000 0.5000000 0.2000000
## 181 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 182 0.2500000 0.2000000 0.5000000 0.0000000 0.2500000
## 183 0.3076923 0.2222222 0.7500000 0.2857143 0.7500000
## 184 0.3333333 0.4000000 0.5000000 0.6666667 0.6666667
## 185 0.4615385 0.5000000 0.2222222 0.2857143 0.4000000
## 186 0.0000000 0.3333333 0.0000000 0.3333333 0.5000000
## 187 0.3636364 0.4000000 0.4285714 0.4444444 0.2500000
## 188 0.3333333 0.2857143 0.3333333 0.5000000 0.0000000
## 189 0.2857143 0.3333333 0.0000000 0.2222222 0.4444444
## 190 0.2000000 0.1538462 0.6666667 0.3529412 0.2857143
## 191 0.2857143 0.0000000 0.7500000 0.2500000 0.1818182
## 192 0.4000000 0.3333333 0.0000000 0.0000000 0.0000000
## 193 0.3333333 0.0000000 0.2857143 0.0000000 0.2857143
## 194 0.4000000 0.3636364 0.6000000 0.2000000 0.6000000
## 195 0.4285714 0.7272727 0.6666667 0.4285714 0.6666667
## 196 0.2857143 0.4000000 0.0000000 0.0000000 0.0000000
## 197 0.6666667 0.7142857 0.5000000 0.6666667 0.4285714
## 198 0.0000000 0.0000000 0.2500000 0.5000000 0.4000000
## 199 0.2857143 0.0000000 0.0000000 0.0000000 0.4000000
## 200 0.4000000 0.0000000 0.2000000 0.4444444 0.6666667
## 201 0.5714286 0.5000000 0.5000000 0.3333333 0.0000000
## 202 0.4615385 0.5454545 0.5333333 0.3636364 0.7142857
## 203 0.2222222 0.0000000 0.5000000 0.2222222 0.0000000
## 204 0.2857143 0.6666667 0.8571429 0.6000000 0.3333333
## 205 0.5000000 0.5000000 0.4000000 0.0000000 0.3636364
## 206 0.0000000 0.4000000 0.3333333 0.0000000 0.0000000
## 207 0.2857143 0.5000000 0.6666667 0.6666667 0.2857143
## 208 0.5714286 0.3636364 0.2500000 0.0000000 0.2500000
## 209 0.1818182 0.4000000 0.4444444 0.6000000 0.4000000
## 210 0.3333333 0.3333333 0.5000000 0.5000000 0.0000000
## 211 0.0000000 0.4000000 0.6666667 0.6666667 0.0000000
## 212 0.7500000 0.6666667 0.5714286 0.2857143 0.7500000
## 213 0.2857143 0.3333333 0.2857143 0.2500000 0.0000000
## 214 0.6666667 0.5714286 0.5000000 0.4444444 0.6000000
## 215 0.4000000 0.3636364 0.2500000 0.0000000 0.2500000
## 216 0.5714286 0.2857143 0.5000000 0.0000000 0.2222222
## 217 0.0000000 0.3636364 0.2000000 0.2857143 0.0000000
## 218 0.6666667 0.5000000 0.4444444 0.6666667 0.2500000
## 219 0.2222222 0.4000000 0.3636364 0.6666667 0.2857143
## 220 0.7272727 0.2857143 0.8000000 0.8000000 0.3636364
## 221 0.6666667 0.7500000 0.5714286 0.5714286 0.2857143
## 222 0.0000000 0.2000000 0.5000000 0.3636364 0.8000000
## 223 0.0000000 0.4000000 0.0000000 0.0000000 0.0000000
## 224 0.3333333 0.2222222 0.3333333 0.4444444 0.5000000
## 225 0.3333333 1.0000000 0.2500000 0.4000000 0.5000000
## 226 0.6666667 0.8000000 0.8333333 0.6000000 0.4444444
## 227 0.4615385 0.2222222 0.6153846 0.6666667 0.2000000
## 228 0.0000000 0.0000000 0.0000000 0.3333333 0.4000000
## 229 0.0000000 0.5000000 0.0000000 0.3333333 0.5000000
## 230 0.5714286 0.3333333 0.3636364 0.6666667 0.6666667
## 231 0.5000000 0.6666667 0.3333333 0.2222222 0.0000000
## 232 0.2500000 0.3333333 0.7272727 0.3333333 0.2857143
## 233 0.5714286 0.2857143 0.5000000 0.5714286 0.2857143
## 234 0.0000000 0.0000000 0.2500000 0.4000000 0.6666667
## 235 0.0000000 0.2857143 0.0000000 0.0000000 0.0000000
## 236 0.4000000 0.2500000 0.2857143 0.2222222 0.4000000
## 237 0.0000000 0.5000000 0.3333333 0.0000000 0.0000000
## 238 0.0000000 0.2857143 0.2857143 0.4000000 0.4000000
## 239 0.2500000 0.3333333 0.3333333 0.4000000 0.2222222
## 240 0.3333333 0.3333333 0.5000000 0.0000000 0.5000000
## 241 0.0000000 0.0000000 0.2222222 0.0000000 0.0000000
## 242 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 243 0.8000000 0.5000000 0.0000000 0.0000000 0.3333333
## 244 0.5714286 0.2857143 0.0000000 0.5000000 0.4000000
## 245 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 246 0.0000000 0.6666667 0.0000000 0.5000000 0.6666667
## 247 0.5000000 0.0000000 0.6666667 0.5000000 0.0000000
## 248 0.3333333 0.5000000 0.5000000 0.5000000 0.3333333
## 249 0.7500000 0.3333333 0.6666667 0.3333333 0.2857143
## 250 0.7500000 0.6666667 1.0000000 0.5714286 0.4000000
## 251 0.0000000 0.3333333 0.2857143 0.0000000 0.3333333
## 252 0.0000000 0.0000000 0.0000000 0.0000000 NaN
## 253 0.0000000 0.0000000 0.0000000 0.0000000 0.5000000
## 254 0.3333333 0.4000000 0.5000000 0.0000000 0.3333333
## 255 0.0000000 0.4444444 0.4444444 0.4000000 0.6666667
## 256 0.6666667 0.5714286 0.4000000 0.6666667 0.2857143
## 257 0.0000000 0.6000000 0.0000000 0.2857143 0.2500000
## 258 0.3333333 0.4000000 0.4000000 0.3333333 0.2857143
## 259 0.5714286 0.5000000 0.8000000 0.4000000 0.0000000
## 260 0.8000000 0.0000000 0.8000000 0.0000000 0.4000000
## 261 0.0000000 0.2857143 0.0000000 0.0000000 0.4000000
## 262 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 263 0.0000000 NaN 0.0000000 0.0000000 0.0000000
## 264 0.4000000 0.5000000 0.4000000 0.4000000 0.5000000
## 265 0.5000000 0.4000000 0.8000000 0.5000000 0.8571429
## 266 0.6666667 0.3333333 0.5000000 0.4000000 0.5000000
## 267 0.8000000 0.6666667 0.4000000 0.8000000 0.6666667
## 268 0.6666667 0.8000000 0.2857143 0.6666667 0.6666667
## 269 0.5000000 0.3333333 0.6666667 0.4000000 0.2857143
## 270 0.4000000 0.5000000 0.3333333 0.5000000 0.3333333
## 271 0.3333333 0.3333333 0.4000000 0.5000000 0.3333333
## 272 0.0000000 0.0000000 0.3333333 0.6666667 0.0000000
## 273 0.3333333 0.4000000 0.0000000 0.4444444 0.0000000
## 274 0.5000000 0.3333333 0.3333333 0.3333333 0.5000000
## 275 0.3333333 0.4000000 0.8000000 0.5714286 0.4000000
## 276 0.2857143 0.5333333 0.4444444 0.4285714 0.5882353
## 277 0.5333333 0.4285714 0.4000000 0.3750000 0.6153846
## 278 0.7777778 0.6666667 0.7368421 0.8235294 0.6250000
## 279 0.3076923 0.3529412 0.4210526 0.1666667 0.5000000
## 280 0.1666667 0.1538462 0.4000000 0.4285714 0.1428571
## 281 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## 282 0.2000000 0.3333333 0.0000000 0.2500000 0.0000000
## 283 0.4000000 0.5000000 0.3333333 0.0000000 0.4000000
## 284 0.5714286 0.5454545 0.4444444 0.1818182 0.6666667
## 285 0.2857143 0.2500000 0.0000000 0.3333333 0.8571429
## 286 0.4444444 0.2500000 0.0000000 0.0000000 0.5714286
## 287 0.5714286 0.2857143 0.5454545 0.2500000 0.5000000
## 288 0.0000000 0.3333333 0.3333333 0.5000000 0.2500000
## 289 0.0000000 0.3333333 0.2857143 0.5000000 0.5000000
## 290 0.3333333 0.4000000 0.2857143 0.0000000 0.5000000
## 291 0.0000000 0.0000000 0.2857143 0.0000000 0.5714286
## 292 0.4444444 0.0000000 0.4000000 0.5000000 0.5000000
## 293 0.4000000 0.4000000 0.5714286 0.4444444 0.6666667
## 294 0.2857143 0.4000000 0.0000000 0.5000000 0.3333333
## 295 0.4000000 0.0000000 0.4000000 0.3333333 0.0000000
## 296 0.6666667 0.3333333 0.0000000 0.5714286 0.5714286
## 297 0.0000000 0.4000000 0.5714286 0.5714286 0.3333333
## 298 0.7619048 0.7619048 0.6000000 0.7777778 0.8571429
## 299 0.8000000 0.5000000 0.8000000 0.3333333 0.5000000
## 300 0.6666667 0.5000000 0.0000000 0.0000000 0.5000000
##
## $Jaccard
## 1 2 3 4 5
## 1 0.14285714 0.20000000 0.14285714 0.25000000 0.28571429
## 2 0.25000000 0.30000000 0.44444444 0.33333333 0.44444444
## 3 0.25000000 0.50000000 0.50000000 0.41666667 0.36363636
## 4 0.38461538 0.36363636 0.40000000 0.55555556 0.33333333
## 5 0.18181818 0.10000000 0.33333333 0.16666667 0.15384615
## 6 0.70000000 0.57142857 0.58333333 0.64285714 0.61538462
## 7 0.45454545 0.10000000 0.30000000 0.36363636 0.33333333
## 8 0.45454545 0.45454545 0.38461538 0.50000000 0.40000000
## 9 0.45454545 0.58333333 0.33333333 0.33333333 0.50000000
## 10 0.33333333 0.33333333 0.27272727 0.36363636 0.57142857
## 11 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 12 0.69230769 0.71428571 0.64285714 0.78571429 0.71428571
## 13 0.66666667 0.75000000 0.75000000 0.80000000 0.66666667
## 14 0.63636364 0.63636364 0.50000000 0.54545455 0.36363636
## 15 0.66666667 0.64285714 0.56250000 0.78571429 0.42857143
## 16 0.53333333 0.58333333 0.57142857 0.71428571 0.53333333
## 17 0.36363636 0.50000000 0.23076923 0.71428571 0.55555556
## 18 0.54545455 0.54545455 0.50000000 0.50000000 0.54545455
## 19 0.66666667 0.73333333 0.80000000 0.73333333 0.86666667
## 20 0.35714286 0.42857143 0.53846154 0.42857143 0.46153846
## 21 0.46153846 0.50000000 0.35714286 0.53846154 0.70000000
## 22 0.30769231 0.35714286 0.35714286 0.42857143 0.53846154
## 23 0.27272727 0.30000000 0.27272727 0.30000000 0.33333333
## 24 0.35714286 0.38461538 0.23076923 0.30769231 0.61538462
## 25 0.30000000 0.57142857 0.44444444 0.30769231 0.25000000
## 26 0.33333333 0.42857143 0.18181818 0.50000000 0.37500000
## 27 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 28 0.46153846 0.45454545 0.33333333 0.30000000 0.33333333
## 29 0.36363636 0.30000000 0.27272727 0.55555556 0.36363636
## 30 0.41666667 0.54545455 0.30769231 0.54545455 0.28571429
## 31 0.57142857 0.53333333 0.57142857 0.71428571 0.50000000
## 32 0.64285714 0.76923077 0.58333333 0.35714286 0.76923077
## 33 0.50000000 0.60000000 0.66666667 0.36363636 0.50000000
## 34 0.53846154 0.53846154 0.46153846 0.36363636 0.50000000
## 35 0.83333333 0.45454545 0.45454545 0.63636364 0.63636364
## 36 0.40000000 0.66666667 0.41666667 0.38461538 0.53846154
## 37 0.57142857 0.57142857 0.61538462 0.69230769 0.60000000
## 38 0.41666667 0.42857143 0.58333333 0.38461538 0.38461538
## 39 0.33333333 0.61538462 0.57142857 0.46153846 0.35714286
## 40 0.58333333 0.53846154 0.63636364 0.75000000 0.38461538
## 41 0.53846154 0.75000000 0.58333333 0.58333333 0.61538462
## 42 0.21428571 0.41666667 0.33333333 0.35714286 0.61538462
## 43 0.58333333 0.45454545 0.33333333 0.50000000 0.60000000
## 44 0.50000000 0.50000000 0.63636364 0.27272727 0.38461538
## 45 0.54545455 0.41666667 0.72727273 0.58333333 0.64285714
## 46 0.50000000 0.50000000 0.54545455 0.54545455 0.66666667
## 47 0.37500000 0.40000000 0.45454545 0.30000000 0.27272727
## 48 0.58333333 0.33333333 0.50000000 0.25000000 0.38461538
## 49 0.25000000 0.38461538 0.60000000 0.70000000 0.45454545
## 50 0.53846154 0.76923077 0.33333333 0.57142857 0.83333333
## 51 0.25000000 0.41666667 0.45454545 0.45454545 0.41666667
## 52 0.50000000 0.20000000 0.22222222 0.30000000 0.33333333
## 53 0.61538462 0.55555556 0.50000000 0.53846154 0.46153846
## 54 0.61538462 0.61538462 0.58333333 0.73333333 0.46153846
## 55 0.44444444 0.33333333 0.36363636 0.36363636 0.33333333
## 56 0.25000000 0.33333333 0.30000000 0.33333333 0.33333333
## 57 0.53846154 0.45454545 0.42857143 0.50000000 0.28571429
## 58 0.30769231 0.55555556 0.53846154 0.50000000 0.54545455
## 59 0.57142857 0.66666667 0.71428571 0.75000000 0.64285714
## 60 0.08333333 0.50000000 0.37500000 0.28571429 0.11111111
## 61 0.60000000 0.50000000 0.46153846 0.58333333 0.75000000
## 62 0.60000000 0.60000000 0.45454545 0.70000000 0.50000000
## 63 0.25000000 0.50000000 0.09090909 0.27272727 0.50000000
## 64 0.50000000 0.42857143 0.57142857 0.36363636 0.40000000
## 65 0.18181818 0.54545455 0.21428571 0.40000000 0.33333333
## 66 0.30769231 0.50000000 0.45454545 0.41666667 0.16666667
## 67 0.38461538 0.50000000 0.41666667 0.26666667 0.46153846
## 68 0.75000000 0.57142857 0.71428571 0.66666667 0.71428571
## 69 0.36363636 0.36363636 0.30000000 0.36363636 0.23076923
## 70 0.53846154 0.46153846 0.50000000 0.30769231 0.50000000
## 71 0.30769231 0.44444444 0.36363636 0.41666667 0.33333333
## 72 0.35714286 0.46153846 0.46153846 0.53846154 0.46153846
## 73 0.83333333 0.58333333 0.33333333 0.61538462 0.66666667
## 74 0.38461538 0.46153846 0.70000000 0.40000000 0.15384615
## 75 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 76 0.53333333 0.66666667 0.50000000 0.42857143 0.60000000
## 77 0.64285714 0.28571429 0.50000000 0.57142857 0.53846154
## 78 0.61538462 0.66666667 0.46153846 0.53846154 0.66666667
## 79 0.12500000 0.00000000 0.10000000 0.22222222 0.00000000
## 80 0.22222222 0.27272727 0.25000000 0.50000000 0.11111111
## 81 0.69230769 0.41666667 0.53846154 0.42857143 0.23076923
## 82 0.54545455 0.16666667 0.37500000 0.41666667 0.50000000
## 83 0.41666667 0.46666667 0.46153846 0.41666667 0.33333333
## 84 0.16666667 0.00000000 0.00000000 0.33333333 0.00000000
## 85 0.28571429 0.14285714 0.25000000 0.22222222 0.12500000
## 86 0.20000000 0.09090909 0.36363636 0.27272727 0.40000000
## 87 0.46153846 0.30769231 0.21428571 0.21428571 0.30769231
## 88 0.54545455 0.45454545 0.23076923 0.25000000 0.54545455
## 89 0.41666667 0.23076923 0.27272727 0.30769231 0.27272727
## 90 0.50000000 0.41666667 0.07692308 0.22222222 0.09090909
## 91 0.41666667 0.16666667 0.38461538 0.38461538 0.41666667
## 92 0.26666667 0.66666667 0.33333333 0.61538462 0.53333333
## 93 0.50000000 0.66666667 0.66666667 0.33333333 0.61538462
## 94 0.50000000 0.41666667 0.30000000 0.40000000 0.45454545
## 95 0.37500000 0.37500000 0.50000000 0.50000000 0.50000000
## 96 0.45454545 0.36363636 0.45454545 0.40000000 0.18181818
## 97 0.33333333 0.38461538 0.55555556 0.15384615 0.41666667
## 98 0.36363636 0.36363636 0.45454545 0.36363636 0.50000000
## 99 0.25000000 0.13333333 0.45454545 0.25000000 0.33333333
## 100 0.25000000 0.60000000 0.36363636 0.10000000 0.16666667
## 101 0.22222222 0.23076923 0.18181818 0.36363636 0.00000000
## 102 0.33333333 0.50000000 0.44444444 0.20000000 0.30000000
## 103 0.45454545 0.33333333 0.55555556 0.35714286 0.20000000
## 104 0.35714286 0.21428571 0.66666667 0.42857143 0.35714286
## 105 0.12500000 0.40000000 0.25000000 0.36363636 0.28571429
## 106 0.12500000 0.20000000 0.22222222 0.28571429 0.37500000
## 107 0.25000000 0.28571429 0.25000000 0.30000000 0.20000000
## 108 0.25000000 0.28571429 0.45454545 0.27272727 0.66666667
## 109 0.21428571 0.23076923 0.28571429 0.41666667 0.33333333
## 110 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 111 0.22222222 0.18181818 0.40000000 0.18181818 0.50000000
## 112 0.20000000 0.23076923 0.50000000 0.36363636 0.27272727
## 113 0.10000000 0.10000000 0.08333333 0.25000000 0.40000000
## 114 0.15384615 0.22222222 0.18181818 0.11111111 0.22222222
## 115 0.30769231 0.63636364 0.10000000 0.28571429 0.27272727
## 116 0.16666667 0.11111111 0.14285714 0.14285714 0.16666667
## 117 0.35714286 0.38461538 0.50000000 0.54545455 0.16666667
## 118 0.50000000 0.53846154 0.50000000 0.46153846 0.50000000
## 119 0.28571429 0.00000000 0.33333333 0.14285714 0.50000000
## 120 0.00000000 0.00000000 0.11111111 0.00000000 0.09090909
## 121 0.45454545 0.08333333 0.25000000 0.16666667 0.10000000
## 122 0.50000000 0.50000000 0.42857143 0.07142857 0.42857143
## 123 0.25000000 0.27272727 0.25000000 0.27272727 0.20000000
## 124 0.00000000 0.25000000 0.25000000 0.10000000 0.12500000
## 125 0.00000000 0.14285714 0.00000000 0.16666667 0.00000000
## 126 0.15384615 0.54545455 0.15384615 0.20000000 0.09090909
## 127 0.08333333 0.41666667 0.23076923 0.16666667 0.09090909
## 128 0.46153846 0.41666667 0.46153846 0.33333333 0.75000000
## 129 0.45454545 0.70000000 0.40000000 0.10000000 0.00000000
## 130 0.46666667 0.28571429 0.41666667 0.46153846 0.30769231
## 131 0.35714286 0.18181818 0.63636364 0.54545455 0.25000000
## 132 0.27272727 0.33333333 0.30769231 0.18181818 0.27272727
## 133 0.12500000 0.42857143 0.22222222 0.09090909 0.28571429
## 134 0.18181818 0.23076923 0.23076923 0.27272727 0.33333333
## 135 0.41666667 0.07142857 0.41666667 0.23076923 0.15384615
## 136 0.33333333 0.30000000 0.30000000 0.25000000 0.36363636
## 137 0.40000000 0.45454545 0.23076923 0.45454545 0.20000000
## 138 0.23076923 0.33333333 0.28571429 0.45454545 0.12500000
## 139 0.00000000 0.00000000 0.00000000 0.22222222 0.22222222
## 140 0.28571429 0.33333333 0.00000000 0.50000000 0.28571429
## 141 0.22222222 0.10000000 0.40000000 0.37500000 0.30000000
## 142 0.09090909 0.11111111 0.30000000 0.30000000 0.30000000
## 143 0.22222222 0.30000000 0.20000000 0.20000000 0.28571429
## 144 0.09090909 0.10000000 0.40000000 0.20000000 0.23076923
## 145 0.18181818 0.20000000 0.28571429 0.40000000 0.11111111
## 146 0.14285714 0.37500000 0.14285714 0.12500000 0.28571429
## 147 0.50000000 0.57142857 0.00000000 0.12500000 0.40000000
## 148 0.30000000 0.18181818 0.33333333 0.10000000 0.30000000
## 149 0.30769231 0.30000000 0.08333333 0.36363636 0.25000000
## 150 0.20000000 0.11111111 0.12500000 0.25000000 0.11111111
## 151 0.16666667 0.25000000 0.33333333 0.25000000 0.30000000
## 152 0.28571429 0.14285714 0.28571429 0.27272727 0.16666667
## 153 0.45454545 0.60000000 0.33333333 0.22222222 0.33333333
## 154 0.16666667 0.25000000 0.28571429 0.16666667 0.50000000
## 155 0.14285714 0.12500000 0.28571429 0.25000000 0.20000000
## 156 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 157 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 158 0.20000000 0.50000000 0.33333333 0.44444444 0.54545455
## 159 0.09090909 0.16666667 0.15384615 0.18181818 0.50000000
## 160 0.50000000 0.16666667 0.42857143 0.00000000 0.16666667
## 161 0.10000000 0.25000000 0.44444444 0.37500000 0.00000000
## 162 0.40000000 0.28571429 0.37500000 0.22222222 0.50000000
## 163 0.16666667 0.25000000 0.00000000 0.33333333 0.28571429
## 164 0.16666667 0.25000000 0.11111111 0.12500000 0.28571429
## 165 0.40000000 0.14285714 0.00000000 0.00000000 0.20000000
## 166 0.16666667 0.00000000 0.28571429 0.10000000 0.00000000
## 167 0.55555556 0.30000000 0.20000000 0.36363636 0.30000000
## 168 0.22222222 0.20000000 0.50000000 0.00000000 0.00000000
## 169 0.00000000 0.00000000 0.14285714 0.00000000 0.00000000
## 170 0.14285714 0.37500000 0.50000000 0.42857143 0.00000000
## 171 0.40000000 0.25000000 0.37500000 0.00000000 0.42857143
## 172 0.18181818 0.22222222 0.25000000 0.37500000 0.30000000
## 173 0.33333333 0.33333333 0.41666667 0.36363636 0.25000000
## 174 0.33333333 0.00000000 0.00000000 0.00000000 0.00000000
## 175 0.22222222 0.22222222 0.20000000 0.18181818 0.18181818
## 176 0.37500000 0.44444444 0.22222222 0.22222222 0.25000000
## 177 0.75000000 0.14285714 0.60000000 0.33333333 0.16666667
## 178 0.25000000 0.16666667 0.30000000 0.33333333 0.18181818
## 179 0.40000000 0.27272727 0.00000000 0.50000000 0.11111111
## 180 0.14285714 0.11111111 0.14285714 0.33333333 0.11111111
## 181 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 182 0.14285714 0.11111111 0.33333333 0.00000000 0.14285714
## 183 0.18181818 0.12500000 0.60000000 0.16666667 0.60000000
## 184 0.20000000 0.25000000 0.33333333 0.50000000 0.50000000
## 185 0.30000000 0.33333333 0.12500000 0.16666667 0.25000000
## 186 0.00000000 0.20000000 0.00000000 0.20000000 0.33333333
## 187 0.22222222 0.25000000 0.27272727 0.28571429 0.14285714
## 188 0.20000000 0.16666667 0.20000000 0.33333333 0.00000000
## 189 0.16666667 0.20000000 0.00000000 0.12500000 0.28571429
## 190 0.11111111 0.08333333 0.50000000 0.21428571 0.16666667
## 191 0.16666667 0.00000000 0.60000000 0.14285714 0.10000000
## 192 0.25000000 0.20000000 0.00000000 0.00000000 0.00000000
## 193 0.20000000 0.00000000 0.16666667 0.00000000 0.16666667
## 194 0.25000000 0.22222222 0.42857143 0.11111111 0.42857143
## 195 0.27272727 0.57142857 0.50000000 0.27272727 0.50000000
## 196 0.16666667 0.25000000 0.00000000 0.00000000 0.00000000
## 197 0.50000000 0.55555556 0.33333333 0.50000000 0.27272727
## 198 0.00000000 0.00000000 0.14285714 0.33333333 0.25000000
## 199 0.16666667 0.00000000 0.00000000 0.00000000 0.25000000
## 200 0.25000000 0.00000000 0.11111111 0.28571429 0.50000000
## 201 0.40000000 0.33333333 0.33333333 0.20000000 0.00000000
## 202 0.30000000 0.37500000 0.36363636 0.22222222 0.55555556
## 203 0.12500000 0.00000000 0.33333333 0.12500000 0.00000000
## 204 0.16666667 0.50000000 0.75000000 0.42857143 0.20000000
## 205 0.33333333 0.33333333 0.25000000 0.00000000 0.22222222
## 206 0.00000000 0.25000000 0.20000000 0.00000000 0.00000000
## 207 0.16666667 0.33333333 0.50000000 0.50000000 0.16666667
## 208 0.40000000 0.22222222 0.14285714 0.00000000 0.14285714
## 209 0.10000000 0.25000000 0.28571429 0.42857143 0.25000000
## 210 0.20000000 0.20000000 0.33333333 0.33333333 0.00000000
## 211 0.00000000 0.25000000 0.50000000 0.50000000 0.00000000
## 212 0.60000000 0.50000000 0.40000000 0.16666667 0.60000000
## 213 0.16666667 0.20000000 0.16666667 0.14285714 0.00000000
## 214 0.50000000 0.40000000 0.33333333 0.28571429 0.42857143
## 215 0.25000000 0.22222222 0.14285714 0.00000000 0.14285714
## 216 0.40000000 0.16666667 0.33333333 0.00000000 0.12500000
## 217 0.00000000 0.22222222 0.11111111 0.16666667 0.00000000
## 218 0.50000000 0.33333333 0.28571429 0.50000000 0.14285714
## 219 0.12500000 0.25000000 0.22222222 0.50000000 0.16666667
## 220 0.57142857 0.16666667 0.66666667 0.66666667 0.22222222
## 221 0.50000000 0.60000000 0.40000000 0.40000000 0.16666667
## 222 0.00000000 0.11111111 0.33333333 0.22222222 0.66666667
## 223 0.00000000 0.25000000 0.00000000 0.00000000 0.00000000
## 224 0.20000000 0.12500000 0.20000000 0.28571429 0.33333333
## 225 0.20000000 1.00000000 0.14285714 0.25000000 0.33333333
## 226 0.50000000 0.66666667 0.71428571 0.42857143 0.28571429
## 227 0.30000000 0.12500000 0.44444444 0.50000000 0.11111111
## 228 0.00000000 0.00000000 0.00000000 0.20000000 0.25000000
## 229 0.00000000 0.33333333 0.00000000 0.20000000 0.33333333
## 230 0.40000000 0.20000000 0.22222222 0.50000000 0.50000000
## 231 0.33333333 0.50000000 0.20000000 0.12500000 0.00000000
## 232 0.14285714 0.20000000 0.57142857 0.20000000 0.16666667
## 233 0.40000000 0.16666667 0.33333333 0.40000000 0.16666667
## 234 0.00000000 0.00000000 0.14285714 0.25000000 0.50000000
## 235 0.00000000 0.16666667 0.00000000 0.00000000 0.00000000
## 236 0.25000000 0.14285714 0.16666667 0.12500000 0.25000000
## 237 0.00000000 0.33333333 0.20000000 0.00000000 0.00000000
## 238 0.00000000 0.16666667 0.16666667 0.25000000 0.25000000
## 239 0.14285714 0.20000000 0.20000000 0.25000000 0.12500000
## 240 0.20000000 0.20000000 0.33333333 0.00000000 0.33333333
## 241 0.00000000 0.00000000 0.12500000 0.00000000 0.00000000
## 242 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 243 0.66666667 0.33333333 0.00000000 0.00000000 0.20000000
## 244 0.40000000 0.16666667 0.00000000 0.33333333 0.25000000
## 245 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 246 0.00000000 0.50000000 0.00000000 0.33333333 0.50000000
## 247 0.33333333 0.00000000 0.50000000 0.33333333 0.00000000
## 248 0.20000000 0.33333333 0.33333333 0.33333333 0.20000000
## 249 0.60000000 0.20000000 0.50000000 0.20000000 0.16666667
## 250 0.60000000 0.50000000 1.00000000 0.40000000 0.25000000
## 251 0.00000000 0.20000000 0.16666667 0.00000000 0.20000000
## 252 0.00000000 0.00000000 0.00000000 0.00000000 NaN
## 253 0.00000000 0.00000000 0.00000000 0.00000000 0.33333333
## 254 0.20000000 0.25000000 0.33333333 0.00000000 0.20000000
## 255 0.00000000 0.28571429 0.28571429 0.25000000 0.50000000
## 256 0.50000000 0.40000000 0.25000000 0.50000000 0.16666667
## 257 0.00000000 0.42857143 0.00000000 0.16666667 0.14285714
## 258 0.20000000 0.25000000 0.25000000 0.20000000 0.16666667
## 259 0.40000000 0.33333333 0.66666667 0.25000000 0.00000000
## 260 0.66666667 0.00000000 0.66666667 0.00000000 0.25000000
## 261 0.00000000 0.16666667 0.00000000 0.00000000 0.25000000
## 262 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 263 0.00000000 NaN 0.00000000 0.00000000 0.00000000
## 264 0.25000000 0.33333333 0.25000000 0.25000000 0.33333333
## 265 0.33333333 0.25000000 0.66666667 0.33333333 0.75000000
## 266 0.50000000 0.20000000 0.33333333 0.25000000 0.33333333
## 267 0.66666667 0.50000000 0.25000000 0.66666667 0.50000000
## 268 0.50000000 0.66666667 0.16666667 0.50000000 0.50000000
## 269 0.33333333 0.20000000 0.50000000 0.25000000 0.16666667
## 270 0.25000000 0.33333333 0.20000000 0.33333333 0.20000000
## 271 0.20000000 0.20000000 0.25000000 0.33333333 0.20000000
## 272 0.00000000 0.00000000 0.20000000 0.50000000 0.00000000
## 273 0.20000000 0.25000000 0.00000000 0.28571429 0.00000000
## 274 0.33333333 0.20000000 0.20000000 0.20000000 0.33333333
## 275 0.20000000 0.25000000 0.66666667 0.40000000 0.25000000
## 276 0.16666667 0.36363636 0.28571429 0.27272727 0.41666667
## 277 0.36363636 0.27272727 0.25000000 0.23076923 0.44444444
## 278 0.63636364 0.50000000 0.58333333 0.70000000 0.45454545
## 279 0.18181818 0.21428571 0.26666667 0.09090909 0.33333333
## 280 0.09090909 0.08333333 0.25000000 0.27272727 0.07692308
## 281 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
## 282 0.11111111 0.20000000 0.00000000 0.14285714 0.00000000
## 283 0.25000000 0.33333333 0.20000000 0.00000000 0.25000000
## 284 0.40000000 0.37500000 0.28571429 0.10000000 0.50000000
## 285 0.16666667 0.14285714 0.00000000 0.20000000 0.75000000
## 286 0.28571429 0.14285714 0.00000000 0.00000000 0.40000000
## 287 0.40000000 0.16666667 0.37500000 0.14285714 0.33333333
## 288 0.00000000 0.20000000 0.20000000 0.33333333 0.14285714
## 289 0.00000000 0.20000000 0.16666667 0.33333333 0.33333333
## 290 0.20000000 0.25000000 0.16666667 0.00000000 0.33333333
## 291 0.00000000 0.00000000 0.16666667 0.00000000 0.40000000
## 292 0.28571429 0.00000000 0.25000000 0.33333333 0.33333333
## 293 0.25000000 0.25000000 0.40000000 0.28571429 0.50000000
## 294 0.16666667 0.25000000 0.00000000 0.33333333 0.20000000
## 295 0.25000000 0.00000000 0.25000000 0.20000000 0.00000000
## 296 0.50000000 0.20000000 0.00000000 0.40000000 0.40000000
## 297 0.00000000 0.25000000 0.40000000 0.40000000 0.20000000
## 298 0.61538462 0.61538462 0.42857143 0.63636364 0.75000000
## 299 0.66666667 0.33333333 0.66666667 0.20000000 0.33333333
## 300 0.50000000 0.33333333 0.00000000 0.00000000 0.33333333
library(biomod2)
## Loading required package: raster
##
## Attaching package: 'raster'
## The following objects are masked from 'package:ape':
##
## rotate, zoom
## Loading required package: reshape
## Loading required package: ggplot2
## biomod2 3.3-7 loaded.
##
## Type browseVignettes(package='biomod2') to access directly biomod2 vignettes.
path.wd<-getwd()
# species
# occurrences
xy <- inv[,1:2]
head(xy)
## x y
## 1 142.25 -10.25
## 2 142.25 -10.75
## 3 131.25 -11.25
## 4 132.25 -11.25
## 5 142.25 -11.25
## 6 142.75 -11.25
sp_occ <- inv[11]
# env
current <- inv[3:7]
head(current)
## aetpet gdd p pet stdp
## 1 0.3180346 7965.1 1595.7 1950.320 137.8134
## 2 0.2807616 7888.9 1693.7 1991.475 156.3950
## 3 0.2638533 8165.3 1595.0 2179.968 127.0621
## 4 0.2790938 8195.6 1346.0 1919.897 114.7686
## 5 0.3030646 7858.1 1711.1 1795.255 158.3286
## 6 0.3217786 7888.5 1711.1 1788.220 151.8030
## BIOMOD
setwd(path.wd)
t1 <- Sys.time()
sp<-1
### Formating the data with the BIOMOD_FormatingData() function form the package biomod2
myBiomodData <- BIOMOD_FormatingData( resp.var = as.numeric(sp_occ[,sp]),
expl.var = current,
resp.xy = xy,
resp.name = colnames(sp_occ)[sp])
##
## -=-=-=-=-=-=-=-=-=-=-=-= species_occ Data Formating -=-=-=-=-=-=-=-=-=-=-=-=
##
## Response variable name was converted into species.occ
## > No pseudo absences selection !
## ! No data has been set aside for modeling evaluation
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
myBiomodOption <- Print_Default_ModelingOptions()
##
## Defaut modeling options. copy, change what you want paste it as arg to BIOMOD_ModelingOptions
##
##
## -=-=-=-=-=-=-=-=-=-=-=-= 'BIOMOD.Model.Options' -=-=-=-=-=-=-=-=-=-=-=-=
##
##
## GLM = list( type = 'quadratic',
## interaction.level = 0,
## myFormula = NULL,
## test = 'AIC',
## family = binomial(link = 'logit'),
## mustart = 0.5,
## control = glm.control(epsilon = 1e-08, maxit = 50
## , trace = FALSE) ),
##
##
## GBM = list( distribution = 'bernoulli',
## n.trees = 2500,
## interaction.depth = 7,
## n.minobsinnode = 5,
## shrinkage = 0.001,
## bag.fraction = 0.5,
## train.fraction = 1,
## cv.folds = 3,
## keep.data = FALSE,
## verbose = FALSE,
## perf.method = 'cv'),
##
## GAM = list( algo = 'GAM_mgcv',
## type = 's_smoother',
## k = -1,
## interaction.level = 0,
## myFormula = NULL,
## family = binomial(link = 'logit'),
## method = 'GCV.Cp',
## optimizer = c('outer','newton'),
## select = FALSE,
## knots = NULL,
## paraPen = NULL,
## control = list(nthreads = 1, irls.reg = 0, epsilon = 1e-07
## , maxit = 200, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15
## , rank.tol = 1.49011611938477e-08
## , nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04, iterlim=200, check.analyticals=0)
## , optim = list(factr=1e+07)
## , newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30, use.svd=0)
## , outerPIsteps = 0, idLinksBases = TRUE, scalePenalty = TRUE
## , keepData = FALSE, scale.est = fletcher, edge.correct = FALSE) ),
##
##
## CTA = list( method = 'class',
## parms = 'default',
## cost = NULL,
## control = list(xval = 5, minbucket = 5, minsplit = 5
## , cp = 0.001, maxdepth = 25) ),
##
##
## ANN = list( NbCV = 5,
## size = NULL,
## decay = NULL,
## rang = 0.1,
## maxit = 200),
##
## SRE = list( quant = 0.025),
##
## FDA = list( method = 'mars',
## add_args = NULL),
##
## MARS = list( type = 'simple',
## interaction.level = 0,
## myFormula = NULL,
## nk = NULL,
## penalty = 2,
## thresh = 0.001,
## nprune = NULL,
## pmethod = 'backward'),
##
## RF = list( do.classif = TRUE,
## ntree = 500,
## mtry = 'default',
## nodesize = 5,
## maxnodes = NULL),
##
## MAXENT.Phillips = list( path_to_maxent.jar = '/private/var/folders/y_/356n2gv11490bhhswc7m8m6w0000gn/T/RtmpDWdwN5/Rbuild6c666bd1c584/ecospat/vignettes',
## memory_allocated = 512,
## background_data_dir = 'default',
## maximumbackground = 'default',
## maximumiterations = 200,
## visible = FALSE,
## linear = TRUE,
## quadratic = TRUE,
## product = TRUE,
## threshold = TRUE,
## hinge = TRUE,
## lq2lqptthreshold = 80,
## l2lqthreshold = 10,
## hingethreshold = 15,
## beta_threshold = -1,
## beta_categorical = -1,
## beta_lqp = -1,
## beta_hinge = -1,
## betamultiplier = 1,
## defaultprevalence = 0.5),
##
## MAXENT.Tsuruoka = list( l1_regularizer = 0,
## l2_regularizer = 0,
## use_sgd = FALSE,
## set_heldout = 0,
## verbose = FALSE)
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
myBiomodOption@GLM$test = 'none'
myBiomodOption@GBM$interaction.depth = 2
### Calibration of simple bivariate models
my.ESM <- ecospat.ESM.Modeling( data=myBiomodData,
models=c('GLM','RF'),
models.options=myBiomodOption,
NbRunEval=1,
DataSplit=70,
weighting.score=c("AUC"),
parallel=F)
##
## > Automatic weights creation to rise a 0.5 prevalence
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.1 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( aetpet gdd )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.1_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.1_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.1 ~ 1 + aetpet + I(aetpet^2) + gdd + I(gdd^2)
## <environment: 0x7fecf18410b0>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.1_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.1 ~ 1 + aetpet + I(aetpet^2) + gdd + I(gdd^2)
## <environment: 0x7fed0d4f16d8>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.2 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( aetpet p )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.2_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.2_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.2 ~ 1 + aetpet + I(aetpet^2) + p + I(p^2)
## <environment: 0x7fed103ec0e8>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.2_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.2 ~ 1 + aetpet + I(aetpet^2) + p + I(p^2)
## <environment: 0x7fecf3fdfc70>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.3 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( aetpet pet )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.3_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.3_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.3 ~ 1 + aetpet + I(aetpet^2) + pet + I(pet^2)
## <environment: 0x7fecf7010f08>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.3_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.3 ~ 1 + aetpet + I(aetpet^2) + pet + I(pet^2)
## <environment: 0x7fecf2faba98>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.4 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( aetpet stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.4_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.4_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.4 ~ 1 + aetpet + I(aetpet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf5119240>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.4_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.4 ~ 1 + aetpet + I(aetpet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf5077e98>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.5 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( gdd p )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.5_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.5_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.5 ~ 1 + gdd + I(gdd^2) + p + I(p^2)
## <environment: 0x7fecf1eb8710>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.5_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.5 ~ 1 + gdd + I(gdd^2) + p + I(p^2)
## <environment: 0x7fecf7ff5e08>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.6 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( gdd pet )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.6_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.6_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.6 ~ 1 + gdd + I(gdd^2) + pet + I(pet^2)
## <environment: 0x7fecf55d4800>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.6_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.6 ~ 1 + gdd + I(gdd^2) + pet + I(pet^2)
## <environment: 0x7fecf54fa038>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.7 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( gdd stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.7_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.7_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.7 ~ 1 + gdd + I(gdd^2) + stdp + I(stdp^2)
## <environment: 0x7fecf52e4918>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.7_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.7 ~ 1 + gdd + I(gdd^2) + stdp + I(stdp^2)
## <environment: 0x7fecf4d21e28>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.8 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( p pet )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.8_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.8_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.8 ~ 1 + p + I(p^2) + pet + I(pet^2)
## <environment: 0x7fecf52a7918>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.8_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.8 ~ 1 + p + I(p^2) + pet + I(pet^2)
## <environment: 0x7fecf757a000>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.9 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( p stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.9_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.9_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.9 ~ 1 + p + I(p^2) + stdp + I(stdp^2)
## <environment: 0x7fecf2a69828>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.9_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.9 ~ 1 + p + I(p^2) + stdp + I(stdp^2)
## <environment: 0x7fecf7268428>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## Loading required library...
##
## Checking Models arguments...
##
## ! User defined data-split table was given -> NbRunEval, DataSplit and do.full.models argument will be ignored
## Creating suitable Workdir...
##
## > Automatic weights creation to rise a 0.5 prevalence
##
##
## -=-=-=-=-=-=-=-=-=-=-= ESM.BIOMOD.10 Modeling Summary -=-=-=-=-=-=-=-=-=-=-=
##
## 2 environmental variables ( pet stdp )
## Number of evaluation repetitions : 2
## Models selected : GLM RF
##
## Total number of model runs : 4
##
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
##
## -=-=-=- Run : ESM.BIOMOD.10_AllData
##
##
## -=-=-=--=-=-=- ESM.BIOMOD.10_AllData_RUN1
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.10 ~ 1 + pet + I(pet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf53a9c28>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Evaluating Model stuff...
##
## -=-=-=--=-=-=- ESM.BIOMOD.10_AllData_RUN2
##
## Model=GLM ( quadratic with no interaction )
## No stepwise procedure
## ! You might be confronted to models convergence issues !
## selected formula : ESM.BIOMOD.10 ~ 1 + pet + I(pet^2) + stdp + I(stdp^2)
## <environment: 0x7fecf4622ee8>
##
## Model scaling...
## Evaluating Model stuff...
## Model=Breiman and Cutler's random forests for classification and regression
## Model scaling...
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
##
## Evaluating Model stuff...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
### Evaluation and average of simple bivariate models to ESMs
my.ESM_EF <- ecospat.ESM.EnsembleModeling(my.ESM,weighting.score=c("SomersD"),threshold=0)
### Projection of simple bivariate models into new space
my.ESM_proj_current <- ecospat.ESM.Projection(ESM.modeling.output=my.ESM,
new.env=current)
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.1_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.1_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.2_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.2_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.3_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.3_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.4_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.4_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.5_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.5_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.6_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.6_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.7_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.7_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.8_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.8_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.9_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.9_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
##
## -=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=
##
## ! 'do.stack' arg is always set as TRUE for data.frame/matrix dataset
## > Projecting ESM.BIOMOD.10_AllData_RUN2_GLM ...
## > Projecting ESM.BIOMOD.10_AllData_RUN2_RF ...
## -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
### Projection of calibrated ESMs into new space
my.ESM_EFproj_current <- ecospat.ESM.EnsembleProjection(ESM.prediction.output=my.ESM_proj_current,
ESM.EnsembleModeling.output=my.ESM_EF)
Input data for the first argument (proba) as data frame of rough probabilities from SDMs for all species in columns in the considered sites in rows.
proba <- ecospat.testData[,73:92]
Input data for the second argument (sr) as data frame with richness value in the first column and sites.
sr <- as.data.frame(rowSums(proba))
ecospat.SESAM.prr(proba, sr)
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## [1] "test.prr, processing row 297"
## [1] "test.prr, processing row 298"
## [1] "test.prr, processing row 299"
## [1] "test.prr, processing row 300"
## glm_Agrostis_capillaris glm_Leontodon_hispidus_sl
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## glm_Dactylis_glomerata glm_Trifolium_repens_sstr
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## glm_Geranium_sylvaticum glm_Ranunculus_acris_sl glm_Prunella_vulgaris
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## glm_Veronica_chamaedrys glm_Taraxacum_officinale_aggr
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## glm_Plantago_lanceolata glm_Potentilla_erecta glm_Carex_sempervirens
## 1 0 1 1
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## 256 0 0 1
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## 262 0 0 0
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## 269 0 0 1
## 270 0 0 1
## 271 0 0 1
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## 275 0 0 1
## 276 1 1 0
## 277 0 0 0
## 278 1 1 0
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## 286 0 0 0
## 287 0 0 1
## 288 0 0 1
## 289 0 0 1
## 290 0 0 1
## 291 0 0 1
## 292 0 0 1
## 293 0 0 1
## 294 0 0 1
## 295 0 0 1
## 296 0 0 1
## 297 0 0 1
## 298 1 0 0
## 299 0 0 0
## 300 0 0 1
## glm_Soldanella_alpina glm_Cynosurus_cristatus
## 1 0 0
## 2 0 1
## 3 0 1
## 4 0 1
## 5 0 1
## 6 0 1
## 7 0 0
## 8 0 1
## 9 0 0
## 10 0 1
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## 76 0 1
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## 242 1 0
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## 244 0 0
## 245 1 0
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## 247 0 0
## 248 1 0
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## 251 0 0
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## 254 1 0
## 255 1 0
## 256 0 0
## 257 1 0
## 258 1 0
## 259 0 0
## 260 1 0
## 261 0 0
## 262 0 0
## 263 0 0
## 264 0 0
## 265 1 0
## 266 1 0
## 267 0 0
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## 282 1 0
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## 284 0 0
## 285 1 0
## 286 0 0
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## 288 0 0
## 289 1 0
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## 291 0 0
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## 293 1 0
## 294 0 0
## 295 0 0
## 296 1 0
## 297 1 0
## 298 0 0
## 299 0 0
## 300 0 0
## glm_Campanula_scheuchzeri glm_Festuca_pratensis_sl
## 1 0 0
## 2 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 1
## 7 0 1
## 8 0 1
## 9 0 1
## 10 0 1
## 11 0 1
## 12 0 1
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## 31 0 1
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## 37 0 1
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## 51 0 1
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## 146 1 0
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## 148 0 0
## 149 1 0
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## 157 1 0
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## 159 1 0
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## 162 1 0
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## 167 0 0
## 168 1 0
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## 228 1 0
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## 247 1 0
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## 255 1 0
## 256 1 0
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## 262 1 0
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## 264 1 0
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## 286 1 0
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## 290 1 0
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## 293 1 0
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## 295 1 0
## 296 1 0
## 297 1 0
## 298 0 1
## 299 1 0
## 300 0 0
## glm_Bromus_erectus_sstr glm_Saxifraga_oppositifolia glm_Daucus_carota
## 1 0 0 0
## 2 0 0 0
## 3 0 0 0
## 4 0 0 0
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## 12 1 0 0
## 13 0 0 0
## 14 0 0 0
## 15 0 0 0
## 16 1 0 1
## 17 0 0 0
## 18 0 0 0
## 19 1 0 1
## 20 1 0 1
## 21 0 0 0
## 22 1 0 1
## 23 0 0 0
## 24 0 0 0
## 25 0 0 0
## 26 0 0 0
## 27 0 0 0
## 28 0 0 0
## 29 0 0 0
## 30 0 0 0
## 31 1 0 1
## 32 0 0 0
## 33 0 0 0
## 34 1 0 1
## 35 0 0 0
## 36 1 0 1
## 37 1 0 1
## 38 1 0 1
## 39 0 0 0
## 40 0 0 0
## 41 0 0 0
## 42 0 0 0
## 43 0 0 0
## 44 0 0 0
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## 50 0 0 0
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## 110 1 0 0
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## 141 0 0 0
## 142 0 0 0
## 143 0 0 0
## 144 0 0 0
## 145 1 0 1
## 146 0 0 0
## 147 0 0 0
## 148 0 0 0
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## 150 0 0 0
## 151 0 0 0
## 152 0 0 0
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## 160 0 0 0
## 161 0 0 0
## 162 0 0 0
## 163 0 0 0
## 164 0 0 0
## 165 0 0 0
## 166 0 0 0
## 167 0 0 0
## 168 0 0 0
## 169 0 0 0
## 170 0 0 0
## 171 0 0 0
## 172 0 0 0
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## 174 0 0 0
## 175 0 0 0
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## 180 0 0 0
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## 186 0 0 0
## 187 0 0 0
## 188 0 0 0
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## 190 0 0 0
## 191 0 0 0
## 192 0 0 0
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## 194 0 0 0
## 195 0 0 0
## 196 0 0 0
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## 199 0 0 0
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## 235 0 0 0
## 236 0 1 0
## 237 0 0 0
## 238 0 0 0
## 239 0 0 0
## 240 0 0 0
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## 242 0 0 0
## 243 0 0 0
## 244 0 0 0
## 245 0 0 0
## 246 0 0 0
## 247 0 0 0
## 248 0 1 0
## 249 0 0 0
## 250 0 0 0
## 251 0 0 0
## 252 0 0 0
## 253 0 1 0
## 254 0 1 0
## 255 0 0 0
## 256 0 1 0
## 257 0 0 0
## 258 0 1 0
## 259 0 1 0
## 260 0 0 0
## 261 0 1 0
## 262 0 1 0
## 263 0 1 0
## 264 0 1 0
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## 266 0 1 0
## 267 0 1 0
## 268 0 1 0
## 269 0 1 0
## 270 0 1 0
## 271 0 1 0
## 272 0 1 0
## 273 0 1 0
## 274 0 1 0
## 275 0 1 0
## 276 0 0 0
## 277 0 0 0
## 278 1 0 1
## 279 0 0 0
## 280 0 0 0
## 281 0 0 0
## 282 0 0 0
## 283 0 0 0
## 284 0 0 0
## 285 0 0 0
## 286 0 0 0
## 287 0 0 0
## 288 0 0 0
## 289 0 0 0
## 290 0 0 0
## 291 0 0 0
## 292 0 0 0
## 293 0 0 0
## 294 0 0 0
## 295 0 0 0
## 296 0 0 0
## 297 0 1 0
## 298 0 0 0
## 299 0 1 0
## 300 0 1 0
## glm_Pritzelago_alpina_sstr
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
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## 30 0
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## 100 0
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## 130 0
## 131 0
## 132 0
## 133 0
## 134 0
## 135 0
## 136 0
## 137 0
## 138 0
## 139 0
## 140 0
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Input data as a matrix of plots (rows) x species (columns). Input matrices should have column names (species names) and row names (sampling plots).
presence<-ecospat.testData[c(53,62,58,70,61,66,65,71,69,43,63,56,68,57,55,60,54,67,59,64)]
pred<-ecospat.testData[c(73:92)]
Define the number of permutations. It is recomended to use at least 10000 permutatiobns for the test. As an example we used nperm = 100, to reduce the computational time.
nbpermut <- 100
Define the outpath
outpath <- getwd()
Run the function ecospat.cons_Cscore
The function tests for non-random patterns of species co-occurrence in a presence-absence matrix. It calculates the C-score index for the whole community and for each species pair. An environmental constraint is applied during the generation of the null communities.
ecospat.cons_Cscore(presence, pred, nbpermut, outpath)
## Computing observed co-occurence matrix
## .............
## .............
## .............
## Computing permutations
## .............
## .............
## .............
## Permutations finished Wed Nov 22 11:45:23 2017
## .............
## .............
## Exporting dataset
## .............
## .............
## .............
## $ObsCscoreTot
## [1] 2675.468
##
## $SimCscoreTot
## [1] 2834.858
##
## $PVal.less
## [1] 0.00990099
##
## $PVal.greater
## [1] 1
##
## $SES.Tot
## [1] -4.12709
The function returns
A table is saved in the path specified where the same metrics are calculated for each species pair (only the table with species pairs with significant p.values is saved).