The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The function ltcmt conducts Line x Tester analysis for
multiple traits when the data contains only crosses. The
experimental design may be RCBD or Alpha lattice design.
# Load the package
library(gpbStat)
#Load the dataset
data("alphaltcmt")
# View the structure of dataframe.
str(alphaltcmt)
#> spc_tbl_ [60 × 7] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#> $ replication: chr [1:60] "r1" "r3" "r2" "r4" ...
#> $ block : chr [1:60] "b2" "b2" "b4" "b5" ...
#> $ line : chr [1:60] "DIL 2" "DIL 2" "DIL 2" "DIL 2" ...
#> $ tester : chr [1:60] "DIL-101" "DIL-101" "DIL-101" "DIL-101" ...
#> $ hsw : num [1:60] 25.7 24.5 23.7 25.1 23 ...
#> $ sh : num [1:60] 81.7 83.3 86 84.6 85.5 ...
#> $ gy : num [1:60] 25.9 41 65.7 47.3 30.8 ...
#> - attr(*, "spec")=List of 3
#> ..$ cols :List of 7
#> .. ..$ replication: list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ block : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ line : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ tester : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
#> .. ..$ hsw : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#> .. ..$ sh : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#> .. ..$ gy : list()
#> .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
#> ..$ default: list()
#> .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
#> ..$ delim : chr ","
#> ..- attr(*, "class")= chr "col_spec"
#> - attr(*, "problems")=<externalptr>
# Conduct Line x Tester analysis
result = ltcmt(alphaltcmt, replication, line, tester, alphaltcmt[,5:7], block)
#>
#> Analysis of Line x Tester for Multiple traits
#> Warning in sqrt(x): NaNs produced
#> Warning in sqrt(x): NaNs produced
#> Warning in sqrt(x): NaNs produced
#> Warning in sqrt(x): NaNs produced
#> Warning in sqrt(x): NaNs produced
#> Warning in sqrt(x): NaNs produced
# View the output
result
#> $Mean
#> $Mean$hsw
#> Tester
#> Line DIL 102 DIL-101 DIL-103
#> DIL 2 23.1800 24.7525 23.8525
#> DIL 3 25.0975 22.1300 25.4675
#> DIL 5 23.8625 24.4075 22.9050
#> DIL-1 24.3900 24.2800 26.4325
#> DIL-4 26.5250 25.3625 26.3225
#>
#> $Mean$sh
#> Tester
#> Line DIL 102 DIL-101 DIL-103
#> DIL 2 84.6225 83.8950 83.7725
#> DIL 3 84.4600 83.6100 83.0450
#> DIL 5 82.5875 83.0425 84.8300
#> DIL-1 83.8700 82.9375 84.2025
#> DIL-4 84.3250 84.2775 81.8175
#>
#> $Mean$gy
#> Tester
#> Line DIL 102 DIL-101 DIL-103
#> DIL 2 45.3125 44.9575 47.3975
#> DIL 3 54.7700 46.0625 55.0550
#> DIL 5 53.5300 58.2675 53.5525
#> DIL-1 48.8625 54.2675 44.7525
#> DIL-4 52.1400 60.5650 53.7975
#>
#>
#> $ANOVA
#> $ANOVA$hsw
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 123.534952 41.178317 5.2008236 0.006007676
#> Blocks within Replication 16 159.578141 9.973634 1.2596705 0.292005429
#> Crosses 14 95.647543 6.831967 0.8628778 0.602918614
#> Lines 4 44.421693 11.105423 1.0220298 0.406231362
#> Testers 2 6.558103 3.279052 0.3017705 0.740992561
#> Lines X Testers 8 44.667747 5.583468 0.5138454 0.839635289
#> Error 26 205.858982 7.917653 NA NA
#> Total 59 584.619618 NA NA NA
#>
#> $ANOVA$sh
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 47.847660 15.9492200 5.5792805 0.004311049
#> Blocks within Replication 16 61.895494 3.8684684 1.3532492 0.239549969
#> Crosses 14 39.935293 2.8525210 0.9978553 0.482967180
#> Lines 4 3.050693 0.7626733 0.1864544 0.944255260
#> Testers 2 2.468943 1.2344717 0.3017971 0.740973054
#> Lines X Testers 8 34.415657 4.3019571 1.0517198 0.413116072
#> Error 26 74.324946 2.8586518 NA NA
#> Total 59 224.003393 NA NA NA
#>
#> $ANOVA$gy
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 3171.01367 1057.00456 7.6631523 0.0007893935
#> Blocks within Replication 16 2338.12660 146.13291 1.0594455 0.4352040161
#> Crosses 14 1411.65982 100.83284 0.7310257 0.7261397075
#> Lines 4 787.60961 196.90240 0.9741847 0.4310920496
#> Testers 2 48.49009 24.24505 0.1199536 0.8872442280
#> Lines X Testers 8 575.56012 71.94502 0.3559517 0.9380005166
#> Error 26 3586.26808 137.93339 NA NA
#> Total 59 10507.06817 NA NA NA
#>
#>
#> $GCA.Line
#> hsw sh gy
#> DIL 2 -0.6695000 0.41033333 -5.6635000
#> DIL 3 -0.3661667 0.01866667 0.4098333
#> DIL 5 -0.8728333 -0.19966667 3.5640000
#> DIL-1 0.4363333 -0.01633333 -2.2585000
#> DIL-4 1.4721667 -0.21300000 3.9481667
#>
#> $GCA.Tester
#> hsw sh gy
#> DIL 102 0.01316667 0.2866667 -0.6296667
#> DIL-101 -0.41133333 -0.1338333 1.2713333
#> DIL-103 0.39816667 -0.1528333 -0.6416667
#>
#> $SCA
#> $SCA$hsw
#> Tester
#> Line DIL 102 DIL-101 DIL-103
#> DIL 2 -0.7615000 1.2355000 -0.4740000
#> DIL 3 0.8526667 -1.6903333 0.8376667
#> DIL 5 0.1243333 1.0938333 -1.2181667
#> DIL-1 -0.6573333 -0.3428333 1.0001667
#> DIL-4 0.4418333 -0.2961667 -0.1456667
#>
#> $SCA$sh
#> Tester
#> Line DIL 102 DIL-101 DIL-103
#> DIL 2 0.23916667 -0.06783333 -0.1713333
#> DIL 3 0.46833333 0.03883333 -0.5071667
#> DIL 5 -1.18583333 -0.31033333 1.4961667
#> DIL-1 -0.08666667 -0.59866667 0.6853333
#> DIL-4 0.56500000 0.93800000 -1.5030000
#>
#> $SCA$gy
#> Tester
#> Line DIL 102 DIL-101 DIL-103
#> DIL 2 0.053000 -2.203000 2.150000
#> DIL 3 3.437167 -7.171333 3.734167
#> DIL 5 -0.957000 1.879500 -0.922500
#> DIL-1 0.198000 3.702000 -3.900000
#> DIL-4 -2.731167 3.792833 -1.061667
#>
#>
#> $CV
#> hsw sh gy
#> 11.439351 2.020348 22.781566
#>
#> $Genetic.Variance.Covariance.
#> Phenotypic Variance Genotypic Variance Environmental Variance
#> hsw -0.6689343 -8.586587 7.917653
#> sh -0.4155230 -3.274175 2.858652
#> gy -101.1095400 -239.042928 137.933388
#> Phenotypic coefficient of Variation Genotypic coefficient of Variation
#> hsw NaN NaN
#> sh NaN NaN
#> gy NaN NaN
#> Environmental coefficient of Variation Broad sense heritability
#> hsw 11.439351 12.836220
#> sh 2.020348 7.879648
#> gy 22.781566 2.364198
#>
#> $Std.Error
#> S.E. gca for line S.E. gca for tester S.E. sca effect S.E. (gi - gj)line
#> hsw 0.8122835 0.6291921 1.4069162 1.1487423
#> sh 0.4880789 0.3780643 0.8453774 0.6902478
#> gy 3.3903464 2.6261511 5.8722523 4.7946739
#> S.E. (gi - gj)tester S.E. (sij - skl)tester
#> hsw 0.8898120 1.989680
#> sh 0.5346636 1.195544
#> gy 3.7139384 8.304619
#>
#> $C.D.
#> C.D. gca for line C.D. gca for tester C.D. sca effect C.D. (gi - gj)line
#> hsw 1.669673 1.2933228 2.891958 2.361274
#> sh 1.003260 0.7771222 1.737698 1.418825
#> gy 6.968957 5.3981308 12.070587 9.855593
#> C.D. (gi - gj)tester C.D. (sij - skl)tester
#> hsw 1.829035 4.089846
#> sh 1.099017 2.457476
#> gy 7.634110 17.070388
#>
#> $Add.Dom.Var
#> Cov H.S. (line) Cov H.S. (tester) Cov H.S. (average) Cov F.S. (average)
#> hsw 0.4601629 -0.1152208 0.03310414 -0.3374874
#> sh -0.2949403 -0.1533743 -0.03843202 -0.1641164
#> gy 10.4131155 -2.3849984 0.76596517 -10.5696184
#> Addittive Variance(F=0) Addittive Variance(F=1) Dominance Variance(F=0)
#> hsw 0.1324166 0.06620828 -1.1670924
#> sh -0.1537281 -0.07686404 0.7216527
#> gy 3.0638607 1.53193033 -32.9941861
#> Dominance Variance(F=1)
#> hsw -0.5835462
#> sh 0.3608263
#> gy -16.4970931
#>
#> $Contribution.of.Line.Tester
#> Lines Tester Line x Tester
#> hsw 46.443110 6.856531 46.70036
#> sh 7.639091 6.182359 86.17855
#> gy 55.793159 3.434970 40.77187# Load the package
library(gpbStat)
#Load the dataset
data("rcbdltcmt")
# View the structure of dataframe.
str(rcbdltc)
#> tibble [60 × 4] (S3: tbl_df/tbl/data.frame)
#> $ replication: num [1:60] 1 2 3 4 1 2 3 4 1 2 ...
#> $ line : num [1:60] 1 1 1 1 1 1 1 1 1 1 ...
#> $ tester : num [1:60] 6 6 6 6 7 7 7 7 8 8 ...
#> $ yield : num [1:60] 74.4 70.9 60.9 68 91.8 ...
# Conduct Line x Tester analysis
result1 = ltcmt(rcbdltcmt, replication, line, tester, rcbdltcmt[,4:5])
# View the output
result1
#> $Mean
#> $Mean$ph
#> Tester
#> Line DIL-101 DIL-102 DIL-103
#> DIL 2 197.75 177.50 177.25
#> DIL 4 202.00 169.80 188.00
#> DIL- 3 183.25 172.00 171.25
#> DIL-1 175.50 197.75 202.00
#> DIL-5 168.40 188.25 184.65
#>
#> $Mean$eh
#> Tester
#> Line DIL-101 DIL-102 DIL-103
#> DIL 2 100.50 90.00 91.500
#> DIL 4 97.25 79.50 95.500
#> DIL- 3 88.00 81.00 80.000
#> DIL-1 87.00 102.25 102.500
#> DIL-5 72.25 71.45 80.675
#>
#>
#> $ANOVA
#> $ANOVA$ph
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 442.4927 147.4976 0.5028866 0.68235896
#> Crosses 14 7885.4240 563.2446 1.9203581 0.05197320
#> Lines 4 1816.0907 454.0227 1.6010303 0.19053280
#> Testers 2 213.1320 106.5660 0.3757861 0.68888394
#> Lines X Testers 8 5856.2013 732.0252 2.5813568 0.02068038
#> Error 42 12318.6773 293.3018 NA NA
#> Total 59 20646.5940 NA NA NA
#>
#> $ANOVA$eh
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 162.4298 54.14328 0.6740871 5.727648e-01
#> Crosses 14 5957.8783 425.56274 5.2982817 1.239227e-05
#> Lines 4 3942.9167 985.72917 12.5449584 6.156545e-07
#> Testers 2 302.4323 151.21617 1.9244642 1.577768e-01
#> Lines X Testers 8 1712.5293 214.06617 2.7243296 1.541154e-02
#> Error 42 3373.4777 80.32090 NA NA
#> Total 59 9493.7858 NA NA NA
#>
#>
#> $GCA.Line
#> ph eh
#> DIL 2 0.4766667 6.041667
#> DIL 4 2.9100000 2.791667
#> DIL- 3 -8.1900000 -4.958333
#> DIL-1 8.0600000 9.291667
#> DIL-5 -3.2566667 -13.166667
#>
#> $GCA.Tester
#> ph eh
#> DIL-101 1.69 1.041667
#> DIL-102 -2.63 -3.118333
#> DIL-103 0.94 2.076667
#>
#> $SCA
#> $SCA$ph
#> Tester
#> Line DIL-101 DIL-102 DIL-103
#> DIL 2 11.89333 -4.036667 -7.856667
#> DIL 4 13.71000 -14.170000 0.460000
#> DIL- 3 6.06000 -0.870000 -5.190000
#> DIL-1 -17.94000 8.630000 9.310000
#> DIL-5 -13.72333 10.446667 3.276667
#>
#> $SCA$eh
#> Tester
#> Line DIL-101 DIL-102 DIL-103
#> DIL 2 5.458333 -0.8816667 -4.576667
#> DIL 4 5.458333 -8.1316667 2.673333
#> DIL- 3 3.958333 1.1183333 -5.076667
#> DIL-1 -11.291667 8.1183333 3.173333
#> DIL-5 -3.583333 -0.2233333 3.806667
#>
#>
#> $CV
#> [1] 9.323348 10.189134
#>
#> $Genetic.Variance.Covariance
#> Phenotypic Variance Genotypic Variance Environmental Variance
#> ph 397.2386 103.93675 293.3018
#> eh 173.1758 92.85487 80.3209
#> Phenotypic coefficient of Variation Genotypic coefficient of Variation
#> ph 10.85026 5.550078
#> eh 14.96120 10.955327
#> Environmental coefficient of Variation Broad sense heritability
#> ph 9.323348 0.2616482
#> eh 10.189134 0.5361886
#>
#> $Std.Error
#> S.E. gca for line S.E. gca for tester S.E. sca effect S.E. (gi - gj)line
#> ph 4.943867 3.829503 8.563029 6.991684
#> eh 2.587162 2.004007 4.481096 3.658800
#> S.E. (gi - gj)tester S.E. (sij - skl)tester
#> ph 5.415735 12.109951
#> eh 2.834094 6.337227
#>
#> $C.D.
#> C.D. gca for line C.D. gca for tester C.D. sca effect C.D. (gi - gj)line
#> ph 9.892655 7.662817 17.134581 13.990327
#> eh 5.176900 4.010009 8.966653 7.321242
#> C.D. (gi - gj)tester C.D. (sij - skl)tester
#> ph 10.836860 24.23196
#> eh 5.671009 12.68076
#>
#> $Add.Dom.Var
#> Cov H.S. (line) Cov H.S. (tester) Cov H.S. (average) Cov F.S. (average)
#> ph -23.16688 -31.27296 -4.475243 37.37585
#> eh 64.30525 -3.14250 5.607864 88.76549
#> Addittive Variance(F=0) Addittive Variance(F=1) Dominance Variance(F=0)
#> ph -17.90097 -8.950486 219.36166
#> eh 22.43145 11.215727 66.87263
#> Dominance Variance(F=1)
#> ph 109.68083
#> eh 33.43632
#>
#> $Contribution.of.Line.Tester
#> Lines Tester Line x Tester
#> ph 23.03098 2.702860 74.26616
#> eh 66.17988 5.076175 28.74395These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.