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ex3 <- read.sym.table(file = 'tsym1.csv', header=TRUE, sep=';',dec='.', row.names=1)
ex3
#> # A tibble: 7 × 7
#> F1 F2 F3 F4 F5 F6 F7
#> <dbl> <symblc_n> <symbl> <dbl> <symblc_> <symblc_n> <symblc_n>
#> 1 2.8 [1.00 : 2.00] <hist> 6 {a,d} [0.00 : 90.00] [9.00 : 24.00]
#> 2 1.4 [3.00 : 9.00] <hist> 8 {b,c,d} [-90.00 : 98.00] [-9.00 : 9.00]
#> 3 3.2 [-1.00 : 4.00] <hist> -7 {a,b} [65.00 : 90.00] [65.00 : 70.00]
#> 4 -2.1 [0.00 : 2.00] <hist> 0 {a,b,c,d} [45.00 : 89.00] [25.00 : 67.00]
#> 5 -3 [-4.00 : -2.00] <hist> -9.5 {b} [20.00 : 40.00] [9.00 : 40.00]
#> 6 0.1 [10.00 : 21.00] <hist> -1 {a,d} [5.00 : 8.00] [5.00 : 8.00]
#> 7 9 [4.00 : 21.00] <hist> 0.5 {a} [3.14 : 6.76] [4.00 : 6.00]
C:Lobo5nYF3e48125f303f.R
##How to save a Symbolic Table in a CSV file with RSDA?
write.sym.table(ex3, file = 'tsymtemp.csv', sep = ';',dec = '.',
row.names = TRUE, col.names = TRUE)
C:Lobo5nYF3e48125f303f.R
data(example3)
example3
#> # A tibble: 7 × 7
#> F1 F2 F3 F4 F5 F6
#> <dbl> <symblc_n> <symblc_m> <dbl> <symblc_> <symblc_n>
#> 1 2.8 [1.00 : 2.00] M1:0.10 M2:0.70 M3:0.20 6 {e,g,i,k} [0.00 : 90.00]
#> 2 1.4 [3.00 : 9.00] M1:0.60 M2:0.30 M3:0.10 8 {a,b,c,d} [-90.00 : 98.00]
#> 3 3.2 [-1.00 : 4.00] M1:0.20 M2:0.20 M3:0.60 -7 {2,b,1,c} [65.00 : 90.00]
#> 4 -2.1 [0.00 : 2.00] M1:0.90 M2:0.00 M3:0.10 0 {a,3,4,c} [45.00 : 89.00]
#> 5 -3 [-4.00 : -2.00] M1:0.60 M2:0.00 M3:0.40 -9.5 {e,g,i,k} [20.00 : 40.00]
#> 6 0.1 [10.00 : 21.00] M1:0.00 M2:0.70 M3:0.30 -1 {e,1,i} [5.00 : 8.00]
#> 7 9 [4.00 : 21.00] M1:0.20 M2:0.20 M3:0.60 0.5 {e,a,2} [3.14 : 6.76]
#> # ℹ 1 more variable: F7 <symblc_n>
C:Lobo5nYF3e48125f303f.R
example3[2,]
#> # A tibble: 1 × 7
#> F1 F2 F3 F4 F5 F6
#> <dbl> <symblc_n> <symblc_m> <dbl> <symblc_s> <symblc_n>
#> 1 1.4 [3.00 : 9.00] M1:0.60 M2:0.30 M3:0.10 8 {a,b,c,d} [-90.00 : 98.00]
#> # ℹ 1 more variable: F7 <symblc_n>
example3[,3]
#> # A tibble: 7 × 1
#> F3
#> <symblc_m>
#> 1 M1:0.10 M2:0.70 M3:0.20
#> 2 M1:0.60 M2:0.30 M3:0.10
#> 3 M1:0.20 M2:0.20 M3:0.60
#> 4 M1:0.90 M2:0.00 M3:0.10
#> 5 M1:0.60 M2:0.00 M3:0.40
#> 6 M1:0.00 M2:0.70 M3:0.30
#> 7 M1:0.20 M2:0.20 M3:0.60
example3[2:3,5]
#> # A tibble: 2 × 1
#> F5
#> <symblc_s>
#> 1 {a,b,c,d}
#> 2 {2,b,1,c}
example3$F1
#> [1] 2.8 1.4 3.2 -2.1 -3.0 0.1 9.0
C:Lobo5nYF3e48125f303f.R
data(ex1_db2so)
ex1_db2so
#> state sex county group age
#> 1 Florida M 2 6 3
#> 2 California F 4 3 4
#> 3 Texas M 12 3 4
#> 4 Florida F 2 3 4
#> 5 Texas M 4 6 4
#> 6 Texas F 2 3 3
#> 7 Florida M 6 3 4
#> 8 Florida F 2 6 4
#> 9 California M 2 3 6
#> 10 California F 21 3 4
#> 11 California M 2 3 4
#> 12 California M 2 6 7
#> 13 Texas F 23 3 4
#> 14 Florida M 2 3 4
#> 15 Florida F 12 7 4
#> 16 Texas M 2 3 8
#> 17 California F 3 7 9
#> 18 California M 2 3 11
#> 19 California M 1 3 11
C:Lobo5nYF3e48125f303f.R
The classic.to.sym
function allows to convert a
traditional table into a symbolic one, to this we must indicate the
following parameters.
x
= a data.frameconcept
= variables to be used as a conceptvariables
= variables to be used, conceptible with
tidyselect optionsdefault.numeric
= function that will be used by default
for numerical values (sym.interval)default.categorical
= functions to be used by default
for categorical values (sym.model)result <- classic.to.sym(x = ex1_db2so,
concept = c(state, sex),
variables = c(county, group, age))
result
#> # A tibble: 6 × 3
#> county group age
#> <symblc_n> <symblc_n> <symblc_n>
#> 1 [3.00 : 21.00] [3.00 : 7.00] [4.00 : 9.00]
#> 2 [1.00 : 2.00] [3.00 : 6.00] [4.00 : 11.00]
#> 3 [2.00 : 12.00] [3.00 : 7.00] [4.00 : 4.00]
#> 4 [2.00 : 6.00] [3.00 : 6.00] [3.00 : 4.00]
#> 5 [2.00 : 23.00] [3.00 : 3.00] [3.00 : 4.00]
#> 6 [2.00 : 12.00] [3.00 : 6.00] [4.00 : 8.00]
C:Lobo5nYF3e48125f303f.R
We can add new variables indicating the type we want them to be.
result <- classic.to.sym(x = ex1_db2so,
concept = c("state", "sex"),
variables = c(county, group, age),
age_hist = sym.histogram(age, breaks = pretty(ex1_db2so$age, 5)))
result
#> # A tibble: 6 × 4
#> age_hist county group age
#> <symblc_h> <symblc_n> <symblc_n> <symblc_n>
#> 1 <hist> [3.00 : 21.00] [3.00 : 7.00] [4.00 : 9.00]
#> 2 <hist> [1.00 : 2.00] [3.00 : 6.00] [4.00 : 11.00]
#> 3 <hist> [2.00 : 12.00] [3.00 : 7.00] [4.00 : 4.00]
#> 4 <hist> [2.00 : 6.00] [3.00 : 6.00] [3.00 : 4.00]
#> 5 <hist> [2.00 : 23.00] [3.00 : 3.00] [3.00 : 4.00]
#> 6 <hist> [2.00 : 12.00] [3.00 : 6.00] [4.00 : 8.00]
C:Lobo5nYF3e48125f303f.R
data(USCrime)
head(USCrime)
#> state fold population householdsize racepctblack racePctWhite racePctAsian
#> 1 8 1 0.19 0.33 0.02 0.90 0.12
#> 2 53 1 0.00 0.16 0.12 0.74 0.45
#> 3 24 1 0.00 0.42 0.49 0.56 0.17
#> 4 34 1 0.04 0.77 1.00 0.08 0.12
#> 5 42 1 0.01 0.55 0.02 0.95 0.09
#> 6 6 1 0.02 0.28 0.06 0.54 1.00
#> racePctHisp agePct12t21 agePct12t29 agePct16t24 agePct65up numbUrban pctUrban
#> 1 0.17 0.34 0.47 0.29 0.32 0.20 1.0
#> 2 0.07 0.26 0.59 0.35 0.27 0.02 1.0
#> 3 0.04 0.39 0.47 0.28 0.32 0.00 0.0
#> 4 0.10 0.51 0.50 0.34 0.21 0.06 1.0
#> 5 0.05 0.38 0.38 0.23 0.36 0.02 0.9
#> 6 0.25 0.31 0.48 0.27 0.37 0.04 1.0
#> medIncome pctWWage pctWFarmSelf pctWInvInc pctWSocSec pctWPubAsst pctWRetire
#> 1 0.37 0.72 0.34 0.60 0.29 0.15 0.43
#> 2 0.31 0.72 0.11 0.45 0.25 0.29 0.39
#> 3 0.30 0.58 0.19 0.39 0.38 0.40 0.84
#> 4 0.58 0.89 0.21 0.43 0.36 0.20 0.82
#> 5 0.50 0.72 0.16 0.68 0.44 0.11 0.71
#> 6 0.52 0.68 0.20 0.61 0.28 0.15 0.25
#> medFamInc perCapInc whitePerCap blackPerCap indianPerCap AsianPerCap
#> 1 0.39 0.40 0.39 0.32 0.27 0.27
#> 2 0.29 0.37 0.38 0.33 0.16 0.30
#> 3 0.28 0.27 0.29 0.27 0.07 0.29
#> 4 0.51 0.36 0.40 0.39 0.16 0.25
#> 5 0.46 0.43 0.41 0.28 0.00 0.74
#> 6 0.62 0.72 0.76 0.77 0.28 0.52
#> OtherPerCap HispPerCap NumUnderPov PctPopUnderPov PctLess9thGrade
#> 1 0.36 0.41 0.08 0.19 0.10
#> 2 0.22 0.35 0.01 0.24 0.14
#> 3 0.28 0.39 0.01 0.27 0.27
#> 4 0.36 0.44 0.01 0.10 0.09
#> 5 0.51 0.48 0.00 0.06 0.25
#> 6 0.48 0.60 0.01 0.12 0.13
#> PctNotHSGrad PctBSorMore PctUnemployed PctEmploy PctEmplManu PctEmplProfServ
#> 1 0.18 0.48 0.27 0.68 0.23 0.41
#> 2 0.24 0.30 0.27 0.73 0.57 0.15
#> 3 0.43 0.19 0.36 0.58 0.32 0.29
#> 4 0.25 0.31 0.33 0.71 0.36 0.45
#> 5 0.30 0.33 0.12 0.65 0.67 0.38
#> 6 0.12 0.80 0.10 0.65 0.19 0.77
#> PctOccupManu PctOccupMgmtProf MalePctDivorce MalePctNevMarr FemalePctDiv
#> 1 0.25 0.52 0.68 0.40 0.75
#> 2 0.42 0.36 1.00 0.63 0.91
#> 3 0.49 0.32 0.63 0.41 0.71
#> 4 0.37 0.39 0.34 0.45 0.49
#> 5 0.42 0.46 0.22 0.27 0.20
#> 6 0.06 0.91 0.49 0.57 0.61
#> TotalPctDiv PersPerFam PctFam2Par PctKids2Par PctYoungKids2Par PctTeen2Par
#> 1 0.75 0.35 0.55 0.59 0.61 0.56
#> 2 1.00 0.29 0.43 0.47 0.60 0.39
#> 3 0.70 0.45 0.42 0.44 0.43 0.43
#> 4 0.44 0.75 0.65 0.54 0.83 0.65
#> 5 0.21 0.51 0.91 0.91 0.89 0.85
#> 6 0.58 0.44 0.62 0.69 0.87 0.53
#> PctWorkMomYoungKids PctWorkMom NumIlleg PctIlleg NumImmig PctImmigRecent
#> 1 0.74 0.76 0.04 0.14 0.03 0.24
#> 2 0.46 0.53 0.00 0.24 0.01 0.52
#> 3 0.71 0.67 0.01 0.46 0.00 0.07
#> 4 0.85 0.86 0.03 0.33 0.02 0.11
#> 5 0.40 0.60 0.00 0.06 0.00 0.03
#> 6 0.30 0.43 0.00 0.11 0.04 0.30
#> PctImmigRec5 PctImmigRec8 PctImmigRec10 PctRecentImmig PctRecImmig5
#> 1 0.27 0.37 0.39 0.07 0.07
#> 2 0.62 0.64 0.63 0.25 0.27
#> 3 0.06 0.15 0.19 0.02 0.02
#> 4 0.20 0.30 0.31 0.05 0.08
#> 5 0.07 0.20 0.27 0.01 0.02
#> 6 0.35 0.43 0.47 0.50 0.50
#> PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
#> 1 0.08 0.08 0.89 0.06
#> 2 0.25 0.23 0.84 0.10
#> 3 0.04 0.05 0.88 0.04
#> 4 0.11 0.11 0.81 0.08
#> 5 0.04 0.05 0.88 0.05
#> 6 0.56 0.57 0.45 0.28
#> PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
#> 1 0.14 0.13 0.33 0.39
#> 2 0.16 0.10 0.17 0.29
#> 3 0.20 0.20 0.46 0.52
#> 4 0.56 0.62 0.85 0.77
#> 5 0.16 0.19 0.59 0.60
#> 6 0.25 0.19 0.29 0.53
#> PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR MedNumBR
#> 1 0.28 0.55 0.09 0.51 0.5
#> 2 0.17 0.26 0.20 0.82 0.0
#> 3 0.43 0.42 0.15 0.51 0.5
#> 4 1.00 0.94 0.12 0.01 0.5
#> 5 0.37 0.89 0.02 0.19 0.5
#> 6 0.18 0.39 0.26 0.73 0.0
#> HousVacant PctHousOccup PctHousOwnOcc PctVacantBoarded PctVacMore6Mos
#> 1 0.21 0.71 0.52 0.05 0.26
#> 2 0.02 0.79 0.24 0.02 0.25
#> 3 0.01 0.86 0.41 0.29 0.30
#> 4 0.01 0.97 0.96 0.60 0.47
#> 5 0.01 0.89 0.87 0.04 0.55
#> 6 0.02 0.84 0.30 0.16 0.28
#> MedYrHousBuilt PctHousNoPhone PctWOFullPlumb OwnOccLowQuart OwnOccMedVal
#> 1 0.65 0.14 0.06 0.22 0.19
#> 2 0.65 0.16 0.00 0.21 0.20
#> 3 0.52 0.47 0.45 0.18 0.17
#> 4 0.52 0.11 0.11 0.24 0.21
#> 5 0.73 0.05 0.14 0.31 0.31
#> 6 0.25 0.02 0.05 0.94 1.00
#> OwnOccHiQuart RentLowQ RentMedian RentHighQ MedRent MedRentPctHousInc
#> 1 0.18 0.36 0.35 0.38 0.34 0.38
#> 2 0.21 0.42 0.38 0.40 0.37 0.29
#> 3 0.16 0.27 0.29 0.27 0.31 0.48
#> 4 0.19 0.75 0.70 0.77 0.89 0.63
#> 5 0.30 0.40 0.36 0.38 0.38 0.22
#> 6 1.00 0.67 0.63 0.68 0.62 0.47
#> MedOwnCostPctInc MedOwnCostPctIncNoMtg NumInShelters NumStreet PctForeignBorn
#> 1 0.46 0.25 0.04 0 0.12
#> 2 0.32 0.18 0.00 0 0.21
#> 3 0.39 0.28 0.00 0 0.14
#> 4 0.51 0.47 0.00 0 0.19
#> 5 0.51 0.21 0.00 0 0.11
#> 6 0.59 0.11 0.00 0 0.70
#> PctBornSameState PctSameHouse85 PctSameCity85 PctSameState85 LandArea PopDens
#> 1 0.42 0.50 0.51 0.64 0.12 0.26
#> 2 0.50 0.34 0.60 0.52 0.02 0.12
#> 3 0.49 0.54 0.67 0.56 0.01 0.21
#> 4 0.30 0.73 0.64 0.65 0.02 0.39
#> 5 0.72 0.64 0.61 0.53 0.04 0.09
#> 6 0.42 0.49 0.73 0.64 0.01 0.58
#> PctUsePubTrans LemasPctOfficDrugUn ViolentCrimesPerPop
#> 1 0.20 0.32 0.20
#> 2 0.45 0.00 0.67
#> 3 0.02 0.00 0.43
#> 4 0.28 0.00 0.12
#> 5 0.02 0.00 0.03
#> 6 0.10 0.00 0.14
C:Lobo5nYF3e48125f303f.R
result <- classic.to.sym(x = USCrime,
concept = state,
variables= c(NumInShelters,
NumImmig,
ViolentCrimesPerPop),
ViolentCrimesPerPop_hist = sym.histogram(ViolentCrimesPerPop,
breaks = pretty(USCrime$ViolentCrimesPerPop,5)))
result
#> # A tibble: 46 × 4
#> ViolentCrimesPerPop_hist NumInShelters NumImmig ViolentCrimesPerPop
#> <symblc_h> <symblc_n> <symblc_n> <symblc_n>
#> 1 <hist> [0.00 : 0.32] [0.00 : 0.04] [0.01 : 1.00]
#> 2 <hist> [0.01 : 0.18] [0.01 : 0.09] [0.05 : 0.36]
#> 3 <hist> [0.00 : 1.00] [0.00 : 0.57] [0.05 : 0.57]
#> 4 <hist> [0.00 : 0.08] [0.00 : 0.02] [0.02 : 1.00]
#> 5 <hist> [0.00 : 1.00] [0.00 : 1.00] [0.01 : 1.00]
#> 6 <hist> [0.00 : 0.68] [0.00 : 0.23] [0.07 : 0.75]
#> 7 <hist> [0.00 : 0.79] [0.00 : 0.14] [0.00 : 0.94]
#> 8 <hist> [0.01 : 0.01] [0.01 : 0.01] [0.37 : 0.37]
#> 9 <hist> [1.00 : 1.00] [0.39 : 0.39] [1.00 : 1.00]
#> 10 <hist> [0.00 : 0.52] [0.00 : 1.00] [0.06 : 1.00]
#> # ℹ 36 more rows
C:Lobo5nYF3e48125f303f.R
data("ex_mcfa1")
head(ex_mcfa1)
#> suspect age hair eyes region
#> 1 1 42 h_red e_brown Bronx
#> 2 2 20 h_black e_green Bronx
#> 3 3 64 h_brown e_brown Brooklyn
#> 4 4 55 h_blonde e_brown Bronx
#> 5 5 4 h_brown e_green Manhattan
#> 6 6 61 h_blonde e_green Bronx
C:Lobo5nYF3e48125f303f.R
sym.table <- classic.to.sym(x = ex_mcfa1,
concept = suspect,
variables=c(hair,
eyes,
region),
default.categorical = sym.set)
sym.table
#> # A tibble: 100 × 3
#> hair eyes region
#> <symblc_s> <symblc_s> <symblc_s>
#> 1 {h_red} {e_brown,e_black} {Bronx}
#> 2 {h_black,h_blonde} {e_green,e_black} {Bronx,Manhattan}
#> 3 {h_brown,h_white} {e_brown,e_green} {Brooklyn,Queens}
#> 4 {h_blonde} {e_brown,e_black} {Bronx,Manhattan}
#> 5 {h_brown,h_red} {e_green} {Manhattan,Bronx}
#> 6 {h_blonde,h_white} {e_green,e_blue} {Bronx,Queens}
#> 7 {h_white,h_red} {e_black,e_blue} {Queens,Bronx}
#> 8 {h_blonde,h_white} {e_brown,e_black} {Manhattan,Brooklyn}
#> 9 {h_blonde,h_white} {e_black,e_brown} {Brooklyn,Bronx}
#> 10 {h_brown,h_black} {e_brown,e_green} {Manhattan,Bronx}
#> # ℹ 90 more rows
C:Lobo5nYF3e48125f303f.R
We can modify the function that will be applied by default to the categorical variables
sym.table <- classic.to.sym(x = ex_mcfa1,
concept = suspect,
default.categorical = sym.set)
sym.table
#> # A tibble: 100 × 4
#> age hair eyes region
#> <symblc_n> <symblc_s> <symblc_s> <symblc_s>
#> 1 [22.00 : 42.00] {h_red} {e_brown,e_black} {Bronx}
#> 2 [20.00 : 57.00] {h_black,h_blonde} {e_green,e_black} {Bronx,Manhattan}
#> 3 [29.00 : 64.00] {h_brown,h_white} {e_brown,e_green} {Brooklyn,Queens}
#> 4 [14.00 : 55.00] {h_blonde} {e_brown,e_black} {Bronx,Manhattan}
#> 5 [4.00 : 47.00] {h_brown,h_red} {e_green} {Manhattan,Bronx}
#> 6 [32.00 : 61.00] {h_blonde,h_white} {e_green,e_blue} {Bronx,Queens}
#> 7 [49.00 : 61.00] {h_white,h_red} {e_black,e_blue} {Queens,Bronx}
#> 8 [8.00 : 32.00] {h_blonde,h_white} {e_brown,e_black} {Manhattan,Brooklyn}
#> 9 [39.00 : 67.00] {h_blonde,h_white} {e_black,e_brown} {Brooklyn,Bronx}
#> 10 [50.00 : 68.00] {h_brown,h_black} {e_brown,e_green} {Manhattan,Bronx}
#> # ℹ 90 more rows
C:Lobo5nYF3e48125f303f.R
hani3101 <- SDS.to.RSDA(file.path = "hani3101.sds")
#> Preprocessing file
#> Converting data to JSON format
#> Processing variable 1: R3101
#> Processing variable 2: RNINO12
#> Processing variable 3: RNINO3
#> Processing variable 4: RNINO4
#> Processing variable 5: RNINO34
#> Processing variable 6: RSOI
hani3101
#> # A tibble: 32 × 6
#> R3101 RNINO12
#> <symblc_m> <symblc_m>
#> 1 X2:0.21 X4:0.18 X3:0.15 X5:... X1:0.17 X2:0.83 X3:0.00
#> 2 X2:0.30 X4:0.14 X3:0.19 X5:... X1:0.00 X2:0.25 X3:0.75
#> 3 X2:0.16 X4:0.12 X3:0.20 X5:... X1:0.67 X2:0.33 X3:0.00
#> 4 X2:0.13 X4:0.15 X3:0.22 X5:... X1:0.17 X2:0.83 X3:0.00
#> 5 X2:0.14 X4:0.14 X3:0.18 X5:... X1:0.42 X2:0.58 X3:0.00
#> 6 X2:0.26 X4:0.06 X3:0.23 X5:... X1:0.00 X2:0.67 X3:0.33
#> 7 X2:0.28 X4:0.14 X3:0.10 X5:... X1:0.00 X2:1.00 X3:0.00
#> 8 X2:0.25 X4:0.15 X3:0.19 X5:... X1:0.00 X2:1.00 X3:0.00
#> 9 X2:0.20 X4:0.15 X3:0.19 X5:... X1:0.00 X2:1.00 X3:0.00
#> 10 X2:0.21 X4:0.16 X3:0.31 X5:... X1:0.08 X2:0.92 X3:0.00
#> # ℹ 22 more rows
#> # ℹ 4 more variables: RNINO3 <symblc_m>, RNINO4 <symblc_m>, RNINO34 <symblc_m>,
#> # RSOI <symblc_m>
C:Lobo5nYF3e48125f303f.R
# We can save the file in CSV to RSDA format as follows:
write.sym.table(hani3101,
file='hani3101.csv',
sep=';',
dec='.',
row.names=TRUE,
col.names=TRUE)
C:Lobo5nYF3e48125f303f.R
abalone <- SODAS.to.RSDA("abalone.xml")
#> Processing variable 1: LENGTH
#> Processing variable 2: DIAMETER
#> Processing variable 3: HEIGHT
#> Processing variable 4: WHOLE_WEIGHT
#> Processing variable 5: SHUCKED_WEIGHT
#> Processing variable 6: VISCERA_WEIGHT
#> Processing variable 7: SHELL_WEIGHT
abalone
#> # A tibble: 24 × 7
#> LENGTH DIAMETER HEIGHT WHOLE_WEIGHT SHUCKED_WEIGHT
#> <symblc_n> <symblc_n> <symblc_n> <symblc_n> <symblc_n>
#> 1 [0.28 : 0.66] [0.20 : 0.48] [0.07 : 0.18] [0.08 : 1.37] [0.03 : 0.64]
#> 2 [0.30 : 0.74] [0.22 : 0.58] [0.02 : 1.13] [0.15 : 2.25] [0.06 : 1.16]
#> 3 [0.34 : 0.78] [0.26 : 0.63] [0.06 : 0.23] [0.20 : 2.66] [0.07 : 1.49]
#> 4 [0.39 : 0.82] [0.30 : 0.65] [0.10 : 0.25] [0.26 : 2.51] [0.11 : 1.23]
#> 5 [0.40 : 0.74] [0.32 : 0.60] [0.10 : 0.24] [0.35 : 2.20] [0.12 : 0.84]
#> 6 [0.45 : 0.80] [0.38 : 0.63] [0.14 : 0.22] [0.64 : 2.53] [0.16 : 0.93]
#> 7 [0.49 : 0.72] [0.36 : 0.58] [0.12 : 0.21] [0.68 : 2.12] [0.16 : 0.82]
#> 8 [0.55 : 0.70] [0.46 : 0.58] [0.18 : 0.22] [1.21 : 1.81] [0.32 : 0.71]
#> 9 [0.08 : 0.24] [0.06 : 0.18] [0.01 : 0.06] [0.00 : 0.07] [0.00 : 0.03]
#> 10 [0.13 : 0.58] [0.10 : 0.45] [0.00 : 0.15] [0.01 : 0.89] [0.00 : 0.50]
#> # ℹ 14 more rows
#> # ℹ 2 more variables: VISCERA_WEIGHT <symblc_n>, SHELL_WEIGHT <symblc_n>
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
mean(example3$F2,method = "interval")
#> <symbolic_interval[1]>
#> [1] [1.86 : 8.14]
mean(example3[,2],method = "interval")
#> <symbolic_interval[1]>
#> [1] [1.86 : 8.14]
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
median(example3$F6, method = 'interval')
#> <symbolic_interval[1]>
#> [1] [5.00 : 89.00]
median(example3[,6], method = 'interval')
#> <symbolic_interval[1]>
#> [1] [5.00 : 89.00]
C:Lobo5nYF3e48125f303f.R
var(example3[,1])
#> [1] 15.98238
var(example3[,2])
#> [1] 90.66667
var(example3$F6)
#> [1] 1872.358
var(example3$F6, method = 'interval')
#> <symbolic_interval[1]>
#> [1] [2,408.97 : 1,670.51]
var(example3$F6, method = 'billard')
#> [1] 1355.143
sd(example3$F1)
#> [1] 3.997797
sd(example3$F2)
#> [1] 6.733003
sd(example3$F6)
#> [1] 30.59704
sd(example3$F6, method = 'interval')
#> <symbolic_interval[1]>
#> [1] [49.08 : 40.87]
sd(example3$F6, method = 'billard')
#> [1] 36.81226
C:Lobo5nYF3e48125f303f.R
cor(example3$F1, example3$F4)
#> [1] 0.2864553
cor(example3[,1], example3[,4])
#> [,1]
#> [1,] 0.2864553
cor(example3$F2, example3$F6, method = 'centers')
#> [1] -0.6693648
cor(example3$F2, example3$F6, method = 'billard')
#> [1] -0.6020041
C:Lobo5nYF3e48125f303f.R
library(ggpolypath)
#> Loading required package: ggplot2
data(oils)
oils <- RSDA:::to.v3(RSDA:::to.v2(oils))
sym.radar.plot(oils[2:3,])
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0, label = round(min(real.value), : All aesthetics have length 1, but the data has 20 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0.25, label = inverse.rescale(0.25, : All aesthetics have length 1, but the data has 20 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0.5, label = inverse.rescale(0.5, : All aesthetics have length 1, but the data has 20 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0.75, label = inverse.rescale(0.75, : All aesthetics have length 1, but the data has 20 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 1, label = round(max(real.value), : All aesthetics have length 1, but the data has 20 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
#> font family not found in Windows font database
sym.radar.plot(oils[2:5,])
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
#> the caller; using TRUE
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0, label = round(min(real.value), : All aesthetics have length 1, but the data has 40 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0.25, label = inverse.rescale(0.25, : All aesthetics have length 1, but the data has 40 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0.5, label = inverse.rescale(0.5, : All aesthetics have length 1, but the data has 40 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 0.75, label = inverse.rescale(0.75, : All aesthetics have length 1, but the data has 40 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in ggplot2::geom_text(ggplot2::aes(x = 0.5, y = 1, label = round(max(real.value), : All aesthetics have length 1, but the data has 40 rows.
#> ℹ Please consider using `annotate()` or provide this layer with data containing
#> a single row.
#> Warning in grid.Call.graphics(C_text, as.graphicsAnnot(x$label), x$x, x$y, :
#> font family not found in Windows font database
res
#> $frequency
#> [1] 25 49 1 25
#>
#> $histogram
#> [,1]
#> [1,] 0.7
#> [2,] 1.9
#> [3,] 3.1
#> [4,] 4.3
res <- interval.histogram.plot(oils[,3],
n.bins = 3,
main = "Histogram",
col = c(2, 3, 4))
C:Lobo5nYF3e48125f303f.R
data("oils")
DM <- sym.dist.interval(sym.data = oils[,1:4],
method = "Gowda.Diday")
model <- hclust(DM)
plot(model, hang = -1)
C:Lobo5nYF3e48125f303f.R
data(int_prost_train)
data(int_prost_test)
res.cm <- sym.lm(formula = lpsa~., sym.data = int_prost_train, method = 'cm')
res.cm
#>
#> Call:
#> stats::lm(formula = formula, data = centers)
#>
#> Coefficients:
#> (Intercept) lcavol lweight age lbph svi
#> 0.411537 0.579327 0.614128 -0.018659 0.143918 0.730937
#> lcp gleason pgg45
#> -0.205536 -0.030924 0.009507
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
RMSE.L(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.7229999
RMSE.U(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.7192467
R2.L(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.501419
R2.U(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.5058389
deter.coefficient(int_prost_test$lpsa, pred.cm$Fitted)
#> [1] 0.4962964
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
res.cm.lasso <- sym.glm(sym.data = int_prost_train,
response = 9,
method = 'cm',
alpha = 1,
nfolds = 10,
grouped = TRUE)
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
RMSE.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.7132806
RMSE.U(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.7097654
R2.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.514355
R2.U(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.5185425
deter.coefficient(int_prost_test$lpsa, pred.cm.lasso)
#> [1] 0.4972531
C:Lobo5nYF3e48125f303f.R
data(int_prost_train)
data(int_prost_test)
res.cm.ridge <- sym.glm(sym.data = int_prost_train,
response = 9,
method = 'cm',
alpha = 0,
nfolds = 10,
grouped = TRUE)
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
RMSE.L(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.703543
RMSE.U(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.7004145
R2.L(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.5286114
R2.U(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.5322683
deter.coefficient(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.4808652
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
data("ex_mcfa1")
ex_mcfa1
#> suspect age hair eyes region
#> 1 1 42 h_red e_brown Bronx
#> 2 2 20 h_black e_green Bronx
#> 3 3 64 h_brown e_brown Brooklyn
#> 4 4 55 h_blonde e_brown Bronx
#> 5 5 4 h_brown e_green Manhattan
#> 6 6 61 h_blonde e_green Bronx
#> 7 7 61 h_white e_black Queens
#> 8 8 32 h_blonde e_brown Manhattan
#> 9 9 39 h_blonde e_black Brooklyn
#> 10 10 50 h_brown e_brown Manhattan
#> 11 11 41 h_red e_blue Manhattan
#> 12 12 35 h_blonde e_green Brooklyn
#> 13 13 56 h_blonde e_brown Bronx
#> 14 14 52 h_red e_brown Queens
#> 15 15 55 h_red e_green Brooklyn
#> 16 16 25 h_brown e_brown Queens
#> 17 17 52 h_blonde e_brown Brooklyn
#> 18 18 28 h_red e_brown Manhattan
#> 19 19 21 h_white e_blue Manhattan
#> 20 20 66 h_black e_black Brooklyn
#> 21 21 67 h_blonde e_brown Queens
#> 22 22 13 h_white e_blue Brooklyn
#> 23 23 39 h_brown e_green Manhattan
#> 24 24 47 h_black e_green Brooklyn
#> 25 25 54 h_blonde e_brown Bronx
#> 26 26 75 h_brown e_blue Brooklyn
#> 27 27 3 h_white e_green Manhattan
#> 28 28 40 h_white e_green Manhattan
#> 29 29 58 h_red e_blue Queens
#> 30 30 41 h_brown e_green Bronx
#> 31 31 25 h_white e_black Brooklyn
#> 32 32 75 h_blonde e_blue Manhattan
#> 33 33 58 h_white e_brown Bronx
#> 34 34 61 h_white e_brown Manhattan
#> 35 35 52 h_white e_blue Bronx
#> 36 36 19 h_red e_black Queens
#> 37 37 58 h_red e_black Bronx
#> 38 38 46 h_black e_green Manhattan
#> 39 39 74 h_brown e_black Manhattan
#> 40 40 26 h_blonde e_brown Brooklyn
#> 41 41 63 h_blonde e_blue Queens
#> 42 42 40 h_brown e_black Queens
#> 43 43 65 h_black e_brown Brooklyn
#> 44 44 51 h_blonde e_brown Brooklyn
#> 45 45 15 h_white e_black Brooklyn
#> 46 46 32 h_blonde e_brown Bronx
#> 47 47 68 h_white e_black Manhattan
#> 48 48 51 h_white e_black Queens
#> 49 49 14 h_red e_green Queens
#> 50 50 72 h_white e_brown Brooklyn
#> 51 51 7 h_red e_blue Brooklyn
#> 52 52 22 h_red e_brown Bronx
#> 53 53 52 h_red e_brown Brooklyn
#> 54 54 62 h_brown e_green Bronx
#> 55 55 41 h_black e_brown Queens
#> 56 56 32 h_black e_black Manhattan
#> 57 57 58 h_brown e_brown Queens
#> 58 58 25 h_black e_brown Queens
#> 59 59 70 h_blonde e_green Brooklyn
#> 60 60 64 h_brown e_blue Queens
#> 61 61 25 h_white e_blue Bronx
#> 62 62 42 h_black e_black Brooklyn
#> 63 63 56 h_red e_black Brooklyn
#> 64 64 41 h_blonde e_black Brooklyn
#> 65 65 8 h_white e_black Manhattan
#> 66 66 7 h_black e_green Brooklyn
#> 67 67 42 h_white e_brown Queens
#> 68 68 10 h_white e_blue Manhattan
#> 69 69 60 h_brown e_black Bronx
#> 70 70 52 h_blonde e_brown Brooklyn
#> 71 71 39 h_brown e_blue Manhattan
#> 72 72 69 h_brown e_green Queens
#> 73 73 67 h_blonde e_green Manhattan
#> 74 74 46 h_red e_black Brooklyn
#> 75 75 72 h_black e_black Queens
#> 76 76 66 h_red e_blue Queens
#> 77 77 4 h_black e_blue Manhattan
#> 78 78 62 h_black e_green Brooklyn
#> 79 79 10 h_blonde e_blue Bronx
#> 80 80 16 h_blonde e_black Manhattan
#> 81 81 59 h_blonde e_brown Bronx
#> 82 82 63 h_blonde e_blue Manhattan
#> 83 83 54 h_red e_blue Queens
#> 84 84 14 h_brown e_blue Brooklyn
#> 85 85 48 h_black e_green Manhattan
#> 86 86 59 h_blonde e_black Bronx
#> 87 87 73 h_blonde e_black Bronx
#> 88 88 51 h_brown e_brown Bronx
#> 89 89 14 h_white e_black Bronx
#> 90 90 58 h_blonde e_black Queens
#> 91 91 56 h_red e_green Manhattan
#> 92 92 26 h_red e_blue Brooklyn
#> 93 93 59 h_brown e_black Manhattan
#> 94 94 27 h_white e_green Manhattan
#> 95 95 38 h_black e_green Manhattan
#> 96 96 5 h_blonde e_green Bronx
#> 97 97 14 h_black e_blue Queens
#> 98 98 13 h_black e_brown Manhattan
#> 99 99 54 h_white e_blue Brooklyn
#> 100 100 66 h_white e_green Manhattan
#> 101 1 22 h_red e_black Bronx
#> 102 2 57 h_blonde e_black Manhattan
#> 103 3 29 h_white e_green Queens
#> 104 4 14 h_blonde e_black Manhattan
#> 105 5 47 h_red e_green Bronx
#> 106 6 32 h_white e_blue Queens
#> 107 7 49 h_red e_blue Bronx
#> 108 8 8 h_white e_black Brooklyn
#> 109 9 67 h_white e_brown Bronx
#> 110 10 68 h_black e_green Bronx
#> 111 11 15 h_black e_brown Manhattan
#> 112 12 46 h_white e_brown Bronx
#> 113 13 68 h_white e_black Manhattan
#> 114 14 55 h_blonde e_blue Manhattan
#> 115 15 7 h_white e_green Bronx
#> 116 16 10 h_black e_brown Brooklyn
#> 117 17 49 h_red e_blue Manhattan
#> 118 18 12 h_brown e_blue Brooklyn
#> 119 19 41 h_white e_blue Bronx
#> 120 20 10 h_brown e_blue Bronx
#> 121 21 12 h_white e_green Manhattan
#> 122 22 53 h_white e_blue Manhattan
#> 123 23 5 h_black e_black Manhattan
#> 124 24 46 h_brown e_black Queens
#> 125 25 14 h_brown e_black Queens
#> 126 26 55 h_white e_green Brooklyn
#> 127 27 53 h_red e_brown Manhattan
#> 128 28 31 h_black e_brown Manhattan
#> 129 29 31 h_blonde e_brown Queens
#> 130 30 55 h_brown e_black Brooklyn
C:Lobo5nYF3e48125f303f.R
sym.table <- classic.to.sym(x = ex_mcfa1,
concept = suspect,
default.categorical = sym.set)
sym.table
#> # A tibble: 100 × 4
#> age hair eyes region
#> <symblc_n> <symblc_s> <symblc_s> <symblc_s>
#> 1 [22.00 : 42.00] {h_red} {e_brown,e_black} {Bronx}
#> 2 [20.00 : 57.00] {h_black,h_blonde} {e_green,e_black} {Bronx,Manhattan}
#> 3 [29.00 : 64.00] {h_brown,h_white} {e_brown,e_green} {Brooklyn,Queens}
#> 4 [14.00 : 55.00] {h_blonde} {e_brown,e_black} {Bronx,Manhattan}
#> 5 [4.00 : 47.00] {h_brown,h_red} {e_green} {Manhattan,Bronx}
#> 6 [32.00 : 61.00] {h_blonde,h_white} {e_green,e_blue} {Bronx,Queens}
#> 7 [49.00 : 61.00] {h_white,h_red} {e_black,e_blue} {Queens,Bronx}
#> 8 [8.00 : 32.00] {h_blonde,h_white} {e_brown,e_black} {Manhattan,Brooklyn}
#> 9 [39.00 : 67.00] {h_blonde,h_white} {e_black,e_brown} {Brooklyn,Bronx}
#> 10 [50.00 : 68.00] {h_brown,h_black} {e_brown,e_green} {Manhattan,Bronx}
#> # ℹ 90 more rows
C:Lobo5nYF3e48125f303f.R
res <- sym.mcfa(sym.table, c(2,3))
mcfa.scatterplot(res[,2], res[,3], sym.data = sym.table, pos.var = c(2,3))
C:Lobo5nYF3e48125f303f.R
res <- sym.mcfa(sym.table, c(2,3,4))
mcfa.scatterplot(res[,2], res[,3], sym.data = sym.table, pos.var = c(2,3,4))
C:Lobo5nYF3e48125f303f.R
datos <- oils
datos
#> # A tibble: 8 × 4
#> GRA FRE IOD SAP
#> * <symblc_n> <symblc_n> <symblc_n> <symblc_n>
#> 1 [0.93 : 0.94] [-27.00 : -18.00] [170.00 : 204.00] [118.00 : 196.00]
#> 2 [0.93 : 0.94] [-5.00 : -4.00] [192.00 : 208.00] [188.00 : 197.00]
#> 3 [0.92 : 0.92] [-6.00 : -1.00] [99.00 : 113.00] [189.00 : 198.00]
#> 4 [0.92 : 0.93] [-6.00 : -4.00] [104.00 : 116.00] [187.00 : 193.00]
#> 5 [0.92 : 0.92] [-25.00 : -15.00] [80.00 : 82.00] [189.00 : 193.00]
#> 6 [0.91 : 0.92] [0.00 : 6.00] [79.00 : 90.00] [187.00 : 196.00]
#> 7 [0.86 : 0.87] [30.00 : 38.00] [40.00 : 48.00] [190.00 : 199.00]
#> 8 [0.86 : 0.86] [22.00 : 32.00] [53.00 : 77.00] [190.00 : 202.00]
C:Lobo5nYF3e48125f303f.R
x <- sym.umap(datos)
x
#> V1 V2 V3 V4
#> 1 -9.223207503 1.335672 0.68537020 -8.34256261
#> 2 -9.259582855 1.352077 0.68664128 -8.36391335
#> 3 -9.256634776 1.558130 0.89718409 -8.56747945
#> 4 -9.144158031 1.289749 0.64612458 -8.29514532
#> 5 -9.417350406 1.429899 0.74930884 -8.44568910
#> 6 -9.044848400 1.261329 0.75089198 -8.23580612
#> 7 -9.167593046 1.530339 0.88945893 -8.53498644
#> 8 -9.183525748 1.398884 0.75540844 -8.40492902
#> 9 -0.523374876 1.549802 -1.20353051 -2.69362539
#> 10 -0.458349188 1.632917 -1.24250506 -2.74869948
#> 11 -0.423227869 1.635512 -1.18344527 -2.80641997
#> 12 -0.568316836 1.410449 -1.35809052 -2.59095106
#> 13 -0.448757010 1.518036 -1.34886803 -2.81856565
#> 14 -0.513563226 1.609737 -1.29644646 -2.72082652
#> 15 -0.420699791 1.768993 -1.28703249 -2.88406883
#> 16 -0.218652342 1.879808 -1.25025195 -2.84533327
#> 17 -0.045367645 2.382403 -0.24363177 -3.17967958
#> 18 -0.160261349 2.184013 -0.37240606 -3.37823974
#> 19 0.224212629 2.492123 -0.22879494 -3.27207251
#> 20 -0.004112628 2.144137 -0.26046945 -3.21065935
#> 21 0.073952654 2.242091 -0.13553486 -3.01914898
#> 22 -0.112038248 2.232621 -0.01949994 -3.18057066
#> 23 0.112769771 2.254836 -0.05162506 -2.95868332
#> 24 -0.059583672 2.120503 -0.05550223 -3.12632996
#> 25 0.118493516 2.166854 -0.66206903 -3.19682794
#> 26 0.109471333 1.933302 -0.67868089 -3.10703050
#> 27 0.130364913 2.333378 -0.59227835 -3.30070512
#> 28 0.165537169 2.088871 -0.66494850 -3.15263333
#> 29 0.229458848 2.244440 -0.51495185 -3.22121520
#> 30 0.378997130 1.964077 -0.41528950 -3.38221878
#> 31 0.385053565 2.387561 -0.61531545 -3.37165779
#> 32 0.238040637 2.252413 -0.70104904 -3.41072205
#> 33 -7.964392938 -3.224930 0.16143972 0.12763081
#> 34 -7.897961153 -3.341925 -0.05757983 0.21741398
#> 35 -8.048453585 -3.007984 0.37575365 0.57912569
#> 36 -7.833814814 -2.900955 0.48305115 0.67793194
#> 37 -7.987725948 -3.224413 0.06744160 0.07792081
#> 38 -8.032892076 -3.481177 0.19590674 -0.11951568
#> 39 -7.753587690 -3.012231 0.26906457 0.32652056
#> 40 -7.797695542 -3.074750 0.16776582 0.22217430
#> 41 -9.384571188 -3.378439 1.16643868 0.37422982
#> 42 -9.604885654 -3.394328 0.99459076 0.43155156
#> 43 -9.522436550 -3.250319 1.52251776 0.52611752
#> 44 -9.426269549 -3.247242 1.49656648 0.59288115
#> 45 -9.463362880 -3.614539 0.91223407 0.09880701
#> 46 -9.370342378 -3.653331 0.85577410 0.11074737
#> 47 -9.562281348 -3.458574 1.20678293 0.27387237
#> 48 -9.546425843 -3.599321 1.00237216 0.07293179
#> 49 -7.562344306 -3.342054 -0.24296706 -0.03458676
#> 50 -7.895429418 -3.650796 -0.21828732 -0.19996914
#> 51 -7.705291866 -3.207859 -0.18685591 0.13804876
#> 52 -7.806137206 -3.573850 -0.28226871 -0.07717293
#> 53 -7.728135729 -3.350649 -0.07990213 -0.17323438
#> 54 -7.974110952 -3.760932 -0.09624115 -0.38867595
#> 55 -7.911818707 -3.165671 -0.22620532 -0.08772709
#> 56 -7.956240694 -3.707672 -0.16511495 -0.29486838
#> 57 -8.829083384 -3.534828 0.40866805 0.19904364
#> 58 -8.426609409 -3.767035 0.49464326 -0.23061214
#> 59 -8.753325323 -3.403580 0.54694080 -0.07487983
#> 60 -8.559121051 -3.659930 0.23579787 -0.04471857
#> 61 -8.760380391 -3.641831 0.46372045 -0.02297091
#> 62 -8.698009718 -3.873140 0.27503658 -0.11410827
#> 63 -8.900721552 -3.754823 0.62800480 -0.16313798
#> 64 -8.565573539 -3.974408 0.17121621 -0.22496434
#> 65 -1.531045516 19.525391 3.40973892 2.67820576
#> 66 -1.515128673 19.346949 3.33696257 2.49142438
#> 67 -0.153016759 19.923565 2.91694714 3.42533084
#> 68 -0.076759795 20.000216 2.84036770 3.50205047
#> 69 -1.767234683 19.668493 3.29327519 2.51861079
#> 70 -1.534647643 19.453923 3.40038854 2.56471224
#> 71 -0.101988154 19.976780 2.86422918 3.47757700
#> 72 -0.013316060 20.065926 2.77488985 3.56690003
#> 73 -1.550954016 19.442680 3.60089040 2.61875421
#> 74 -1.585894438 19.410446 3.57226538 2.55907157
#> 75 -0.243253954 19.823840 3.01499072 3.33053642
#> 76 -0.291862799 19.761987 3.07285583 3.27503519
#> 77 -1.627677261 19.349984 3.67040266 2.48797212
#> 78 -1.563379216 19.360251 3.55258657 2.55986552
#> 79 -0.013478133 20.055973 2.78324438 3.56197856
#> 80 0.031041705 20.100306 2.73880445 3.60650330
#> 81 -8.023296606 -2.767930 1.01127978 1.18257261
#> 82 -7.943360367 -2.696739 0.85673530 1.06601034
#> 83 -7.959123013 -2.933720 1.05649906 1.13765163
#> 84 -8.097088211 -2.799729 1.12916008 1.22742332
#> 85 -7.942811399 -2.684895 0.79636700 1.04656987
#> 86 -7.962844840 -2.853980 0.77380761 0.98345690
#> 87 -7.844535861 -3.026456 1.09969307 1.11111373
#> 88 -7.938196645 -3.052247 1.17333099 1.20681935
#> 89 -9.471675502 -3.010232 1.80410555 0.80780118
#> 90 -9.269480815 -2.890674 1.80684006 0.59693459
#> 91 -9.397310369 -2.874876 1.87179822 1.04478425
#> 92 -9.237801932 -2.616285 1.81116218 0.89837486
#> 93 -9.547744412 -3.096017 1.87064178 0.72333143
#> 94 -9.383053931 -2.988294 1.81672023 0.66527955
#> 95 -9.388260515 -2.717982 1.95728418 1.01956202
#> 96 -9.543955175 -2.850355 2.02305494 0.87563586
#> 97 14.931544855 -6.975130 -2.64388400 2.18071678
#> 98 14.871214845 -6.926865 -2.81012194 2.30822534
#> 99 15.012485673 -7.128114 -2.58788149 2.46128440
#> 100 14.675002538 -7.034513 -2.41812287 2.34083951
#> 101 15.139858678 -6.903300 -2.75497288 2.07424076
#> 102 14.926232268 -6.831357 -2.87702725 2.49981161
#> 103 15.040132777 -7.055711 -2.37208315 2.45400187
#> 104 14.710268889 -7.049292 -2.40194180 2.58651405
#> 105 15.964812575 -6.987724 -1.98407621 2.32631297
#> 106 15.857291307 -7.023722 -2.09916646 2.51314478
#> 107 15.592745649 -6.982681 -1.89209951 2.45426189
#> 108 15.642837422 -7.188322 -2.23946027 2.70238895
#> 109 16.188245942 -7.008548 -2.19898468 2.22481094
#> 110 16.058780433 -6.925703 -2.20321948 2.57497395
#> 111 15.865417672 -7.156119 -2.00651823 2.65187471
#> 112 15.669831848 -7.217345 -2.20114576 2.76401774
#> 113 15.463402292 -6.590517 -2.66906742 2.00245221
#> 114 15.525933933 -6.514854 -2.74962770 1.88861171
#> 115 15.084602008 -6.913534 -2.46858987 2.53687264
#> 116 14.983884648 -6.935962 -2.89834591 2.39420787
#> 117 15.614369847 -6.357062 -2.96067719 2.09689836
#> 118 15.565706244 -6.397567 -2.75692528 1.80368763
#> 119 15.267863786 -6.443002 -2.79254970 2.06691804
#> 120 15.378634588 -6.643633 -2.63785101 1.92076533
#> 121 16.175277323 -6.643369 -2.18306754 2.15473475
#> 122 16.109342217 -6.777690 -2.08858807 2.19302391
#> 123 16.130145650 -6.980430 -1.99182035 2.49991533
#> 124 16.211735909 -6.916375 -2.04446282 2.48085819
#> 125 16.343872823 -6.572821 -2.02783374 2.20888248
#> 126 16.309426147 -6.579874 -2.03732771 2.16037858
#> 127 16.420003689 -6.584869 -1.91922376 2.29745881
#> 128 16.474516168 -6.511731 -2.00913245 2.33065370
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
datos <- Cardiological
datos
#> # A tibble: 11 × 3
#> Pulse Syst Diast
#> <symblc_n> <symblc_n> <symblc_n>
#> 1 [44.00 : 68.00] [90.00 : 100.00] [50.00 : 70.00]
#> 2 [60.00 : 72.00] [90.00 : 130.00] [70.00 : 90.00]
#> 3 [56.00 : 90.00] [140.00 : 180.00] [90.00 : 100.00]
#> 4 [70.00 : 112.00] [110.00 : 142.00] [80.00 : 108.00]
#> 5 [54.00 : 72.00] [90.00 : 100.00] [50.00 : 70.00]
#> 6 [70.00 : 100.00] [130.00 : 160.00] [80.00 : 110.00]
#> 7 [63.00 : 75.00] [60.00 : 100.00] [140.00 : 150.00]
#> 8 [72.00 : 100.00] [130.00 : 160.00] [76.00 : 90.00]
#> 9 [76.00 : 98.00] [110.00 : 190.00] [70.00 : 110.00]
#> 10 [86.00 : 96.00] [138.00 : 180.00] [90.00 : 110.00]
#> 11 [86.00 : 100.00] [110.00 : 150.00] [78.00 : 100.00]
C:Lobo5nYF3e48125f303f.R
x <- sym.umap(datos)
x
#> V1 V2 V3
#> 1 1.09816725 -3.538156899 3.52643628
#> 2 1.76812481 -3.346935863 2.74148245
#> 3 0.90699889 -3.636123855 3.72398006
#> 4 1.60231401 -3.231225208 2.82233692
#> 5 0.68871687 -3.627660279 3.19471789
#> 6 1.29533886 -2.674806538 2.44908975
#> 7 0.76340493 -3.455349904 3.24095315
#> 8 1.42147513 -2.393110767 2.07603893
#> 9 1.04616182 -2.853632711 2.85405265
#> 10 1.26684172 -2.503855749 2.23837220
#> 11 1.68851271 -0.925039419 0.84399774
#> 12 1.58223197 -1.054301597 0.95702759
#> 13 0.71890192 -2.683341005 2.34588002
#> 14 0.82451132 -2.391109966 1.97436221
#> 15 1.34037747 -0.452855232 0.59927690
#> 16 1.55755848 -0.574244376 0.59578152
#> 17 1.43356229 -0.185902540 0.47030533
#> 18 -1.20847784 1.779207765 -2.25151482
#> 19 1.31663701 0.380578434 0.24974963
#> 20 -0.28683309 2.990289247 -1.88759709
#> 21 1.21183273 -0.182581109 0.54650965
#> 22 -1.09759035 1.552352470 -1.71870611
#> 23 1.06107859 0.418083618 0.19340722
#> 24 -0.33912315 2.904323425 -1.47925531
#> 25 1.35383919 -2.004018943 1.63133760
#> 26 -2.01860675 1.804741189 -2.71238894
#> 27 1.77476551 -0.660466584 0.49174687
#> 28 -1.46697677 2.296408202 -2.69055451
#> 29 0.04251169 -0.444502161 0.58157328
#> 30 -2.13099380 1.528284548 -1.94486222
#> 31 0.72652238 0.050619425 0.40143330
#> 32 -1.70803300 2.117853539 -1.66497395
#> 33 0.94043021 -3.372758783 3.39684683
#> 34 1.70378308 -3.299058094 2.65710390
#> 35 0.98910482 -3.430558317 3.55000316
#> 36 1.68786493 -3.322400624 2.59573648
#> 37 0.84716435 -3.069099246 3.07790579
#> 38 1.42968879 -2.398926587 2.10740820
#> 39 0.94393474 -3.051954434 3.15796089
#> 40 1.33483013 -2.307960056 1.96340619
#> 41 1.66028643 -0.842585638 0.74696799
#> 42 -1.99945582 1.913283115 -2.67996531
#> 43 1.65467938 -0.006400584 0.06289713
#> 44 -1.02514302 2.580730247 -2.47862607
#> 45 0.42605377 -0.183384377 0.43048498
#> 46 -1.86632655 1.813657087 -1.70145940
#> 47 0.86748188 0.381639834 0.20370572
#> 48 -1.26265744 2.379830604 -1.54014670
#> 49 -0.98904525 -0.941427029 1.12388890
#> 50 -0.98179308 -0.981799270 1.00642279
#> 51 -0.71759350 -0.588184749 1.24639905
#> 52 -0.68690526 -0.612042641 1.16036583
#> 53 -0.88095107 -0.986836917 0.96096545
#> 54 -0.81700486 -0.859960139 0.90545453
#> 55 -0.63681232 -0.771412459 1.06039809
#> 56 -0.61700786 -0.657136276 1.10691161
#> 57 1.49274556 -1.110346424 1.01543610
#> 58 -1.69716693 1.979967050 -2.83467668
#> 59 1.66555003 0.228613688 -0.08430390
#> 60 -1.10569944 2.527831449 -2.51440055
#> 61 1.69502267 -0.645931310 0.32403468
#> 62 -1.80000875 1.894974359 -2.40533961
#> 63 1.40448125 0.098614296 -0.13136499
#> 64 -1.33649841 2.439660261 -2.12611758
#> 65 1.70599911 -2.325385152 1.59099305
#> 66 -1.88085784 1.617197840 -3.06336960
#> 67 1.53213513 0.718806324 -0.27097853
#> 68 -0.67684111 2.886050864 -2.25118054
#> 69 -0.22153162 -0.264035948 0.33500838
#> 70 -1.90926478 1.275350948 -1.69424750
#> 71 -0.10709370 2.631466034 -1.14360720
#> 72 -0.68486729 2.860191391 -1.36016658
#> 73 -1.03958038 1.451797613 -2.08054544
#> 74 -1.55058932 1.824743603 -2.25318067
#> 75 -0.02504676 2.903059033 -1.77666343
#> 76 -0.55151902 2.995692568 -2.04373888
#> 77 -1.14829136 1.348060429 -1.34613615
#> 78 -1.49471982 1.680909076 -1.49096595
#> 79 -0.39695433 2.868505377 -1.36636691
#> 80 -0.66428082 2.730702135 -1.47800989
#> 81 -1.63833693 1.236282318 -3.04401700
#> 82 -1.89499992 1.641627746 -2.94616587
#> 83 -0.76737500 1.698877682 -2.30270006
#> 84 -1.22642606 2.469874334 -2.82513404
#> 85 -1.57577611 0.757059808 -1.50731599
#> 86 -2.10379650 1.416716430 -2.00286564
#> 87 -0.83272595 1.655567366 -1.59249273
#> 88 -1.40404487 2.118722990 -1.85045246
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
data(oils)
datos <- oils
interval.length(datos)
#> GRA FRE IOD SAP
#> L 0.005 9 34 78
#> P 0.007 1 16 9
#> Co 0.002 5 14 9
#> S 0.006 2 12 6
#> Ca 0.001 10 2 4
#> O 0.005 6 11 9
#> B 0.010 8 8 9
#> H 0.006 10 24 12
C:Lobo5nYF3e48125f303f.R
data("hardwoodBrito")
Hardwood.histogram<-hardwoodBrito
Hardwood.cols<-colnames(Hardwood.histogram)
Hardwood.names<-row.names(Hardwood.histogram)
Hardwood.histogram
#> # A tibble: 5 × 4
#> ANNT JULT ANNP MITM
#> * <symblc_h> <symblc_h> <symblc_h> <symblc_h>
#> 1 <hist> <hist> <hist> <hist>
#> 2 <hist> <hist> <hist> <hist>
#> 3 <hist> <hist> <hist> <hist>
#> 4 <hist> <hist> <hist> <hist>
#> 5 <hist> <hist> <hist> <hist>
Hardwood.histogram[[1]][[1]]
#> $breaks
#> [1] -3.9 4.2 10.3 20.6
#>
#> $props
#> [1] 0.5 0.4 0.1
C:Lobo5nYF3e48125f303f.R
C:Lobo5nYF3e48125f303f.R
BIN.Matrix<-matrix(rep(3,length(Hardwood.cols)*length(Hardwood.names)),nrow = length(Hardwood.names))
C:Lobo5nYF3e48125f303f.R
pca.hist<-sym.histogram.pca(Hardwood.histogram,BIN.Matrix)
#> Warning: Setting row names on a tibble is deprecated.
#> Setting row names on a tibble is deprecated.
#> Setting row names on a tibble is deprecated.
#> Setting row names on a tibble is deprecated.
pca.hist$classic.PCA
#> **Results for the Principal Component Analysis (PCA)**
#> The analysis was performed on 85 individuals, described by 4 variables
#> *The results are available in the following objects:
#>
#> name description
#> 1 "$eig" "eigenvalues"
#> 2 "$var" "results for the variables"
#> 3 "$var$coord" "coord. for the variables"
#> 4 "$var$cor" "correlations variables - dimensions"
#> 5 "$var$cos2" "cos2 for the variables"
#> 6 "$var$contrib" "contributions of the variables"
#> 7 "$ind" "results for the individuals"
#> 8 "$ind$coord" "coord. for the individuals"
#> 9 "$ind$cos2" "cos2 for the individuals"
#> 10 "$ind$contrib" "contributions of the individuals"
#> 11 "$ind.sup" "results for the supplementary individuals"
#> 12 "$ind.sup$coord" "coord. for the supplementary individuals"
#> 13 "$ind.sup$cos2" "cos2 for the supplementary individuals"
#> 14 "$call" "summary statistics"
#> 15 "$call$centre" "mean of the variables"
#> 16 "$call$ecart.type" "standard error of the variables"
#> 17 "$call$row.w" "weights for the individuals"
#> 18 "$call$col.w" "weights for the variables"
pca.hist$sym.hist.matrix.PCA
#> # A tibble: 5 × 4
#> PC.1 PC.2 PC.3 PC.4
#> * <symblc_h> <symblc_h> <symblc_h> <symblc_h>
#> 1 <hist> <hist> <hist> <hist>
#> 2 <hist> <hist> <hist> <hist>
#> 3 <hist> <hist> <hist> <hist>
#> 4 <hist> <hist> <hist> <hist>
#> 5 <hist> <hist> <hist> <hist>
C:Lobo5nYF3e48125f303f.R
ACER.p1<-Sym.PCA.Hist.PCA.k.plot(data.sym.df = pca.hist$Bins.df,
title.graph = " ",
concepts.name = c("ACER"),
title.x = "First Principal Component (84.83%)",
title.y = "Frequency",
pca.axes = 1)
ACER.p1
C:Lobo5nYF3e48125f303f.R
ALL.p1<-Sym.PCA.Hist.PCA.k.plot(data.sym.df = pca.hist$Bins.df,
title.graph = " ",
concepts.name = unique(pca.hist$Bins.df$Object.Name),
title.x = "First Principal Component (84.83%)",
title.y = "Frequency",
pca.axes = 1)
ALL.p1
#> Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps
C:Lobo5nYF3e48125f303f.R
Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
#> Warning in min(which(props.cum >= percentils.RSDA[i])): ningún argumento finito
#> para min; retornando Inf
#> Warning: Setting row names on a tibble is deprecated.
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "First Principal Component (84.83%)"
axes.y.label<- "Second Principal Component (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
quantile.ACER.plot<-Percentil.Arrow.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
quantile.ACER.plot
C:Lobo5nYF3e48125f303f.R
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "First Principal Component (84.83%)"
axes.y.label<- "Second Principal Component (9.70%)"
concept.names<-row.names(Hardwood.quantiles.PCA)
var.names<-c("PC.1","PC.2")
quantile.plot<-Percentil.Arrow.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name
)
quantile.plot
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_segment()`).
C:Lobo5nYF3e48125f303f.R
label.name<-"Hard Wood"
Title<-"First Principal Plane"
axes.x.label<- "PC 1 (84.83%)"
axes.y.label<- "PC 2 (9.70%)"
concept.names<-c("ACER")
var.names<-c("PC.1","PC.2")
plot.3D.HW<-sym.quantiles.PCA.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name)
plot.3D.HW
C:Lobo5nYF3e48125f303f.R
concept.names<-row.names(Hardwood.quantiles.PCA)
sym.all.quantiles.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name)
#> Warning: Ignoring 4 observations
C:Lobo5nYF3e48125f303f.R
sym.all.quantiles.mesh3D.plot(Hardwood.quantiles.PCA,
concept.names,
var.names,
Title,
axes.x.label,
axes.y.label,
label.name)
C:Lobo5nYF3e48125f303f.R
Hardwood.quantiles.PCA.2<-quantiles.RSDA.KS(pca.hist$sym.hist.matrix.PCA,100)
#> Warning: Setting row names on a tibble is deprecated.
h<-Hardwood.quantiles.PCA.2[[1]][[1]]
tmp<-HistRSDAToEcdf(h)
h2<-Hardwood.quantiles.PCA.2[[1]][[2]]
tmp2<-HistRSDAToEcdf(h2)
h3<-Hardwood.quantiles.PCA.2[[1]][[3]]
tmp3<-HistRSDAToEcdf(h3)
h4<-Hardwood.quantiles.PCA.2[[1]][[4]]
tmp4<-HistRSDAToEcdf(h4)
h5<-Hardwood.quantiles.PCA.2[[1]][[5]]
tmp5<-HistRSDAToEcdf(h5)
breaks.unique<-unique(c(h$breaks,h2$breaks,h3$breaks,h4$breaks,h5$breaks))
tmp.unique<-breaks.unique[order(breaks.unique)]
tmp<-tmp(v = tmp.unique)
tmp2<-tmp2(v = tmp.unique)
tmp3<-tmp3(v = tmp.unique)
tmp4<-tmp4(v = tmp.unique)
tmp5<-tmp5(v = tmp.unique)
abs_dif <- abs(tmp2 - tmp)
# La distancia Kolmogorov–Smirnov es el máximo de las distancias absolutas.
distancia_ks <- max(abs_dif)
distancia_ks
#> [1] 0.05857869
C:Lobo5nYF3e48125f303f.R
library(tidyr)
# Se unen los valores calculados en un dataframe.
df.HW <- data.frame(
PC.1 = tmp.unique,
ACER = tmp,
ALNUS = tmp2,
FRAXINUS = tmp3,
JUGLANS = tmp4,
QUERCUS = tmp5
) %>%
pivot_longer(
cols = c(ACER, ALNUS,FRAXINUS,JUGLANS,QUERCUS),
names_to = "HardWood",
values_to = "ecdf"
)
grafico_ecdf <- ggplot(data = df.HW,
aes(x = PC.1, y = ecdf, color = HardWood)) +
geom_line(size = 1) +
labs(
color = "Hardwood",
y = "Empirical Cumulative Distribution "
) +
theme_bw() +
theme(legend.position = "bottom",
plot.title = element_text(size = 12))+geom_line()
grafico_ecdf
C:Lobo5nYF3e48125f303f.R
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