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]
##How to save a Symbolic Table in a CSV file with RSDA?
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>
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$F1
#> [1] 2.8 1.4 3.2 -2.1 -3.0 0.1 9.0
data(ex1_db2so)
ex1_db2so
#> state sex county group age
#> 1 Florida M 2 6 3
#> 2 California F 4 3 4
#> 3 Texas M 12 3 4
#> 4 Florida F 2 3 4
#> 5 Texas M 4 6 4
#> 6 Texas F 2 3 3
#> 7 Florida M 6 3 4
#> 8 Florida F 2 6 4
#> 9 California M 2 3 6
#> 10 California F 21 3 4
#> 11 California M 2 3 4
#> 12 California M 2 6 7
#> 13 Texas F 23 3 4
#> 14 Florida M 2 3 4
#> 15 Florida F 12 7 4
#> 16 Texas M 2 3 8
#> 17 California F 3 7 9
#> 18 California M 2 3 11
#> 19 California M 1 3 11
The classic.to.sym
function allows to convert a
traditional table into a symbolic one, to this we must indicate the
following parameters.
x
= a data.frameconcept
= variables to be used as a conceptvariables
= variables to be used, conceptible with
tidyselect optionsdefault.numeric
= function that will be used by default
for numerical values (sym.interval)default.categorical
= functions to be used by default
for categorical values (sym.model)result <- classic.to.sym(x = ex1_db2so,
concept = c(state, sex),
variables = c(county, group, age))
result
#> # A tibble: 6 × 3
#> county group age
#> <symblc_n> <symblc_n> <symblc_n>
#> 1 [3.00 : 21.00] [3.00 : 7.00] [4.00 : 9.00]
#> 2 [1.00 : 2.00] [3.00 : 6.00] [4.00 : 11.00]
#> 3 [2.00 : 12.00] [3.00 : 7.00] [4.00 : 4.00]
#> 4 [2.00 : 6.00] [3.00 : 6.00] [3.00 : 4.00]
#> 5 [2.00 : 23.00] [3.00 : 3.00] [3.00 : 4.00]
#> 6 [2.00 : 12.00] [3.00 : 6.00] [4.00 : 8.00]
We can add new variables indicating the type we want them to be.
result <- classic.to.sym(x = ex1_db2so,
concept = c("state", "sex"),
variables = c(county, group, age),
age_hist = sym.histogram(age, breaks = pretty(ex1_db2so$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]
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
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
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
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
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
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>
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>
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
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.
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.
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
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$Fitted)
#> [1] 0.4962964
RMSE.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.7087091
RMSE.U(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.705288
R2.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.5207597
R2.U(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.5248207
deter.coefficient(int_prost_test$lpsa, pred.cm.lasso)
#> [1] 0.4943982
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
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
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
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]
x <- sym.umap(datos)
x
#> V1 V2 V3 V4
#> 1 -4.904584 1.538384 6.75820083 -2.3212114
#> 2 -4.857704 1.585300 6.71138946 -2.2743489
#> 3 -4.906814 1.536368 6.76051988 -2.3236377
#> 4 -5.013118 1.429992 6.86698226 -2.4299134
#> 5 -5.023432 1.419456 6.87772098 -2.4400440
#> 6 -5.069342 1.373482 6.92369221 -2.4859095
#> 7 -5.071728 1.371018 6.92628662 -2.4882502
#> 8 -5.177990 1.264263 7.03320402 -2.5940432
#> 9 -6.300605 4.793098 0.38748728 -6.2030963
#> 10 -6.299365 4.537489 0.42403038 -5.9700775
#> 11 -6.308239 4.603611 0.43227548 -6.0371285
#> 12 -6.416368 4.470363 0.48284009 -5.9046240
#> 13 -6.270467 4.764999 0.33841388 -6.2628796
#> 14 -6.371458 4.604948 0.37827941 -6.0295381
#> 15 -6.111820 4.797583 0.33298975 -6.3855645
#> 16 -6.079197 4.645989 0.48883781 -6.4214283
#> 17 -6.249113 3.574731 0.10357841 -5.9150911
#> 18 -6.079768 3.510695 0.40452578 -5.8926781
#> 19 -6.101873 3.371452 0.23340349 -5.7182819
#> 20 -6.221269 3.454136 0.18637275 -5.6270594
#> 21 -6.167177 3.455088 0.16406425 -5.9309452
#> 22 -6.151517 3.334416 0.32609083 -5.8285429
#> 23 -6.191590 3.218538 0.27193515 -5.6252016
#> 24 -6.215456 3.287122 0.17240219 -5.8699863
#> 25 -6.104051 3.916735 0.58310194 -5.9583182
#> 26 -6.032951 3.795693 0.44249291 -5.9361667
#> 27 -5.953808 3.926074 0.34660060 -6.1248780
#> 28 -5.888163 3.640787 0.41792174 -5.9773691
#> 29 -6.131044 3.770340 0.75194534 -6.2208176
#> 30 -5.964418 3.585974 0.62380347 -6.0909858
#> 31 -6.055221 3.739218 0.69789821 -6.0896560
#> 32 -6.229060 3.633280 0.66387566 -6.2742763
#> 33 -5.099828 -7.475627 0.74167511 4.4207652
#> 34 -5.071327 -7.593541 0.90041434 4.5307943
#> 35 -5.235268 -7.329050 0.34560749 4.2019275
#> 36 -5.227772 -7.396572 0.39469695 4.2548445
#> 37 -5.135821 -7.682479 0.78662631 5.0620995
#> 38 -5.139942 -7.703007 0.90786897 5.0670564
#> 39 -5.182761 -7.552357 0.57397916 4.8320104
#> 40 -5.178386 -7.654047 0.56870724 4.8603815
#> 41 -5.851107 -6.564107 1.55398555 4.3061961
#> 42 -5.679760 -6.432394 1.46268122 4.1977727
#> 43 -5.763689 -6.219098 1.46968370 4.1255082
#> 44 -5.934873 -6.085088 1.44025767 4.0378334
#> 45 -5.667380 -6.772557 1.70902606 4.5364535
#> 46 -5.613216 -6.831850 1.65018311 4.5744059
#> 47 -5.727431 -6.543448 1.65160305 4.3822837
#> 48 -5.626419 -6.481780 1.82440691 4.4829770
#> 49 -5.126185 -7.897410 0.72859724 4.6928782
#> 50 -4.928033 -8.138788 1.02812100 4.7650078
#> 51 -4.967227 -7.960590 0.58963741 4.6234597
#> 52 -4.974336 -8.160651 0.97041683 4.7893050
#> 53 -4.897846 -8.132371 0.79527874 5.0393528
#> 54 -4.924901 -8.109813 0.88126714 4.9618734
#> 55 -4.969474 -7.884914 0.66408825 5.0324782
#> 56 -5.057587 -8.271352 1.03136993 4.8099312
#> 57 -5.316326 -7.289643 1.43156206 4.5732973
#> 58 -5.111947 -7.633524 1.49503057 4.5128339
#> 59 -5.256842 -7.198255 1.39590899 4.4657796
#> 60 -5.095191 -7.749376 1.61685959 4.5294909
#> 61 -5.359095 -7.391953 1.41028706 4.9097135
#> 62 -5.245188 -7.741391 1.58951341 4.8974179
#> 63 -5.481601 -7.285192 1.42942571 4.9416659
#> 64 -5.251460 -7.697188 1.60535413 4.8650974
#> 65 -8.372880 17.933349 -0.51667831 0.4730853
#> 66 -8.383012 17.993364 -0.50223136 0.4554801
#> 67 -7.620988 18.698964 0.18163965 -1.4428425
#> 68 -7.608644 18.715909 0.19662557 -1.4951278
#> 69 -8.299310 17.852529 -0.52270966 0.4709766
#> 70 -8.340136 17.803680 -0.40507011 0.6114188
#> 71 -7.575126 18.746710 0.22772448 -1.4780285
#> 72 -7.557049 18.762905 0.24476746 -1.3946858
#> 73 -8.369574 18.012276 -0.38702974 0.5375235
#> 74 -8.410000 18.095615 -0.37946406 0.5238287
#> 75 -7.379574 18.911018 0.41223150 -1.3957959
#> 76 -7.332534 18.972796 0.46352492 -1.4469418
#> 77 -8.435612 18.131709 -0.36364736 0.5199528
#> 78 -8.304458 18.132049 -0.55727509 0.3688190
#> 79 -7.459144 18.842547 0.33290648 -1.3265526
#> 80 -7.372105 18.930245 0.42232176 -1.3878557
#> 81 -5.458668 -6.894257 0.06898896 3.7504912
#> 82 -5.445282 -6.817514 0.11835602 3.7296107
#> 83 -5.517718 -6.555932 0.14041982 3.4493345
#> 84 -5.491878 -6.647160 0.06095724 3.5377876
#> 85 -5.337034 -7.080310 0.10108331 3.8606191
#> 86 -5.280924 -7.114982 0.03189064 3.9186513
#> 87 -5.395513 -6.721370 0.01722195 3.3672401
#> 88 -5.533703 -6.645847 0.01947283 3.5078766
#> 89 -5.801749 -5.963931 1.06233850 3.9283262
#> 90 -5.793501 -5.897878 1.09888956 3.8532635
#> 91 -5.825029 -6.006815 0.79436611 3.8354403
#> 92 -5.906884 -5.940017 0.63258519 3.7785073
#> 93 -5.759361 -5.982977 1.15222804 3.9134320
#> 94 -5.819552 -5.866243 1.26678575 3.8168316
#> 95 -6.024359 -5.797050 0.79689884 3.6359669
#> 96 -5.963222 -5.850789 0.77824485 3.7034620
#> 97 17.899063 -2.403311 -3.28888640 -1.2333975
#> 98 17.638703 -2.306253 -3.13604239 -1.2723186
#> 99 17.461451 -2.198433 -3.30951786 -1.0005919
#> 100 17.448743 -2.247254 -3.23907260 -1.0108945
#> 101 17.880502 -2.612442 -3.27887829 -1.0875815
#> 102 17.851416 -2.546209 -3.28138602 -1.2231816
#> 103 17.664357 -2.323982 -3.46172116 -0.8712212
#> 104 17.361283 -2.196847 -3.17160026 -0.9281438
#> 105 17.902661 -1.414592 -3.64901478 -1.1316919
#> 106 17.701823 -1.444522 -3.20948968 -1.1961868
#> 107 17.447623 -1.684116 -3.44717734 -1.0400469
#> 108 17.443707 -1.667049 -3.22878079 -0.9824174
#> 109 17.795465 -1.461886 -3.67314680 -1.1484491
#> 110 17.835233 -1.340439 -3.17075297 -1.2165450
#> 111 17.426748 -1.723475 -3.47586776 -1.0499991
#> 112 17.589303 -1.509566 -3.08295343 -1.1171597
#> 113 18.392268 -2.272137 -3.18861958 -1.2974681
#> 114 18.357815 -2.195586 -3.03349551 -1.2008729
#> 115 17.946772 -2.516625 -3.43894641 -1.1134954
#> 116 17.733153 -2.585212 -3.25655162 -1.0765859
#> 117 18.579509 -2.429155 -3.34396340 -1.2060475
#> 118 18.491009 -2.358731 -3.42307518 -1.2517755
#> 119 18.251700 -2.590580 -3.41014873 -1.1026711
#> 120 18.359787 -2.536417 -3.33901149 -1.1653443
#> 121 18.142335 -1.624291 -3.62947479 -1.0978405
#> 122 18.186572 -1.437277 -3.68497837 -1.3301269
#> 123 17.756532 -1.320255 -3.71869686 -1.1411649
#> 124 17.922320 -1.179414 -3.47807254 -1.3398375
#> 125 18.606321 -1.577696 -3.53997352 -1.3115289
#> 126 18.619996 -1.623919 -3.55057848 -1.3325337
#> 127 18.163459 -1.265042 -3.59648284 -1.2378888
#> 128 18.333828 -1.251088 -3.40928991 -1.0960301
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]
x <- sym.umap(datos)
x
#> V1 V2 V3
#> 1 -2.14479074 2.59379597 3.5392743
#> 2 -2.27545511 1.71887790 3.1477438
#> 3 -2.38952881 2.48635972 3.3872428
#> 4 -2.49659835 1.48846687 3.1361170
#> 5 -2.23857288 2.74106577 3.2013711
#> 6 -1.83477042 1.81496995 2.6924500
#> 7 -1.91095817 2.78276887 3.4245928
#> 8 -1.62915610 1.50727781 2.4928421
#> 9 -1.77579478 2.12977603 3.1588105
#> 10 -1.77127953 1.40161721 2.5171227
#> 11 -0.12045754 1.08547017 1.2842504
#> 12 -0.22991397 1.07202550 1.2634083
#> 13 -1.33114844 1.88060168 3.0330775
#> 14 -1.32401458 1.53730545 2.6984849
#> 15 -0.12018583 0.71234862 0.5338418
#> 16 -0.17214096 1.03805950 0.4162557
#> 17 0.14585323 0.59098197 0.4845428
#> 18 1.24423011 -1.62886494 -1.4558648
#> 19 0.82984700 0.73766770 0.5338695
#> 20 2.58669097 -0.84172708 -1.6202881
#> 21 -0.02714136 0.37013670 0.1772535
#> 22 0.95499117 -1.16570294 -1.5724796
#> 23 0.72272126 0.57666386 0.3148507
#> 24 2.43581890 -0.47180586 -1.6210536
#> 25 -1.20641542 1.32386976 2.2789587
#> 26 0.94521123 -2.16589936 -2.4068447
#> 27 0.30167203 1.03164563 0.7927705
#> 28 1.57607119 -2.15944943 -2.1813110
#> 29 -0.60197739 -0.09130157 -0.3745937
#> 30 0.70980030 -1.28712481 -2.5805079
#> 31 0.14496065 0.22564598 -0.1755002
#> 32 1.25330417 -1.04559782 -2.3088856
#> 33 -2.49417607 2.03921682 3.5254530
#> 34 -2.35634689 1.58335217 3.2579128
#> 35 -2.30535769 2.20581050 3.4539240
#> 36 -2.50668957 1.53111616 2.9113920
#> 37 -1.77463757 2.42086694 3.4753352
#> 38 -1.64588955 1.61483445 2.7491991
#> 39 -1.90144302 2.51292048 3.2665300
#> 40 -1.48627894 1.19241024 2.4023657
#> 41 -0.06252246 1.15543251 0.7680155
#> 42 1.21970830 -2.25307971 -2.1787469
#> 43 0.75079522 0.77970536 0.5535985
#> 44 2.12754580 -1.76105059 -1.9310708
#> 45 -0.25723361 0.03428569 -0.3733735
#> 46 0.97202894 -0.99213835 -2.3604784
#> 47 0.34182022 0.27006775 -0.1572117
#> 48 1.61062104 -0.85048362 -2.0314136
#> 49 -2.15974590 -0.67157361 -0.5370372
#> 50 -1.96502373 -0.71064904 -0.5332742
#> 51 -1.84550170 -0.52180718 -0.3755741
#> 52 -1.88028893 -0.59435032 -0.5520671
#> 53 -1.90184332 -0.69894890 -0.4197792
#> 54 -2.08784849 -0.73428617 -0.5192094
#> 55 -1.99977944 -0.86072876 -0.8744527
#> 56 -1.98957541 -0.52783081 -0.7484574
#> 57 -0.16021319 1.04998809 0.8923613
#> 58 1.36676310 -2.03621056 -2.0089949
#> 59 1.01167010 0.70898645 0.7317910
#> 60 2.13307773 -1.95573928 -1.8435555
#> 61 -0.32032393 0.83879703 0.2758640
#> 62 1.09116017 -1.78249310 -2.0601891
#> 63 1.01923226 0.61656327 0.2722339
#> 64 1.95771125 -1.45757093 -1.9451797
#> 65 -1.42907673 1.14362811 2.1742642
#> 66 0.62379180 -2.23128676 -2.2281560
#> 67 1.44393799 0.38760451 0.2077620
#> 68 2.59199328 -1.39035742 -1.7946066
#> 69 -0.48280370 -0.22988577 -0.6594886
#> 70 0.44986619 -1.13604503 -2.4198694
#> 71 2.22713778 -0.19389759 -1.2670647
#> 72 2.22138869 -0.60181128 -1.8103428
#> 73 1.06113935 -1.38241655 -1.2662027
#> 74 1.35409615 -1.76863472 -1.6425753
#> 75 2.73055401 -0.58699492 -1.4754511
#> 76 2.61302263 -0.95963867 -1.7384982
#> 77 0.68919426 -0.85774385 -1.4694289
#> 78 1.02236433 -0.90473805 -2.0140435
#> 79 2.36192469 -0.40910210 -1.5734577
#> 80 2.14542392 -0.54499795 -1.7347569
#> 81 0.29667233 -1.99281738 -1.6657416
#> 82 0.71926954 -2.04617292 -2.3540021
#> 83 1.44594323 -1.67901732 -1.1528951
#> 84 1.96677301 -2.17343071 -2.0018194
#> 85 0.05638720 -1.06417574 -1.6819384
#> 86 0.51052832 -1.36656198 -2.2661834
#> 87 1.16449201 -0.96687742 -1.3619679
#> 88 1.46369318 -1.17996627 -2.1012484