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.

How to use RSDA 3.3

RSDA Package version 3.3

Oldemar Rodríguez R.

Installing the package

CRAN

install.packages("RSDA", dependencies=TRUE)

Github

devtools::install_github("PROMiDAT/RSDA")

How to read a Symbolic Table from a CSV file with RSDA?

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?

write.sym.table(ex3, file = 'tsymtemp.csv', sep = ';',dec = '.',
                row.names = TRUE, col.names = TRUE)

Symbolic Data Frame Example in 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

How to generated a symbolic data table from a classic data table in RSDA?

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.

Example 1

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]

Example 2

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

Example 3

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

Example 4

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

Converting a SODAS 1.0 *.SDS files to RSDA files

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>
# 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)

Converting a SODAS 2.0 *.XML files to RSDA files

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>
write.sym.table(abalone,
                file='abalone.csv',
                sep=';',
                dec='.',
                row.names = TRUE,
                col.names = TRUE)

Basic statistics

Symbolic Mean

data(example3)
mean(example3$F1)
#> [1] 1.628571
mean(example3[,1])
#> [1] 1.628571
mean(example3$F2)
#> [1] 5
mean(example3[,2])
#> [1] 5
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]

Symbolic median

median(example3$F1)
#> [1] 1.4
median(example3[,1])
#> [1] 1.4
median(example3$F2)
#> [1] 1.5
median(example3[,2])
#> [1] 1.5
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]

Variance and standard deviation

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

Symbolic correlation

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

Radar plot for intervals

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.


res <- interval.histogram.plot(oils[,2],
                               n.bins = 4,
                               col = c(2,3,4,5))

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))

res
#> $frequency
#> [1] 50 25 25
#> 
#> $histogram
#>      [,1]
#> [1,]  0.7
#> [2,]  1.9
#> [3,]  3.1

Distances for intervals

Gowda-Diday

data("oils")
DM <- sym.dist.interval(sym.data = oils[,1:4],
                        method = "Gowda.Diday")
model <- hclust(DM)
plot(model, hang = -1)

Ichino

DM <- sym.dist.interval(sym.data= oils[,1:4],
                        method = "Ichino")
model <- hclust(DM)
plot(model, hang = -1)

Hausdorff

DM <- sym.dist.interval(sym.data = oils[,c(1,2,4)],
                        gamma = 0.5,
                        method = "Hausdorff",
                        normalize = FALSE,
                        SpanNormalize = TRUE,
                        euclidea = TRUE,
                        q = 2)
model <- hclust(DM)
plot(model, hang = -1)

Linear regression for intervals

Training

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

Prediction

pred.cm <- sym.predict(model = res.cm, new.sym.data = int_prost_test)

Testing

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

LASSO regression for intervals

data(int_prost_train)
data(int_prost_test)

Training

res.cm.lasso <- sym.glm(sym.data = int_prost_train,
                        response = 9,
                        method = 'cm',
                        alpha = 1,
                        nfolds = 10,
                        grouped = TRUE)

Prediction

pred.cm.lasso <- sym.predict(res.cm.lasso,
                             response = 9,
                             int_prost_test,
                             method = 'cm')

Testing

plot(res.cm.lasso)

plot(res.cm.lasso$glmnet.fit, "lambda", label=TRUE)

RMSE.L(int_prost_test$lpsa,pred.cm.lasso)
#> [1] 0.706699
RMSE.U(int_prost_test$lpsa,pred.cm.lasso) 
#> [1] 0.7033206
R2.L(int_prost_test$lpsa,pred.cm.lasso) 
#> [1] 0.5236074
R2.U(int_prost_test$lpsa,pred.cm.lasso) 
#> [1] 0.5276086
deter.coefficient(int_prost_test$lpsa, pred.cm.lasso)
#> [1] 0.4929714

RIDGE regression for intervals

Training

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)

Prediction

pred.cm.ridge <- sym.predict(res.cm.ridge,
                             response = 9,
                             int_prost_test,
                             method = 'cm')

Testing

plot(res.cm.ridge)

plot(res.cm.ridge$glmnet.fit, "lambda", label=TRUE)

RMSE.L(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.7003401
RMSE.U(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.6973955
R2.L(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.5335437
R2.U(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.5369574
deter.coefficient(int_prost_test$lpsa, pred.cm.ridge)
#> [1] 0.4743088

PCA for intervals

Example 1

data("oils")
res <- sym.pca(oils,'centers')
plot(res, choix = "ind")

plot(res, choix = "var")

Example 2

res <- sym.pca(oils,'tops')
#> Warning in fun(libname, pkgname): couldn't connect to display ":0"
plot(res, choix = "ind")

Example 3

res <- sym.pca(oils, 'principal.curves')
plot(res, choix = "ind")

Example 4

res <- sym.pca(oils,'optimized.distance')
plot(res, choix = "ind")

plot(res, choix = "var")

Example 5

res <- sym.pca(oils,'optimized.variance')
plot(res, choix = "ind")

plot(res, choix = "var")

Symbolic Multiple Correspondence Analysis

Example 1

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
res <- sym.mcfa(sym.table, c(2,3))
mcfa.scatterplot(res[,2], res[,3], sym.data = sym.table, pos.var = c(2,3))

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))

Symbolic UMAP

Ejemplo Oils

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   -2.585767  0.68879511 12.25608472 -4.444671
#> 2   -2.731787  0.83482318 12.40253807 -4.298676
#> 3   -2.612730  0.71577486 12.28307965 -4.417711
#> 4   -2.785322  0.88836144 12.45726421 -4.245171
#> 5   -2.673584  0.77663742 12.34394377 -4.356922
#> 6   -2.472002  0.57506048 12.14125741 -4.558478
#> 7   -2.631238  0.73430321 12.30205138 -4.399287
#> 8   -2.764020  0.86708353 12.43541279 -4.266562
#> 9   -7.724020 -7.92523567 -4.18923765 -8.380159
#> 10  -7.660165 -7.98827404 -4.07399969 -8.265870
#> 11  -7.561059 -7.75351401 -4.28568299 -8.480643
#> 12  -7.706022 -7.83889673 -4.21813375 -8.473581
#> 13  -7.537845 -7.98309330 -3.94004793 -8.111801
#> 14  -7.466931 -7.99246148 -3.87223699 -8.036772
#> 15  -7.650829 -7.69241363 -3.92892819 -8.220309
#> 16  -7.603829 -7.75445331 -3.95048011 -8.258781
#> 17  -7.151658 -6.90595628 -3.39486298 -8.112696
#> 18  -7.099121 -7.03749245 -3.42422280 -7.951225
#> 19  -7.386255 -7.27193312 -3.49644728 -7.934284
#> 20  -7.143278 -7.10483695 -3.54026654 -8.135395
#> 21  -7.097380 -6.79966692 -3.18329507 -7.924504
#> 22  -7.336343 -7.02352637 -3.18666989 -7.940248
#> 23  -7.350272 -6.97224292 -3.19505905 -7.830173
#> 24  -7.173078 -6.92953717 -3.25557779 -7.929381
#> 25  -7.383290 -7.30092854 -3.57641310 -8.090767
#> 26  -7.453193 -7.21065084 -3.44738185 -8.403358
#> 27  -7.642228 -7.22628796 -3.74692803 -8.173056
#> 28  -7.299115 -7.21932547 -3.48585279 -8.054155
#> 29  -7.159441 -7.19566618 -3.51864014 -8.054212
#> 30  -7.355388 -7.06952266 -3.57206520 -8.211042
#> 31  -7.530940 -7.10686497 -3.51955207 -8.312959
#> 32  -7.661988 -6.93491520 -3.43447928 -8.251999
#> 33  -1.844555 -1.04290494  1.03803316  2.468671
#> 34  -1.894925 -1.21731165  1.06595489  2.251006
#> 35  -2.030665 -0.74341110  1.21038452  2.778374
#> 36  -1.991029 -0.76281866  1.26834385  2.726389
#> 37  -1.586868 -1.38707402  0.87759006  2.178879
#> 38  -1.552982 -1.30763095  0.99259123  1.949740
#> 39  -1.848414 -1.26586441  1.15924369  2.499329
#> 40  -1.637320 -1.22573433  1.35890467  2.579197
#> 41  -2.768460 -0.96453418 -0.06793758  1.610002
#> 42  -2.836483 -0.89052998 -0.18784656  1.744210
#> 43  -3.105718 -0.66044381 -0.35400205  1.799579
#> 44  -3.203142 -0.58006097 -0.44521436  1.742590
#> 45  -2.622591 -1.10559143 -0.15266826  1.709793
#> 46  -2.657812 -1.08947159  0.11601985  1.640287
#> 47  -2.836352 -1.10281490 -0.47297490  1.427039
#> 48  -2.829936 -1.04011156 -0.29230433  1.530826
#> 49  -1.721788 -1.10815215  1.24248815  2.293656
#> 50  -1.597092 -0.96236510  1.36322001  1.916581
#> 51  -1.646256 -0.91514231  1.35019261  2.473820
#> 52  -1.581155 -0.84104088  1.43849232  2.014578
#> 53  -1.445202 -1.23209979  1.23695469  2.012062
#> 54  -1.347159 -1.12292219  1.28770735  1.721182
#> 55  -1.343237 -1.16456789  1.34044411  2.058444
#> 56  -1.402121 -1.04971562  1.33875057  1.748518
#> 57  -2.159157 -1.16898475  0.56281654  1.874829
#> 58  -2.087334 -0.89802617  0.76577799  1.773909
#> 59  -2.133166 -1.29417973  0.64104079  1.833269
#> 60  -1.976745 -0.96805896  0.79273967  1.642469
#> 61  -1.947080 -1.25979011  0.45455005  1.709792
#> 62  -1.808467 -1.03041947  0.73785109  1.392100
#> 63  -1.880543 -1.38963769  0.68738869  1.563035
#> 64  -1.927964 -1.05505821  0.71240505  1.533037
#> 65  -9.349573 18.51018519  2.84037400  1.734092
#> 66  -9.231415 18.66294911  2.69242855  1.762992
#> 67  -8.796598 17.57164402  3.80224727  2.753445
#> 68  -8.719104 17.45826645  3.91559202  2.775338
#> 69  -9.244395 18.69407041  2.68381062  1.783429
#> 70  -9.117674 18.54942787  2.80599589  1.893048
#> 71  -8.891021 17.68311504  3.69085830  2.684595
#> 72  -8.883562 17.80044003  3.57610410  2.831710
#> 73  -9.207170 18.79559661  2.55611608  1.849539
#> 74  -9.319703 18.70173056  2.56052848  1.633185
#> 75  -8.727636 17.63100559  3.74522302  2.676892
#> 76  -8.822450 17.58109073  3.79189318  2.758334
#> 77  -9.101866 18.83734460  2.58470473  1.828574
#> 78  -9.084235 18.60868851  2.69590841  1.618814
#> 79  -8.726689 17.50998152  3.86184989  2.718337
#> 80  -8.758971 17.70301321  3.66930434  2.622840
#> 81  -2.361431 -0.15720674  1.24516140  3.226076
#> 82  -2.301916 -0.20291600  1.21276081  3.172281
#> 83  -2.576419 -0.08845250  1.25454786  3.410821
#> 84  -2.515827  0.03560128  1.12240090  3.335561
#> 85  -2.298455 -0.29634397  1.34130376  3.187503
#> 86  -2.435769 -0.23741165  1.37208962  3.311329
#> 87  -2.492401 -0.02446727  1.20263512  3.337572
#> 88  -2.668923 -0.09703857  1.26503836  3.475320
#> 89  -3.350443 -0.34481979 -0.19470225  2.087779
#> 90  -3.399585 -0.38192757 -0.29903464  2.070243
#> 91  -3.570085 -0.11626785 -0.15239144  2.320478
#> 92  -3.523809 -0.15551436 -0.04857718  2.296337
#> 93  -3.329781 -0.45517549 -0.47447307  1.899326
#> 94  -3.274401 -0.46200057 -0.31879372  1.992414
#> 95  -3.565785 -0.12303539 -0.13070258  2.314503
#> 96  -3.705151 -0.02676851 -0.26159866  2.386652
#> 97  14.600543 -3.17960153 -2.82343421  2.879040
#> 98  14.388323 -2.98014294 -2.90619573  2.951172
#> 99  14.319133 -3.04201223 -3.10747853  3.079411
#> 100 14.172337 -2.94519010 -3.17391597  3.090219
#> 101 14.634854 -3.11381596 -2.95251766  2.930081
#> 102 14.391315 -2.89886277 -2.87315085  2.872361
#> 103 14.383907 -3.23878408 -3.20547488  3.174793
#> 104 14.159485 -2.95175794 -3.11257035  3.109487
#> 105 14.013361 -2.13966024 -3.17498555  2.855503
#> 106 14.161041 -2.33912999 -3.05716547  2.870218
#> 107 13.863167 -2.22716807 -3.31827973  3.065827
#> 108 13.923721 -2.43908994 -3.32476543  3.085483
#> 109 13.860525 -2.00433353 -3.04261066  2.691761
#> 110 14.216072 -2.27064218 -3.04659495  2.889963
#> 111 13.863109 -2.33504362 -3.11146068  3.185221
#> 112 14.007917 -2.32199983 -3.38876322  3.055431
#> 113 14.726169 -3.11747121 -2.59925841  2.730143
#> 114 14.701482 -2.95403006 -2.54051092  2.643047
#> 115 14.509285 -3.29922201 -2.78246569  2.994653
#> 116 14.555373 -3.08841428 -3.12716302  3.047964
#> 117 14.539144 -2.94932361 -2.56013730  2.327946
#> 118 14.682764 -2.73137698 -2.41029287  2.180093
#> 119 14.332207 -3.25467506 -2.68525452  2.609423
#> 120 14.451270 -3.07446627 -2.60918246  2.487410
#> 121 14.005862 -2.15698375 -2.81826707  2.586575
#> 122 14.184980 -2.17036152 -2.82403428  2.704875
#> 123 13.672441 -1.93502328 -3.25707044  2.645234
#> 124 13.872759 -1.92077205 -3.06882331  2.641074
#> 125 14.156465 -2.23846738 -2.64115038  2.242410
#> 126 14.133397 -2.10861597 -2.73876824  2.268164
#> 127 14.284650 -1.83756950 -2.75209552  2.369839
#> 128 14.227024 -1.89747016 -2.82687325  2.313475
plot(x)

Ejemplo Cardiological

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   0.50482681  0.89569899  4.175064018
#> 2   0.39176790  0.19957824  3.854900350
#> 3   0.42147893  1.11595497  4.086126195
#> 4   0.33696866  0.08401944  3.577035498
#> 5   0.87120897  1.20803008  3.938785434
#> 6   0.25797245  0.19683937  3.183172711
#> 7   0.76160504  1.12654538  4.012255382
#> 8   0.43973166  0.19537999  2.905889688
#> 9   0.71381978  0.78367405  3.493104774
#> 10  0.31362586  0.20339265  2.821073396
#> 11  0.05027448 -0.39167266  1.454325805
#> 12  0.02620461 -0.43514252  1.443707687
#> 13  0.65207558  0.53322224  2.973798111
#> 14  0.66514446  0.33966776  2.664414000
#> 15  0.55638442 -0.89298985  0.735988343
#> 16  0.40192880 -0.71679407  0.967846182
#> 17  0.30708130 -0.86474184  0.493136541
#> 18 -1.20038520  0.54945830 -2.092431004
#> 19  0.16449452 -0.81882373 -0.021537837
#> 20 -0.50177793 -0.61046329 -2.341345837
#> 21  0.54965572 -0.94737083  0.457249079
#> 22 -0.84471891  0.61128043 -1.956073947
#> 23  0.19509924 -0.78605139 -0.129875596
#> 24 -0.23679378 -0.55596409 -2.414222150
#> 25  0.28100924 -0.01882712  2.285162720
#> 26 -2.06549216  1.21283911 -2.565465957
#> 27 -0.05644056 -0.49350624  0.886339905
#> 28 -2.14062426  0.60254461 -2.595513522
#> 29  1.45054823 -1.12332966  0.598217759
#> 30 -1.30458398  1.52275589 -2.188012223
#> 31  0.96257806 -1.12750470  0.104383648
#> 32 -0.90358924  1.11557288 -2.536337745
#> 33  0.34809612  0.67583651  3.926844298
#> 34  0.26728301 -0.00892598  3.653604184
#> 35  0.54915036  0.59362347  4.383613787
#> 36  0.46418569  0.13794701  3.929532511
#> 37  0.87980090  0.83465046  3.694587979
#> 38  0.36748993  0.24321503  2.772446883
#> 39  1.06135985  0.98014233  3.822205087
#> 40  0.13460346  0.27552914  2.599422341
#> 41  0.10134081 -0.69094301  0.982792619
#> 42 -1.92317931  0.89560930 -2.459376076
#> 43 -0.03574900 -0.61503130  0.308236324
#> 44 -1.58215187 -0.03189326 -2.588926664
#> 45  1.25610001 -1.16030190  0.235825579
#> 46 -1.01224475  1.25679673 -2.297400957
#> 47  0.57591307 -0.93720721 -0.169369150
#> 48 -0.53980821  0.42703690 -2.678240831
#> 49  2.73790977 -1.68387774 -0.422815564
#> 50  2.77400463 -1.60742283 -0.401588403
#> 51  2.73103202 -1.69263759 -0.087467949
#> 52  2.59888914 -1.63194637 -0.043184661
#> 53  2.88927404 -1.51765095 -0.313651876
#> 54  3.02308535 -1.84415215 -0.246736613
#> 55  2.71999222 -1.40562069  0.010239102
#> 56  2.90806980 -1.85288715  0.006106137
#> 57  0.16237839 -0.39489404  1.238317464
#> 58 -2.13584373  0.96299000 -2.315591749
#> 59 -0.28476460 -0.45493411  0.232480494
#> 60 -1.77042712  0.14467133 -2.539536502
#> 61  0.36378005 -0.75720641  1.065660877
#> 62 -1.70512086  0.96656283 -2.474564095
#> 63  0.13562691 -0.56577829  0.207870078
#> 64 -1.25772754  0.18336224 -2.780853751
#> 65  0.05757132 -0.09412350  2.325245448
#> 66 -2.13514565  1.40420568 -2.219288077
#> 67 -0.27392183 -0.56068573 -0.338081957
#> 68 -0.83205592 -0.54442424 -2.503592837
#> 69  1.48090171 -1.11265623  0.415930739
#> 70 -1.19926134  1.52816364 -1.954583996
#> 71  0.05158421 -0.63969376 -2.039220273
#> 72 -0.14693637 -0.25989545 -2.707709845
#> 73 -1.01367809  0.49010301 -1.695930790
#> 74 -1.56936264  0.67315310 -2.397082865
#> 75 -0.34653692 -0.74668140 -2.135702987
#> 76 -0.52739111 -0.56870425 -2.624396166
#> 77 -0.48554282  0.80297471 -1.899243944
#> 78 -0.59524673  0.92252843 -2.259596168
#> 79  0.07804262 -0.40726525 -2.331644228
#> 80 -0.15690570 -0.27371741 -2.843896889
#> 81 -1.99099961  1.26459207 -1.563144319
#> 82 -2.21836640  1.31602849 -2.116005441
#> 83 -1.19251848  0.26344962 -1.803760688
#> 84 -1.92133330  0.25894973 -2.648278125
#> 85 -1.12107276  1.33335041 -1.471763428
#> 86 -1.44361994  1.48130259 -2.165821238
#> 87 -0.48950454  0.32765843 -1.841735008
#> 88 -0.83212698  0.70345260 -2.702339232
plot(x)

Length of intervals

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

PCA Histogram

Hardwood Data

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

Weighted Center Matrix

weighted.center<-weighted.center.Hist.RSDA(Hardwood.histogram)

Bin Matrix

BIN.Matrix<-matrix(rep(3,length(Hardwood.cols)*length(Hardwood.names)),nrow = length(Hardwood.names))

PCA

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>

Plots

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

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: 4 unlabeled data points (too many overlaps). Consider
#> increasing max.overlaps

Hardwood.quantiles.PCA<-quantiles.RSDA(pca.hist$sym.hist.matrix.PCA,3)
#> 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

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

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
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)
sym.all.quantiles.mesh3D.plot(Hardwood.quantiles.PCA,
                               concept.names,
                               var.names,
                               Title,
                               axes.x.label,
                               axes.y.label,
                               label.name)

KS

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
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

These 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.