if (params$tipo=="pca"){
source("PCA.R")
df<-read.csv(file=params$data, header=TRUE, sep=",")
PCA(df)
} else if (params$tipo=="fa"){
source("FA.R")
df<-read.csv(file=params$data, header=TRUE, sep=",")
FA(df,params$factores)
}
## Loading required package: GPArotation
## Loading required package: psych
## Loading required package: naniar
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
## Loading required package: lmtest
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: corrplot
## corrplot 0.84 loaded
## Loading required package: ggcorrplot
## Loading required package: Amelia
## Loading required package: Rcpp
## ##
## ## Amelia II: Multiple Imputation
## ## (Version 1.7.5, built: 2018-05-07)
## ## Copyright (C) 2005-2019 James Honaker, Gary King and Matthew Blackwell
## ## Refer to http://gking.harvard.edu/amelia/ for more information
## ##
## $Importance
## [1] NA
##
## $Resumen
## Length Class Mode
## converged 1 -none- logical
## loadings 22 loadings numeric
## uniquenesses 11 -none- numeric
## correlation 121 -none- numeric
## criteria 3 -none- numeric
## factors 1 -none- numeric
## dof 1 -none- numeric
## method 1 -none- character
## rotmat 4 -none- numeric
## scores 98 -none- numeric
## STATISTIC 1 -none- numeric
## PVAL 1 -none- numeric
## n.obs 1 -none- numeric
## call 6 -none- call
##
## $Loadings
## Factor1 Factor2
## X0.48 0.49745519 0.7398665
## X5.234 0.64402212 0.5923535
## X2.62 0.03774806 -0.2962165
## X2.857 -0.84836991 -0.1316592
## X0.803 -0.55273033 -0.1605973
## X13.897 0.05471558 0.9405670
## X0.326 0.15978106 0.7942431
## X0.902 -0.95140782 0.2997201
## X0.164 0.17977452 0.5237552
## X0.183 0.74847175 0.3609033
## X4.155 0.55845733 0.6691403
##
## $Varianza_especifica
## X0.48 X5.234 X2.62 X2.857 X0.803 X13.897 X0.326
## 0.2051358 0.2343530 0.9108228 0.2629376 0.6687133 0.1123367 0.3436487
## X0.902 X0.164 X0.183 X4.155
## 0.0050000 0.6933681 0.3095375 0.2403776
##
## $Scores
## Factor1 Factor2
## [1,] -3.02537703 -0.447358676
## [2,] -1.78976173 0.834638030
## [3,] 0.11962828 0.795487721
## [4,] 1.40932612 -0.501041347
## [5,] 0.63155319 -1.543494691
## [6,] -1.80373238 1.690836927
## [7,] 0.92742186 -0.096377536
## [8,] 0.39237170 0.646099416
## [9,] -0.29450008 -0.312209905
## [10,] 1.31646169 0.772567290
## [11,] 1.06990395 0.062084460
## [12,] -0.42715825 0.444023364
## [13,] 0.62336837 -1.511973319
## [14,] -0.33937183 0.797578092
## [15,] 0.25922258 -0.295146642
## [16,] 1.10722406 0.001961947
## [17,] 0.01428857 -1.339957393
## [18,] 0.56395811 0.948260837
## [19,] -1.20744417 -0.587128438
## [20,] -1.06231910 -0.416433760
## [21,] -0.31318856 0.413697428
## [22,] 0.02341679 0.582059211
## [23,] 0.42663007 -1.420075094
## [24,] -0.66061004 1.157287616
## [25,] -0.64558117 -1.107746814
## [26,] 2.36460244 0.597293850
## [27,] 0.45819330 0.328724993
## [28,] -0.50867684 0.098545500
## [29,] -0.23640579 -1.182868701
## [30,] 0.43842540 1.788580984
## [31,] -0.25066112 0.458414833
## [32,] -0.40496897 0.960973400
## [33,] 0.51188176 -0.962489954
## [34,] 0.45061909 -0.040012088
## [35,] -1.84807570 -1.295707065
## [36,] 1.30981193 -0.377188278
## [37,] 0.54500180 -0.953076879
## [38,] -0.30095866 1.608877517
## [39,] 0.85263823 0.778362866
## [40,] 0.83600490 1.486471078
## [41,] -0.48953892 1.141642543
## [42,] 0.19977200 0.172838158
## [43,] -1.39677391 -0.199122867
## [44,] -0.21278165 -2.071540999
## [45,] -0.27012152 -0.373826848
## [46,] -1.08956375 1.019139122
## [47,] 0.49322367 -0.547889557
## [48,] 1.36429616 -0.047719272
## [49,] -0.13167483 -1.956061057
##
## $Correlation_Matrix
## X0.48 X5.234 X2.62 X2.857 X0.803 X13.897
## X0.48 1.0000000 0.8256218 -0.2378719 -0.4925323 -0.41674889 0.7045103
## X5.234 0.8256218 1.0000000 -0.1801430 -0.6778409 -0.49327504 0.5902310
## X2.62 -0.2378719 -0.1801430 1.0000000 0.1581413 0.51524745 -0.2404619
## X2.857 -0.4925323 -0.6778409 0.1581413 1.0000000 0.61191013 -0.1812025
## X0.803 -0.4167489 -0.4932750 0.5152475 0.6119101 1.00000000 -0.1293401
## X13.897 0.7045103 0.5902310 -0.2404619 -0.1812025 -0.12934014 1.0000000
## X0.326 0.6215165 0.4982606 -0.1913915 -0.2191600 -0.19558872 0.7800444
## X0.902 -0.2511038 -0.4346781 -0.1279745 0.7677143 0.47540274 0.2299319
## X0.164 0.4838684 0.4559774 0.0348619 -0.3262001 0.02991624 0.5172076
## X0.183 0.6029664 0.6135842 0.1269604 -0.6779331 -0.41050328 0.4092517
## X4.155 0.8085303 0.7367836 -0.2380837 -0.4785991 -0.47544170 0.6547146
## X0.326 X0.902 X0.164 X0.183 X4.155
## X0.48 0.62151648 -0.25110378 0.48386843 0.6029664 0.8085303
## X5.234 0.49826060 -0.43467810 0.45597737 0.6135842 0.7367836
## X2.62 -0.19139147 -0.12797452 0.03486190 0.1269604 -0.2380837
## X2.857 -0.21916004 0.76771429 -0.32620012 -0.6779331 -0.4785991
## X0.803 -0.19558872 0.47540274 0.02991624 -0.4105033 -0.4754417
## X13.897 0.78004440 0.22993192 0.51720759 0.4092517 0.6547146
## X0.326 1.00000000 0.08565933 0.41872415 0.5025315 0.6597242
## X0.902 0.08565933 1.00000000 -0.01299747 -0.6054592 -0.3317504
## X0.164 0.41872415 -0.01299747 1.00000000 0.3789181 0.3668637
## X0.183 0.50253148 -0.60545918 0.37891810 1.0000000 0.6625745
## X4.155 0.65972417 -0.33175041 0.36686367 0.6625745 1.0000000
##
## $pltcorr
