Trabajo grado

Simulación 2 factores, 8 ítems, 4 opciones de respuesta

Se crea la matriz de correlación con el paquete LikertMakerR

library(LikertMakeR)

Warning: package 'LikertMakeR' was built under R version 4.3.3

items281 <- 8
alpha281 <- 0.99
variance281 <- 0.3
set.seed(281)
cor_matrix281 <- makeCorrAlpha(items = items281, alpha = alpha281, variance = variance281)

correlation values consistent with desired alpha in 1718 iterations
The correlation matrix is positive definite

print(cor_matrix281)

          [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]
[1,] 1.0000000 0.8935159 0.8977847 0.9004613 0.9015496 0.9041765 0.9044367
[2,] 0.8935159 1.0000000 0.9080256 0.9082028 0.9131462 0.9133281 0.9144172
[3,] 0.8977847 0.9080256 1.0000000 0.9249900 0.9291429 0.9300132 0.9321426
[4,] 0.9004613 0.9082028 0.9249900 1.0000000 0.9377217 0.9391159 0.9405242
[5,] 0.9015496 0.9131462 0.9291429 0.9377217 1.0000000 0.9422426 0.9475461
[6,] 0.9041765 0.9133281 0.9300132 0.9391159 0.9422426 1.0000000 0.9498002
[7,] 0.9044367 0.9144172 0.9321426 0.9405242 0.9475461 0.9498002 1.0000000
[8,] 0.9057662 0.9245651 0.9347412 0.9419375 0.9482656 0.9594692 0.9598167
          [,8]
[1,] 0.9057662
[2,] 0.9245651
[3,] 0.9347412
[4,] 0.9419375
[5,] 0.9482656
[6,] 0.9594692
[7,] 0.9598167
[8,] 1.0000000

Se crean 6 modelos con el paqute lavaan, combinando las posibles cargas factoriales: [0.3-0.5] o [0.51 - 0.7] y la correlación entre factores: 0.0, 0.3 o 0.5.

library(lavaan)

Warning: package 'lavaan' was built under R version 4.3.2

This is lavaan 0.6-16
lavaan is FREE software! Please report any bugs.

twof_cor0_load03_05 <- '
  F1 =~ 0.31*x1 + 0.35*x2 + 0.4*x3+0.45*x4
  F2 =~ 0.5*x5+0.45*x6+0.40*x7+0.35*x8
F1 ~~ 0*F2'

twof_cor0_load05_07 <- '
  F1 =~ 0.51*x1 + 0.55*x2 + 0.6*x3+0.65*x4
  F2 =~ 0.7*x5+0.65*x6+0.6*x7+0.55*x8
F1 ~~ 0*F2'


twof_cor03_load03_05 <- '
  F1 =~ 0.31*x1 + 0.35*x2 + 0.4*x3+0.45*x4
  F2 =~ 0.5*x5+0.45*x6+0.40*x7+0.35*x8
F1~~0.3*F2'

twof_cor03_load05_07 <- '
  F1 =~ 0.51*x1 + 0.55*x2 + 0.6*x3+0.65*x4
  F2 =~ 0.7*x5+0.65*x6+0.6*x7+0.55*x8
F1~~0.3*F2'

twof_cor05_load03_05 <- '
  F1 =~ 0.31*x1 + 0.35*x2 + 0.4*x3+0.45*x4
  F2 =~ 0.5*x5+0.45*x6+0.40*x7+0.35*x8
F1~~0.5*F2'

twof_cor05_load05_07 <- '
  F1 =~ 0.51*x1 + 0.55*x2 + 0.6*x3+0.65*x4
  F2 =~ 0.7*x5+0.65*x6+0.6*x7+0.55*x8
F1~~0.5*F2'

Posterior, se guardan los modelos en un objeto

mod2f<- c(twof_cor0_load03_05, twof_cor0_load05_07, twof_cor03_load03_05, twof_cor03_load05_07,
          twof_cor05_load03_05, twof_cor05_load05_07)

Para la creación de una función que permita obtener de manera directa las matrices de correlación, primero es necesario crear una serie de objetos vacíos y otros con un valor prefijado

library(doParallel)

Warning: package 'doParallel' was built under R version 4.3.3

Loading required package: foreach

Warning: package 'foreach' was built under R version 4.3.3

Loading required package: iterators

Loading required package: parallel

library(foreach)
seeds2100<-list(NULL) # semillas aleatorios
data2100 <- list(NULL) # primeras bases de datos con base a los modelos lavaan
iter<-300 # itereaciones 
registerDoParallel(cores = 2) # cores para el desarrollo
cor_matrices2100 <- list(NULL) # objeto para guardar las primeras matrices de correlacion
n2100 <- 100 # Tamaño de muestra
lower2100 <- 1 # limite inferior - opciones de respuesta
upper2100 <- 4 # limite superior - opciones de respuesta
dfMeans2100 <- rep(2.5, 8) # media opciones de respuesta
dfSds2100 <- rep(1, 8) # desviacion estadar opciones de respuesta
basef2100 <- list(NULL) # base final simulada valores entre 1 y 4
cor_pearson2100 <- list(NULL) # matrices de pearson
cor_spearman2100 <- list(NULL) # matrices de spearman 

Teniendo en cuenta que un punto del código se debe volver a obtener una nueva base de datos desde una segunda matriz de correlacion, fue necesario crear una funcion para limpiar la matriz y volver a estimar una nueva base de datos.

clean_matrix <- function(mat) {
  if (is.matrix(mat)) {
    # Eliminar nombres de filas y columnas
    rownames(mat) <- NULL
    colnames(mat) <- NULL
    # Asegurarse de que todos los elementos sean numéricos
    as.matrix(mat)
  } else {
    stop("El objeto no es una matriz.")
  }
}

Se procede a crear el codigo para la generacion de las matrices de pearson y spearman

for (i in 1:iter) {
  # Guardar la semilla
  seeds2100[[i]] <- .Random.seed
  
  # Generar datos acorde a cada modelo lavaan
  data2100[[i]] <- foreach(b = 1:6, .combine = list, .multicombine = TRUE) %dopar% {
    lavaan::simulateData(model = mod2f[[b]], sample.nobs = n2100, model.type = "cfa", 
                         ov.var = cor_matrix281, return.type = "data.frame", 
                         return.fit = FALSE, standardized = FALSE)
  }
  
  # Limpiar y redondear las matrices de correlación
  cor_matrices2100[[i]] <- lapply(data2100[[i]], function(df) {
    cor_matrix <- cor(as.matrix(df))
    clean_matrix(cor_matrix)
  })
  
  # Creando base de datos estilo Likert
  basef2100[[i]] <- foreach(c = 1:6, .combine = list, .multicombine = TRUE) %dopar% {
    LikertMakeR::makeItems(
      n = n2100, means = dfMeans2100, sds = dfSds2100,
      lowerbound = rep(lower2100, 8), upperbound = rep(upper2100, 8),
      cormatrix = cor_matrices2100[[i]][[c]]
    )
  }
  
  # Creando las nuevas matrices de correlación
  cor_pearson2100[[i]] <- foreach(dataset = basef2100[[i]], .combine = 'list', .multicombine = TRUE) %dopar% {
    cor_matrixp <- cor(as.matrix(dataset)) 
    clean_matrix(cor_matrixp)
  }
  
  cor_spearman2100[[i]] <- foreach(dataset = basef2100[[i]], .combine = 'list', .multicombine = TRUE) %dopar% {
    cor_matrixsp <- cor(as.matrix(dataset), method = "spearman") 
    clean_matrix(cor_matrixsp)
  }
}

Análisis paralelo (AP)

Matriz de correlación de Pearson

library(psych)

Warning: package 'psych' was built under R version 4.3.2

Attaching package: 'psych'

The following object is masked from 'package:lavaan':

    cor2cov

The following object is masked from 'package:LikertMakeR':

    alpha

library(doRNG)

Warning: package 'doRNG' was built under R version 4.3.3

Loading required package: rngtools

Warning: package 'rngtools' was built under R version 4.3.3

fa_parallel_pearson1 <- foreach(j = 1:6, .combine = 'list', .packages = c("psych", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "psych", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    res <- fa.parallel(cor_pearson2100[[i]][[j]], fa = "fa", fm = "pa", n.obs = n2100)
    res$nfact  
  }
fa_parallel_pearson1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 3 3 3 1 3 0 3 2 3 1 2 3 3 2 0 2 0 2 3 1 0 2 1 3 6 0 3 4 2 3 3 2 4 0 0 4 2
 [38] 4 2 0 2 0 0 1 2 0 4 3 3 2 3 5 0 4 7 4 3 4 0 2 2 2 2 5 1 3 2 2 2 4 1 1 3 2
 [75] 3 2 3 3 0 4 3 1 2 2 3 4 3 5 2 0 2 0 3 2 2 2 1 0 2 2 4 2 1 5 4 3 2 2 5 1 3
[112] 3 3 3 2 1 3 3 2 1 4 2 2 2 3 3 4 2 0 1 2 0 4 2 2 0 1 2 3 1 2 3 1 2 4 2 4 2
[149] 2 2 3 3 2 3 2 2 2 4 2 3 3 2 2 4 2 4 2 4 3 2 2 4 2 2 2 3 3 3 2 2 1 2 2 3 2
[186] 2 3 3 1 4 3 4 3 1 3 2 4 3 4 5 0 4 3 2 2 3 3 1 3 3 4 5 3 2 2 1 0 5 2 2 0 0
[223] 3 2 0 2 3 3 1 3 0 0 5 2 1 2 0 1 3 3 2 1 3 5 0 4 3 0 2 2 4 0 3 3 3 2 2 2 2
[260] 0 3 2 2 2 4 2 2 3 2 4 1 5 3 3 4 3 1 5 0 1 0 2 2 3 3 3 0 4 5 0 1 2 4 3 2 0
[297] 3 2 4 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 3 3 3 2 3 2 3 2 3 2 2 2 2 2 3 2 3 3 3 2 2 2 2 2 4 2 4 2 2
 [38] 2 2 2 2 4 2 2 2 2 4 2 2 4 2 2 2 2 2 2 4 2 3 3 2 2 2 2 2 2 3 2 2 2 2 3 2 2
 [75] 2 2 2 2 2 2 2 2 3 2 3 2 2 2 3 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 4 3 3 2 2 2 2
[112] 2 2 2 2 2 2 2 4 2 2 2 3 3 2 2 2 2 2 2 2 4 2 2 2 4 4 3 3 2 2 3 2 2 2 3 2 3
[149] 3 2 2 2 2 2 2 3 2 4 3 2 2 2 2 3 3 2 2 2 2 2 2 2 4 3 2 2 2 3 2 3 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 3 2 2 2 3 2 3 2 2 2 2 2 3 2 2 2 2 2 3 2 3 2 3 2 5 2 2 2
[223] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 3 2 2 2 2 2 2 2 3 2
[260] 3 2 2 2 2 2 2 3 2 3 2 3 3 4 3 5 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 2 2 2 2 2 3
[297] 2 2 2 3


[[1]][[1]][[1]][[2]]
  [1] 4 3 0 2 3 3 0 4 2 0 3 2 4 0 2 1 2 2 4 3 1 3 3 2 2 2 2 3 1 3 3 4 4 0 5 4 2
 [38] 3 3 2 2 3 2 2 1 2 1 3 1 2 1 4 4 2 1 2 2 1 1 0 3 3 3 4 3 3 4 2 3 2 1 5 2 3
 [75] 2 2 2 0 3 2 0 2 2 4 2 2 1 0 2 3 2 3 2 0 0 0 0 2 2 3 4 2 3 2 3 3 3 3 2 2 3
[112] 1 2 5 3 4 2 2 2 2 2 4 1 4 2 3 4 1 1 3 1 2 1 1 2 2 0 0 3 3 2 0 3 3 0 2 0 1
[149] 4 3 3 3 4 3 0 2 5 2 3 3 4 1 1 3 0 4 4 2 1 4 0 4 2 0 5 3 2 2 2 3 2 3 3 0 2
[186] 1 1 3 4 1 2 3 0 2 0 4 2 0 4 2 3 4 3 2 2 3 4 2 5 2 5 2 3 2 2 2 2 0 4 2 0 1
[223] 2 4 2 3 0 2 3 2 2 2 2 1 1 3 5 4 3 5 3 3 4 1 4 2 3 2 3 0 2 0 1 0 4 2 0 1 4
[260] 3 1 2 2 4 1 3 0 3 5 2 5 4 3 0 1 0 3 4 4 0 4 2 2 0 0 0 2 1 0 3 2 4 2 2 1 0
[297] 2 3 2 3


[[1]][[1]][[2]]
  [1] 2 3 3 2 2 2 2 2 2 2 2 2 4 2 2 2 2 4 3 2 2 2 2 2 2 3 2 2 2 2 2 2 4 2 2 2 2
 [38] 3 2 2 3 2 2 4 2 3 2 2 2 3 2 2 2 2 2 3 2 2 2 2 3 2 2 2 3 2 2 2 2 2 2 2 3 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 4 3 2 2 3 2 2 3 2 2 2 2 2 4 4 3 3 2 2 2 2 3 3 2 2
[112] 2 3 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 2 4 3 2 2 2 3 2 2 3 2 2 2 2 2 2 2 3 2 3
[149] 3 2 3 2 4 2 2 2 2 3 2 2 2 2 2 5 3 2 3 2 2 2 2 2 4 2 2 2 2 2 2 2 3 3 2 2 4
[186] 2 4 3 4 2 2 3 2 2 3 2 2 2 2 3 3 2 2 2 2 2 2 2 2 3 2 2 4 2 2 2 2 2 3 2 2 2
[223] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 2 3 3 2 2 2 2 3 2 2 3 2 2 3 2 2 2 2
[260] 2 1 2 3 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 3 2 3 2 2 2 3 3 2 2
[297] 2 3 2 2


[[1]][[2]]
  [1] 3 3 4 2 0 0 2 4 3 2 4 3 2 1 1 2 3 0 0 1 2 2 4 2 1 1 3 3 2 1 2 0 3 2 3 4 3
 [38] 3 1 2 3 2 4 1 0 3 4 2 3 0 4 2 1 2 2 3 3 4 2 0 3 0 2 0 1 3 1 3 3 2 2 2 4 2
 [75] 2 3 4 2 1 1 3 4 3 4 1 3 1 5 2 4 2 1 1 0 1 3 2 2 0 2 2 3 1 0 4 3 5 4 0 2 1
[112] 1 2 5 1 1 2 3 4 1 3 3 1 2 1 3 5 2 1 5 3 3 3 2 0 2 1 4 1 1 1 2 3 2 4 2 3 3
[149] 4 2 1 0 4 1 2 2 2 2 1 2 4 1 2 2 3 1 4 2 0 4 3 2 2 2 4 2 1 4 3 3 2 3 4 1 2
[186] 2 1 3 2 4 4 2 2 1 4 4 4 3 4 3 2 1 3 5 1 2 1 2 1 1 4 3 2 2 5 2 4 0 1 2 1 2
[223] 3 1 2 0 3 2 1 3 1 0 4 3 3 1 1 1 3 2 2 2 5 0 1 2 2 0 3 1 2 3 4 1 1 2 0 1 1
[260] 3 3 0 1 2 4 1 0 4 3 5 3 4 2 1 5 1 4 1 5 1 2 3 5 2 1 2 2 1 3 4 1 2 2 1 2 3
[297] 3 2 2 0


[[2]]
  [1] 2 3 3 2 3 2 2 3 3 2 2 2 3 2 3 2 2 2 3 2 2 3 2 2 2 2 3 3 2 3 3 1 2 2 3 2 1
 [38] 2 2 2 2 2 2 2 3 3 2 2 3 3 3 3 1 2 2 2 3 2 3 3 2 3 2 3 2 2 1 2 2 2 2 2 3 2
 [75] 3 2 4 3 2 1 2 4 2 3 2 2 2 5 2 2 2 1 2 2 3 2 2 4 2 3 1 2 2 2 4 3 2 3 1 2 2
[112] 2 3 2 2 3 2 2 2 3 2 2 2 3 2 2 1 2 3 2 2 2 2 2 3 1 2 2 1 4 3 2 2 2 2 2 2 2
[149] 2 2 2 3 2 1 4 3 3 1 3 2 2 2 4 2 3 2 3 2 5 2 2 2 2 2 4 2 3 5 1 2 2 2 2 2 3
[186] 3 2 2 2 2 2 2 2 1 2 2 3 2 3 3 2 2 2 2 2 2 2 1 2 2 2 3 2 2 2 2 4 4 2 3 2 2
[223] 2 2 2 2 2 3 2 2 3 2 2 2 3 2 3 2 2 2 2 2 3 2 2 3 2 3 5 2 4 2 2 2 3 3 2 1 2
[260] 2 3 2 2 2 4 2 4 2 2 3 2 2 2 2 3 2 2 2 3 2 3 4 1 2 2 2 2 2 1 2 2 2 2 2 2 2
[297] 2 2 2 3

Matrices de correlación de Spearman

fa_parallel_spearman1 <- foreach(j = 1:6, .combine = 'list', .packages = c("psych", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "psych", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    res <- fa.parallel(cor_spearman2100[[i]][[j]], fa = "fa", fm = "pa", n.obs = n2100)
    res$nfact  
  }
fa_parallel_spearman1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 3 3 3 1 3 3 0 2 5 1 2 3 3 2 0 3 0 2 3 1 0 2 1 3 6 0 3 4 3 3 3 2 4 0 0 4 2
 [38] 4 2 0 2 0 0 1 4 0 4 3 4 2 3 5 0 4 7 4 3 4 0 2 2 2 4 5 1 0 3 2 2 4 1 1 3 4
 [75] 3 2 3 3 0 4 3 1 2 4 3 4 3 5 2 0 2 0 3 2 2 2 1 0 2 2 3 2 1 5 4 3 2 2 5 1 3
[112] 3 3 3 2 1 3 3 2 1 4 2 2 2 2 3 2 2 0 1 2 0 4 0 2 0 1 2 3 1 2 3 1 2 4 2 4 2
[149] 2 2 3 3 2 3 2 2 2 4 2 3 3 2 2 4 2 4 2 4 3 2 4 2 2 2 6 3 3 4 2 2 1 2 2 3 2
[186] 2 3 3 1 4 3 4 3 1 3 2 0 3 4 5 0 0 3 2 2 3 3 1 3 3 4 5 3 2 2 1 2 5 2 2 0 0
[223] 3 2 0 2 3 3 2 3 1 0 5 2 1 2 0 5 3 3 2 1 3 5 0 3 4 0 2 2 4 0 3 1 3 2 2 2 2
[260] 0 4 2 2 2 4 4 2 4 2 4 2 5 3 3 4 3 1 5 0 1 0 2 2 3 3 3 0 4 5 0 1 2 4 3 2 0
[297] 3 2 4 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 3 3 3 2 3 2 3 2 3 2 2 2 2 2 3 2 3 3 3 2 2 2 2 2 3 2 4 2 2
 [38] 2 2 2 2 4 2 2 2 2 3 2 2 3 2 2 2 2 2 2 4 2 3 3 2 2 2 2 2 2 3 2 2 2 2 3 2 2
 [75] 2 2 2 2 2 2 2 2 3 2 3 2 2 2 3 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 4 3 3 2 2 2 2
[112] 2 2 2 2 2 2 2 4 2 2 2 2 3 2 2 2 2 2 2 2 4 2 2 2 4 4 3 3 2 2 2 2 2 2 3 2 3
[149] 4 2 2 2 2 2 2 3 2 4 2 2 2 2 2 3 3 2 2 2 3 2 2 2 4 3 2 2 2 3 2 3 2 2 3 2 2
[186] 2 2 2 2 2 2 2 2 2 3 2 2 2 3 2 3 2 2 3 2 2 3 2 2 2 2 2 3 2 3 2 3 2 3 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 3 2 2 2 2 2 2 2 3 2
[260] 3 2 2 2 2 2 2 2 2 3 2 3 3 4 2 5 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 2 2 2 2 2 3
[297] 2 2 2 2


[[1]][[1]][[1]][[2]]
  [1] 4 3 0 2 3 3 0 2 2 0 4 2 4 0 2 1 2 2 4 3 1 3 3 2 2 2 2 3 3 3 3 1 0 0 5 5 2
 [38] 3 3 2 2 3 2 2 1 2 1 3 1 0 1 4 4 2 1 2 2 1 1 0 3 3 3 4 5 3 4 2 3 2 1 3 1 3
 [75] 1 2 2 0 2 2 0 2 2 2 2 2 1 0 0 3 2 3 2 0 1 0 0 2 2 3 3 3 3 2 3 3 3 3 2 2 3
[112] 1 2 5 3 4 2 2 2 2 2 0 1 4 2 3 1 4 1 3 1 2 1 1 2 2 0 0 3 0 2 0 3 4 0 4 0 1
[149] 2 3 3 3 4 3 0 2 5 2 3 3 4 2 1 3 0 4 4 2 1 5 0 4 2 0 5 3 2 2 2 3 2 3 3 0 2
[186] 1 1 3 4 4 2 3 0 3 0 4 2 0 3 6 3 4 3 2 2 3 4 2 0 2 5 2 3 2 2 2 2 0 5 2 0 1
[223] 2 4 2 3 0 2 3 2 2 2 2 1 4 3 5 4 3 5 3 0 4 1 4 2 3 2 3 0 2 0 1 0 4 2 0 1 4
[260] 3 1 2 2 3 1 3 0 3 2 2 5 4 3 0 1 0 3 4 4 5 4 2 2 0 0 0 2 1 0 0 3 4 2 2 0 0
[297] 2 3 2 3


[[1]][[1]][[2]]
  [1] 2 3 3 2 2 2 2 2 2 2 2 2 4 3 2 2 2 2 3 2 3 2 2 2 2 3 2 2 2 2 2 2 4 2 2 2 2
 [38] 3 2 2 3 2 2 4 3 3 2 2 2 3 2 2 2 2 2 3 2 2 2 2 3 2 2 2 3 2 2 2 2 2 3 2 3 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 5 3 2 2 3 2 2 3 2 2 2 2 2 4 4 3 3 2 2 2 2 3 3 2 2
[112] 2 3 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 2 4 3 2 2 2 3 2 2 3 2 2 2 2 2 2 2 3 3 3
[149] 3 2 3 2 4 3 2 2 2 3 2 2 2 2 2 5 2 2 3 2 2 2 2 2 4 2 2 2 2 2 2 2 3 3 2 2 4
[186] 2 3 3 2 2 2 3 2 2 3 2 2 2 2 3 3 2 2 2 2 2 2 2 2 3 2 2 4 2 2 2 2 3 4 2 2 2
[223] 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 2 3 2 2 3 2 2 2 2 3 2 2 3 2 2 3 2 2 2 2
[260] 2 1 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 3 3 2 3 2 2 2 3 3 2 3
[297] 2 3 2 2


[[1]][[2]]
  [1] 3 3 4 2 0 2 2 4 3 2 6 4 2 1 1 2 3 0 0 1 1 2 4 2 1 3 2 3 2 3 2 0 3 0 3 4 3
 [38] 3 1 2 3 2 4 2 0 3 3 2 3 0 4 2 1 2 2 3 3 4 2 0 3 0 2 0 1 3 1 3 3 2 2 2 4 2
 [75] 2 3 4 0 1 1 3 4 3 3 1 3 1 6 2 4 2 1 1 0 1 3 1 2 0 2 2 3 1 0 4 3 5 4 0 2 1
[112] 1 2 5 1 1 2 3 4 1 3 3 1 2 1 3 5 2 1 3 3 3 3 2 0 2 2 4 1 1 1 2 3 2 4 2 3 2
[149] 3 2 1 0 4 1 2 2 2 2 1 2 2 1 2 2 3 1 5 2 0 4 5 2 2 2 4 3 1 4 3 3 4 3 4 1 2
[186] 2 1 3 2 4 4 2 2 1 4 2 4 4 4 3 2 1 2 5 1 2 1 2 1 1 2 3 3 2 5 2 4 0 1 2 1 2
[223] 3 3 2 4 3 3 1 3 1 0 1 3 3 1 1 1 2 2 2 2 5 2 1 2 2 0 3 1 2 3 4 1 1 2 0 1 1
[260] 2 4 0 1 3 4 1 0 4 3 5 3 4 2 1 5 1 4 1 5 1 2 3 4 2 1 2 2 1 3 4 1 2 2 1 2 3
[297] 3 2 4 0


[[2]]
  [1] 2 3 3 2 3 2 4 3 2 2 2 2 3 2 3 3 2 2 3 2 2 3 2 2 2 2 3 3 2 3 3 1 2 2 3 2 1
 [38] 2 4 2 2 2 2 2 3 3 2 2 3 2 3 3 1 2 2 2 3 2 3 4 2 3 2 3 2 2 3 2 2 2 2 2 3 3
 [75] 3 3 4 3 2 1 2 4 2 3 2 2 2 3 2 2 2 2 2 2 3 2 2 4 2 3 1 2 3 2 4 3 2 3 1 2 2
[112] 2 3 2 2 3 2 2 2 4 2 2 2 3 2 2 1 2 3 2 2 2 2 2 3 1 2 2 3 4 3 2 3 2 2 2 2 2
[149] 2 2 2 3 2 1 4 3 3 1 3 2 2 2 4 2 2 2 3 2 5 2 2 2 2 2 4 2 4 4 1 2 2 1 2 2 3
[186] 4 2 2 2 2 2 2 2 1 2 2 3 2 2 3 2 2 2 2 2 2 2 1 2 2 2 3 2 2 2 2 4 4 2 3 2 2
[223] 2 3 2 2 2 3 2 2 4 2 2 2 2 2 4 2 2 2 2 2 3 2 2 3 2 3 5 2 4 2 2 2 3 3 4 1 2
[260] 2 3 2 2 2 4 2 4 2 3 3 2 3 2 2 3 2 2 2 4 2 2 4 1 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 3

Mínimo promedio parcial (MAP)

Correlación de Pearson

library(EFA.dimensions)

Warning: package 'EFA.dimensions' was built under R version 4.3.2

**************************************************************************************************
EFA.dimensions 0.1.8.1

Please contact Brian O'Connor at brian.oconnor@ubc.ca if you have questions or suggestions.
**************************************************************************************************

MAP_pearson1 <- foreach(j = 1:6, .combine = 'list', .packages = c("EFA.dimensions", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "EFA.dimensions", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    res <- MAP(cor_pearson2100[[i]][[j]], Ncases = n2100)
    res$NfactorsMAP  
  }
MAP_pearson1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
[297] 0 0 0 0

[[1]][[1]][[1]][[1]][[2]]
  [1] 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0
 [38] 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
 [75] 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
[112] 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 2 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 1 0 1
[186] 0 0 2 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
[223] 0 0 0 0 0 2 2 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0
[260] 0 0 0 0 2 0 1 0 0 0 2 0 1 0 0 0 0 0 0 2 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
[297] 0 0 0 0


[[1]][[1]][[1]][[2]]
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[297] 0 0 0 0


[[1]][[1]][[2]]
  [1] 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 1 0
 [38] 0 0 0 1 0 1 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 0 1
 [75] 0 0 0 2 1 0 1 0 1 0 0 1 1 1 0 0 0 1 1 0 0 1 0 1 2 1 0 1 1 1 1 0 0 0 0 0 1
[112] 0 1 0 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 2 1 0 1 1 1 1
[149] 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 0 2 0 1 0 0 0 2 1 1 0 0 0 0 0 1 1 1 1 2 0
[186] 0 0 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 1 1 1 0 1 1 0 0 0 1 0
[223] 1 1 0 1 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1
[260] 0 1 1 0 1 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 0 1 1
[297] 1 1 1 1


[[1]][[2]]
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
[186] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[223] 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[297] 0 1 0 0


[[2]]
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 2 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0
 [75] 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1
[112] 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1
[149] 1 0 1 0 1 1 1 1 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0
[223] 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 0 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1
[297] 1 1 0 0

Correlación de Spearman

MAP_spearman1 <- foreach(j = 1:6, .combine = 'list', .packages = c("EFA.dimensions", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "EFA.dimensions", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    res <- MAP(cor_spearman2100[[i]][[j]], Ncases = n2100)
    res$NfactorsMAP  
  }
MAP_spearman1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
[297] 0 0 0 0

[[1]][[1]][[1]][[1]][[2]]
  [1] 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0
 [38] 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1
 [75] 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
[112] 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 2 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 1 0 0 1
[186] 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0
[223] 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0
[260] 0 0 0 0 2 0 1 0 0 0 2 0 1 0 0 0 0 0 0 2 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0
[297] 0 0 0 0


[[1]][[1]][[1]][[2]]
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[223] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[297] 0 0 0 0


[[1]][[1]][[2]]
  [1] 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 1 0
 [38] 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 0 0
 [75] 0 0 0 0 1 0 1 0 1 0 0 1 1 0 0 0 0 1 1 0 0 1 0 1 2 1 0 1 1 1 1 0 0 0 0 0 1
[112] 0 1 0 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 1 1 2 1 0 1 1 1 1
[149] 0 0 0 1 0 0 1 1 0 0 0 1 0 0 1 1 0 2 0 1 0 0 0 2 1 1 0 0 0 0 0 1 1 0 1 2 0
[186] 0 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 1 1 1 0 1 1 0 0 0 0 0
[223] 0 1 0 1 0 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 1 0 0 0 1 0 1 0 1 1 0 0 1
[260] 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 0 1 0 1 1
[297] 1 0 1 1


[[1]][[2]]
  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[112] 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0
[186] 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[223] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[297] 0 1 0 0


[[2]]
  [1] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 2 1 0 0 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0
 [75] 1 0 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1
[112] 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1
[149] 1 0 1 0 1 1 0 1 0 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 1 1 1 1 1
[186] 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0
[223] 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1
[260] 0 1 1 0 0 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1
[297] 1 1 0 0

Análisis exploratorio gráfico (AEG)

Correlación de pearson

library(EGAnet)

Warning: package 'EGAnet' was built under R version 4.3.3


EGAnet (version 2.0.6) 

For help getting started, see <https://r-ega.net> 

For bugs and errors, submit an issue to <https://github.com/hfgolino/EGAnet/issues>

EGA_pearson1 <- foreach(j = 1:6, .combine = 'list', .packages = c("EGAnet", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "EGAnet", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    res <- tryCatch(EGA(cor_pearson2100[[i]][[j]], n = n2100, plot.EGA = FALSE),
    warn=FALSE, error=function(e) return(NA))
    res$n.dim 
  }
EGA_pearson1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 0 2 0 0 0 0 3 0 0 1 0 0 2 1 0 0 0 0 2 0 0 0 0 0 2 1 0 3 2 0 0 0 0 1 0 2 0
 [38] 0 0 0 0 1 0 0 0 0 1 0 0 1 3 1 1 0 0 2 1 0 0 0 2 0 0 0 0 1 0 0 1 1 0 0 0 1
 [75] 0 0 0 0 0 0 0 0 2 0 0 2 1 0 1 0 0 0 0 2 0 0 3 1 1 2 1 1 0 0 2 1 0 1 0 0 0
[112] 3 1 0 1 1 1 2 2 2 0 1 0 0 1 0 0 1 0 0 0 0 2 0 1 0 1 0 0 0 0 0 0 0 1 2 0 1
[149] 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0 2 1 0 2 0 0 1 0 1 0 0 0 1 2 0 0 0 2 0 0
[186] 1 0 2 0 0 1 2 0 0 1 0 1 0 0 0 0 2 1 0 2 1 0 1 0 2 0 0 0 0 1 1 0 2 0 1 0 1
[223] 0 1 0 0 0 1 0 2 1 0 0 0 0 0 1 2 0 3 0 0 3 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1
[260] 0 0 0 0 1 2 0 0 0 0 2 1 0 0 0 1 1 1 1 0 1 0 0 0 2 0 0 1 0 0 0 2 0 0 1 0 0
[297] 0 0 0 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 3 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 3 0 2 2 2 2 4 2 0
 [38] 2 2 2 2 3 3 2 2 2 2 2 2 0 2 0 2 2 2 2 2 2 3 2 2 2 2 2 1 2 2 2 2 2 3 1 2 2
 [75] 2 2 2 2 0 2 3 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 0 2 2 0 2 2 2 2 2 2
[112] 2 2 1 2 2 0 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 2 3 3 0 2 2 1 2 2 2 2 2
[149] 2 2 2 2 2 2 0 2 2 2 2 2 1 2 2 2 2 2 2 2 0 2 2 2 2 1 0 2 2 2 2 2 2 2 2 1 2
[186] 2 2 2 2 2 3 2 2 2 2 2 0 2 0 2 2 2 2 2 2 2 2 2 3 2 0 2 0 2 2 2 1 2 2 2 2 2
[223] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 0 2 2 2 2 1 2 2 2 2 2 2 2
[260] 2 2 2 0 2 2 2 3 2 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 0 2 2 2 0 2
[297] 2 3 2 2


[[1]][[1]][[1]][[2]]
  [1] 1 1 0 0 0 1 0 2 0 1 0 0 2 1 0 0 0 0 1 1 0 2 0 0 0 0 1 1 2 0 1 1 0 0 0 1 0
 [38] 1 2 1 0 1 3 0 1 0 0 0 1 0 2 0 0 0 0 1 0 0 0 0 1 0 2 0 0 2 1 0 2 0 1 0 0 0
 [75] 0 0 0 0 0 0 0 0 2 2 1 0 1 0 0 1 0 1 0 2 2 1 0 0 1 1 0 0 1 0 0 1 1 1 1 2 0
[112] 2 0 0 0 3 0 0 1 2 0 0 0 1 0 0 1 0 0 2 1 1 0 2 1 0 1 0 1 0 2 0 0 0 0 1 0 2
[149] 2 1 0 2 0 1 0 0 0 0 0 0 0 1 0 1 1 3 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 2
[186] 2 1 0 0 0 3 0 1 0 0 0 1 2 0 0 1 1 0 0 0 1 1 0 0 0 1 2 0 1 0 1 0 0 0 0 1 0
[223] 0 0 0 0 0 2 0 0 1 0 2 0 0 3 0 1 1 1 1 2 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1
[260] 0 0 2 2 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 0 0 2 0 0 1 3 0 2 1 1 0 1 0 2 1 0 0
[297] 0 0 0 1


[[1]][[1]][[2]]
  [1] 2 2 1 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 0
 [38] 2 2 2 2 2 2 3 0 2 0 0 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 0 2 2 1 2 2 2 2 2 2 2
 [75] 2 1 2 2 2 0 3 2 1 2 2 2 3 3 2 2 2 2 2 3 0 2 1 2 2 2 0 2 2 2 2 2 1 3 0 2 2
[112] 2 2 2 2 2 0 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 0 2 2 0 2 2 1 2
[149] 2 1 2 2 2 0 2 2 2 2 2 2 2 3 2 3 2 2 0 0 2 2 2 2 2 2 2 2 0 2 2 2 2 2 2 2 0
[186] 0 2 2 2 2 2 2 2 1 4 2 2 2 2 2 0 2 0 2 1 2 2 2 2 2 2 0 2 2 2 2 2 2 0 2 0 2
[223] 2 2 2 2 2 0 2 2 2 1 3 2 2 2 2 2 2 2 2 2 1 2 2 2 2 0 2 2 0 2 2 2 3 2 2 2 2
[260] 1 0 0 2 2 0 2 2 2 2 2 2 2 2 2 2 0 0 2 2 0 2 2 3 2 2 1 0 2 2 2 1 0 2 2 2 2
[297] 2 2 2 2


[[1]][[2]]
  [1] 0 1 2 0 0 0 2 0 0 1 0 2 0 3 2 1 1 0 0 1 0 2 0 0 0 0 0 2 0 0 0 1 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 1 0 2 0 1 1 0 0 0 3 0 0 2 0 0 0 0 0 0 0 0 1 2 0 0 0 2 0
 [75] 1 0 0 1 0 0 0 0 0 0 1 2 1 0 0 1 2 1 0 0 1 1 0 1 1 0 1 2 0 0 0 1 0 0 0 1 0
[112] 1 0 0 0 2 2 0 0 1 0 2 0 1 1 0 0 0 0 0 2 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 2 0
[149] 1 1 2 0 2 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 2 3 1 0 0
[186] 1 0 3 0 1 0 2 0 1 0 0 0 0 0 0 0 1 2 0 0 1 0 2 0 0 0 0 0 0 0 1 0 0 0 1 0 2
[223] 0 0 1 1 2 0 0 3 1 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 0 0 0 2 0 0 2 0 0 0 0 0
[260] 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 3 0 0 0 1 0 2 0 0 2 2 0 0 0 0 1 0 0 0 0
[297] 0 2 0 1


[[2]]
  [1] 2 3 2 0 2 2 2 2 2 2 2 2 2 2 3 2 2 2 1 2 1 1 2 2 2 2 2 3 3 2 0 2 1 2 2 2 1
 [38] 2 3 2 1 2 2 2 2 3 2 2 2 1 2 2 1 2 2 2 2 0 2 2 2 2 2 0 1 2 3 1 2 2 0 2 2 2
 [75] 3 2 2 2 2 2 2 2 1 2 0 3 2 3 0 2 0 0 2 2 2 2 1 0 2 2 0 2 1 2 2 1 1 0 1 2 2
[112] 2 2 3 2 3 2 2 2 2 2 2 2 2 2 3 2 3 0 2 2 2 1 2 2 1 1 2 1 3 2 2 2 2 2 2 2 0
[149] 0 0 2 3 2 1 0 2 2 0 3 2 2 2 3 0 3 2 1 2 2 2 2 1 2 2 2 2 2 3 2 2 2 0 2 1 0
[186] 3 2 0 2 2 2 1 2 1 2 2 2 2 0 2 2 0 2 0 0 2 2 1 1 0 0 3 2 2 2 2 3 3 0 2 2 0
[223] 2 3 2 2 1 3 2 2 2 2 2 2 2 2 2 3 2 3 0 2 0 2 2 2 2 2 0 2 0 2 2 2 2 2 2 1 2
[260] 2 0 2 2 2 2 2 2 1 0 2 2 2 2 2 2 2 2 2 2 2 2 3 1 2 0 2 2 2 0 2 1 2 2 0 2 1
[297] 2 3 2 1

Correlación de spearman

EGA_spearman1 <- foreach(j = 1:6, .combine = 'list', .packages = c("EGAnet", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "EGAnet", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    res <- tryCatch(EGA(cor_spearman2100[[i]][[j]], n = n2100, plot.EGA = FALSE),
    warn=FALSE, error=function(e) return(NA))
    res$n.dim 
  }
EGA_spearman1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 0 2 0 0 0 1 1 2 0 1 0 0 2 1 0 1 0 0 0 1 0 0 1 0 2 0 0 3 1 0 0 0 0 0 1 2 0
 [38] 2 0 0 0 0 0 0 0 1 1 0 0 1 3 1 0 0 0 2 1 0 0 0 2 0 0 1 1 1 1 0 1 1 0 1 0 0
 [75] 0 0 0 0 0 0 2 0 0 0 0 0 1 0 1 0 0 0 0 2 0 0 3 0 0 0 0 1 0 0 2 1 0 0 0 0 0
[112] 3 1 0 1 1 1 2 2 0 1 1 0 0 1 0 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 2 1 1
[149] 0 0 0 1 0 0 1 0 1 1 0 1 1 0 2 0 0 2 1 0 0 0 0 0 0 0 0 1 0 1 2 0 1 0 0 0 0
[186] 1 0 2 0 0 1 2 0 0 1 0 1 0 0 0 0 2 1 0 2 0 0 1 0 0 0 0 0 0 1 0 0 2 0 1 0 0
[223] 0 1 0 0 0 0 1 1 1 0 1 0 0 0 0 2 0 3 0 0 4 1 0 0 0 0 0 1 1 0 0 1 0 0 0 2 1
[260] 0 0 1 0 1 0 0 0 0 0 2 0 0 0 0 0 1 1 1 0 0 0 0 0 2 0 0 0 0 0 0 2 0 0 1 0 0
[297] 0 0 0 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 3 2 2 0 0 2 2 0 1 2 0 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 3 0 2 2 2 2 4 2 0
 [38] 2 2 0 2 2 3 2 2 2 0 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 2 2 1 2 2 2 2 2 3 1 2 2
 [75] 2 2 2 2 0 2 2 2 2 2 2 2 2 2 3 1 2 2 2 2 2 2 2 1 2 2 2 0 2 0 0 2 2 2 2 1 2
[112] 2 2 1 2 2 0 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 0 2 2 2 2 1
[149] 4 2 2 2 2 2 0 2 2 2 0 2 1 2 2 2 2 2 2 2 0 2 2 2 2 2 0 2 2 2 2 2 2 2 2 0 2
[186] 2 2 2 2 2 3 2 2 2 2 2 0 2 0 2 2 2 2 2 2 2 2 2 3 2 0 2 0 2 2 2 0 2 0 2 2 2
[223] 0 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 0 2 2 2 1 0 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 0 2 2 2 3 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 3 0 2 2 2 0 2
[297] 2 3 2 2


[[1]][[1]][[1]][[2]]
  [1] 1 1 1 0 0 1 0 1 0 0 0 0 2 0 0 3 0 1 0 1 0 2 1 0 0 0 1 1 0 2 1 1 0 0 0 1 0
 [38] 0 2 0 0 1 3 0 1 0 0 0 0 1 2 0 0 1 1 1 0 0 0 0 0 0 2 0 0 2 1 0 2 0 0 0 0 0
 [75] 0 0 0 0 0 0 0 0 0 2 1 1 1 0 1 1 0 1 0 0 2 1 0 0 0 1 0 0 2 2 0 1 1 1 1 2 0
[112] 0 0 0 0 2 0 0 1 2 0 0 1 1 0 1 1 0 0 2 1 1 0 2 1 0 0 0 1 0 2 0 0 0 0 0 0 1
[149] 0 0 0 2 0 0 0 2 2 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 3 1 1 1 0 0 0 2
[186] 2 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 0 1 2 0 1 0 1 0 0 0 0 0 0
[223] 0 0 0 0 0 2 0 0 1 0 2 0 0 2 0 1 1 1 1 0 3 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0
[260] 0 0 2 2 0 0 1 0 0 0 0 0 2 2 0 0 0 2 0 0 0 3 2 0 0 0 0 0 1 0 0 0 0 2 0 1 0
[297] 0 0 0 1


[[1]][[1]][[2]]
  [1] 2 2 1 2 2 2 2 2 2 2 2 2 1 0 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2 1 0
 [38] 2 2 2 2 2 2 3 1 2 0 0 2 2 2 2 3 2 2 2 2 2 2 2 0 2 2 2 2 2 1 2 2 2 0 2 2 2
 [75] 2 0 2 2 2 1 3 2 1 2 2 2 3 3 2 2 2 2 2 3 0 2 1 2 2 2 0 2 2 2 2 2 1 2 1 2 2
[112] 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 3 2 2 2 2 0 2 2 2 2 2 2 2 2 0 2 2 1 2
[149] 2 1 2 2 2 0 2 2 2 2 2 2 2 3 2 2 2 2 0 3 2 2 2 2 2 2 2 2 0 2 2 2 1 2 2 2 0
[186] 0 1 2 2 2 2 3 1 0 3 2 2 2 2 2 0 2 0 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 0 2 0 2
[223] 2 2 2 2 2 0 2 2 2 1 3 2 2 2 2 2 2 2 2 2 1 2 2 2 2 0 2 2 0 2 2 2 3 2 2 2 1
[260] 1 1 0 2 2 0 2 2 1 2 2 2 2 2 2 2 0 0 2 2 1 2 2 2 2 2 0 1 2 2 2 0 0 2 0 2 2
[297] 2 2 2 2


[[1]][[2]]
  [1] 0 1 3 1 1 0 2 0 1 0 0 2 0 3 0 1 1 0 1 1 0 2 0 0 0 1 0 2 0 0 1 1 0 0 0 0 0
 [38] 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0
 [75] 1 0 0 0 0 0 0 0 1 0 1 2 1 0 0 1 2 1 0 0 0 1 0 0 0 0 1 2 0 0 0 1 0 1 1 1 0
[112] 1 0 0 0 2 2 1 0 1 0 2 0 1 1 0 0 0 0 0 2 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 2 0
[149] 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 2 3 1 0 1
[186] 1 0 3 0 1 0 2 0 1 0 0 0 0 0 0 0 0 2 2 0 2 0 2 0 0 0 0 0 0 0 1 0 3 0 1 0 2
[223] 2 1 1 0 0 0 0 3 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0 1 1 0 0 1 0 0
[260] 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 3 0 0 0 0 0 2 0 1 2 2 0 0 0 0 0 0 0 0 0
[297] 0 2 0 0


[[2]]
  [1] 2 1 2 0 2 2 2 2 2 2 2 2 2 2 3 2 2 2 0 2 0 1 2 2 2 2 2 3 2 2 0 1 2 2 2 2 0
 [38] 2 2 2 1 2 2 2 2 3 2 2 2 0 0 2 1 2 2 2 2 0 2 2 2 2 2 2 1 2 3 1 2 2 0 2 2 2
 [75] 0 2 2 2 2 2 3 2 1 2 0 3 2 3 0 0 0 1 2 2 1 2 1 0 2 2 0 2 1 2 2 1 1 0 1 2 2
[112] 2 2 1 2 3 2 2 2 2 2 1 2 2 2 3 1 3 0 0 2 2 1 2 2 1 0 2 2 3 2 2 2 2 2 3 2 0
[149] 0 0 2 3 2 1 0 2 2 2 3 2 2 2 3 0 3 2 0 2 2 2 2 1 2 2 2 2 2 3 2 2 1 0 2 1 0
[186] 3 2 0 2 0 2 0 2 1 2 2 2 2 0 2 2 0 2 0 0 2 2 1 0 0 0 2 2 2 2 2 3 3 0 2 2 2
[223] 0 3 3 2 1 3 2 2 2 2 2 2 2 2 2 3 0 3 0 2 0 2 1 2 2 2 0 2 0 2 2 2 2 2 2 1 1
[260] 2 0 2 0 2 2 2 3 1 0 2 2 2 2 2 2 2 2 2 2 2 2 3 1 2 0 2 2 2 1 2 1 2 0 0 2 0
[297] 2 3 2 0

Componentes principales categórico (Kaiser - Princals)

Correlación de Pearson

library(Gifi)

Warning: package 'Gifi' was built under R version 4.3.3

PRINCAL_pearson1 <- foreach(j = 1:6, .combine = 'list', .packages = c("Gifi", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "Gifi", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    invisible(capture.output({
  res <- princals(cor_pearson2100[[i]][[j]])
}))
    res$ndim 
  }
PRINCAL_pearson1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2

Correlación de Spearman

PRINCAL_spearman1 <- foreach(j = 1:6, .combine = 'list', .packages = c("Gifi", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "Gifi", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    invisible(capture.output({
  res <- princals(cor_spearman2100[[i]][[j]])
}))
    res$ndim 
  }
PRINCAL_spearman1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2

Estimación PRINCALS directamente en la base de datos

Teniendo en cuenta que los resultados siempre eran los mismos, ejecuté el análisis directamente a las bases de datos.

PRINCALS_base1 <- foreach(j = 1:6, .combine = 'list', .packages = c("Gifi", "foreach", "doRNG")) %:%
  foreach(i = 1:iter, .combine = 'c', .packages = "Gifi", .options.RNG = 1234) %dopar% {
    set.seed(1234 + i + j)
    invisible(capture.output({
  res <- princals(basef2100[[i]][[j]])
}))
    res$ndim
  }
PRINCALS_base1

[[1]]
[[1]][[1]]
[[1]][[1]][[1]]
[[1]][[1]][[1]][[1]]
[[1]][[1]][[1]][[1]][[1]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2

[[1]][[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[1]][[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2


[[2]]
  [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[112] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[297] 2 2 2 2

Medidas de precisión - exactitud

procesar_lista <- function(lista) {
  vector <- unlist(lista)
  split(vector, ceiling(seq_along(vector) / iter))
}

listas <- list(fa_parallel_spearman1, fa_parallel_pearson1, MAP_spearman1, MAP_pearson1, 
               EGA_pearson1, EGA_spearman1, PRINCAL_pearson1, PRINCAL_spearman1,PRINCALS_base1)

bloques <- lapply(listas, procesar_lista)

names(bloques) <- c("fa_parallel_spearman1", "fa_parallel_pearson1", "MAP_spearman1", 
                    "MAP_pearson1", "EGA_pearson1", "EGA_spearman1", 
                    "PRINCAL_pearson1", "PRINCAL_spearman1", "PRINCALS_base1")

proporcion_correcta <- function(results, correct_value = 2) {
  correct_counts <- sum(results == correct_value)
  correct_counts / length(results)
}
mbe <- function(results, correct_value = 2) {
  sum(correct_value - results) / length(results)
}
mae <- function(results, correct_value = 2) {
  mean(abs(correct_value - results))
}

resultados <- lapply(bloques, function(bloque) {
  lapply(bloque, function(data) {
    list(
      PC = proporcion_correcta(data),
      MBE = mbe(data),
      MAE = mae(data)
    )
  })
})

data_frames <- lapply(resultados, function(res) {
  do.call(rbind, lapply(res, function(r) {
    data.frame(PC = r$PC, MBE = r$MBE, MAE = r$MAE)
  }))
})

names(data_frames) <- c("fa_parallel_spearman1", "fa_parallel_pearson1", "MAP_spearman1", 
                        "MAP_pearson1", "EGA_pearson1", "EGA_spearman1", 
                        "PRINCAL_pearson1", "PRINCAL_spearman1", "PRINCALS_base1")

newnames<- c("Carga_baja_ortogonal", "Carga_alta_ortogonal", "Carga_baja_oblic_03", "Carga_alta_oblic_03", "Carga_baja_oblic_05", "Carga_alta_oblic_05")

data_frames <- lapply(data_frames, function(df) {
  rownames(df) <- newnames
  return(df)
})
data_frames

$fa_parallel_spearman1
                            PC        MBE       MAE
Carga_baja_ortogonal 0.3166667 -0.3666667 1.0866667
Carga_alta_ortogonal 0.7700000 -0.2766667 0.2766667
Carga_baja_oblic_03  0.3133333 -0.1700000 1.0633333
Carga_alta_oblic_03  0.7433333 -0.3033333 0.3100000
Carga_baja_oblic_05  0.3000000 -0.2300000 1.0100000
Carga_alta_oblic_05  0.6333333 -0.3566667 0.4633333

$fa_parallel_pearson1
                            PC        MBE       MAE
Carga_baja_ortogonal 0.3333333 -0.3233333 1.0233333
Carga_alta_ortogonal 0.7566667 -0.3033333 0.3033333
Carga_baja_oblic_03  0.3233333 -0.2233333 1.0300000
Carga_alta_oblic_03  0.7500000 -0.3000000 0.3066667
Carga_baja_oblic_05  0.2933333 -0.2066667 1.0133333
Carga_alta_oblic_05  0.6466667 -0.3000000 0.4266667

$MAP_spearman1
                              PC      MBE      MAE
Carga_baja_ortogonal 0.000000000 1.993333 1.993333
Carga_alta_ortogonal 0.030000000 1.806667 1.806667
Carga_baja_oblic_03  0.000000000 2.000000 2.000000
Carga_alta_oblic_03  0.016666667 1.530000 1.530000
Carga_baja_oblic_05  0.000000000 1.956667 1.956667
Carga_alta_oblic_05  0.006666667 1.220000 1.220000

$MAP_pearson1
                              PC      MBE      MAE
Carga_baja_ortogonal 0.000000000 1.993333 1.993333
Carga_alta_ortogonal 0.036666667 1.773333 1.773333
Carga_baja_oblic_03  0.000000000 1.996667 1.996667
Carga_alta_oblic_03  0.020000000 1.490000 1.490000
Carga_baja_oblic_05  0.000000000 1.943333 1.943333
Carga_alta_oblic_05  0.003333333 1.190000 1.190000

$EGA_pearson1
                            PC       MBE       MAE
Carga_baja_ortogonal 0.1166667 1.4600000 1.5066667
Carga_alta_ortogonal 0.8200000 0.1533333 0.2600000
Carga_baja_oblic_03  0.1166667 1.4500000 1.4900000
Carga_alta_oblic_03  0.7800000 0.2333333 0.3400000
Carga_baja_oblic_05  0.1066667 1.5266667 1.5666667
Carga_alta_oblic_05  0.6533333 0.2766667 0.4766667

$EGA_spearman1
                             PC       MBE       MAE
Carga_baja_ortogonal 0.09666667 1.5133333 1.5600000
Carga_alta_ortogonal 0.80000000 0.2100000 0.3166667
Carga_baja_oblic_03  0.11333333 1.4833333 1.5166667
Carga_alta_oblic_03  0.75666667 0.2566667 0.3500000
Carga_baja_oblic_05  0.08666667 1.5400000 1.5933333
Carga_alta_oblic_05  0.61666667 0.3733333 0.5600000

$PRINCAL_pearson1
                     PC MBE MAE
Carga_baja_ortogonal  1   0   0
Carga_alta_ortogonal  1   0   0
Carga_baja_oblic_03   1   0   0
Carga_alta_oblic_03   1   0   0
Carga_baja_oblic_05   1   0   0
Carga_alta_oblic_05   1   0   0

$PRINCAL_spearman1
                     PC MBE MAE
Carga_baja_ortogonal  1   0   0
Carga_alta_ortogonal  1   0   0
Carga_baja_oblic_03   1   0   0
Carga_alta_oblic_03   1   0   0
Carga_baja_oblic_05   1   0   0
Carga_alta_oblic_05   1   0   0

$PRINCALS_base1
                     PC MBE MAE
Carga_baja_ortogonal  1   0   0
Carga_alta_ortogonal  1   0   0
Carga_baja_oblic_03   1   0   0
Carga_alta_oblic_03   1   0   0
Carga_baja_oblic_05   1   0   0
Carga_alta_oblic_05   1   0   0