Control 4

library(rio)

## Warning: package 'rio' was built under R version 4.2.2

data=import("Etr.csv")

data[,c(6:49)]=NULL

names1=c('Pais','FRS','NDS','RPGS','WRS')
names(data)=names1 

library(rio)
data2=import("gpi.csv")

merge

names2=c('Pais','2022')
names(data2)=names2 

Basefinal=merge(data,data2)

library(polycor)

## Warning: package 'polycor' was built under R version 4.2.2

corMatrix=polycor::hetcor(Basefinal)$correlations

## data contain one or more character variables
## the values of which are ordered alphabetically

dontselect=c("pais")
select=setdiff(names(Basefinal), dontselect)
DL=Basefinal[,select]

library(polycor)
corL=polycor::hetcor(DL)$correlations

## data contain one or more character variables
## the values of which are ordered alphabetically

library(ggcorrplot)

## Warning: package 'ggcorrplot' was built under R version 4.2.2

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 4.2.2

ggcorrplot(corL)

library(psych)

## Warning: package 'psych' was built under R version 4.2.2

## 
## Attaching package: 'psych'

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

## The following object is masked from 'package:polycor':
## 
##     polyserial

psych::KMO(corMatrix) 

## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA =  0.79
## MSA for each item = 
## Pais  FRS  NDS RPGS  WRS 2022 
## 0.58 0.74 0.76 0.79 0.81 0.90

cortest.bartlett(corMatrix,n=nrow(DL))$p.value>0.05

## [1] FALSE

library(matrixcalc)

is.singular.matrix(corMatrix)

## [1] FALSE

fa.parallel(DL, fa = ‘fa’,correct = T,plot = F)

library(GPArotation) resfa <- fa(DL, nfactors = 2, cor = ‘mixed’, rotate = “varimax”, fm=“minres”) print(resfa$loadings)