Analisis Factorial Ejemplo 3
Jaime Isaac Peña Mejía
1/5/2021
#paquetes que se necesitan.
ipak <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}# usage
packages <- c("parameters","apa","haven","ggplot2","ggpubr",
"gridExtra","apaTables", "reshape", "GPArotation", "mvtnorm", "psych", "psychometric", "lavaan", "nFactors", "semPlot", "lavaan", "MVN", "semTools")
ipak(packages)## Loading required package: parameters## Warning: package 'parameters' was built under R version 4.0.5## Loading required package: apa## Warning: package 'apa' was built under R version 4.0.5## Loading required package: haven## Loading required package: ggplot2## Loading required package: ggpubr## Warning: package 'ggpubr' was built under R version 4.0.3## Loading required package: gridExtra## Loading required package: apaTables## Warning: package 'apaTables' was built under R version 4.0.5## Loading required package: reshape## Loading required package: GPArotation## Warning: package 'GPArotation' was built under R version 4.0.3## Loading required package: mvtnorm## Loading required package: psych##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
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## %+%, alpha## Loading required package: psychometric## Warning: package 'psychometric' was built under R version 4.0.5## Loading required package: multilevel## Warning: package 'multilevel' was built under R version 4.0.5## Loading required package: nlme## Loading required package: MASS##
## Attaching package: 'psychometric'## The following object is masked from 'package:psych':
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## alpha## The following object is masked from 'package:ggplot2':
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## alpha## Loading required package: lavaan## This is lavaan 0.6-7## lavaan is BETA software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
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## cor2cov## Loading required package: nFactors## Warning: package 'nFactors' was built under R version 4.0.5## Loading required package: lattice##
## Attaching package: 'nFactors'## The following object is masked from 'package:lattice':
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## parallel## Loading required package: semPlot## Warning: package 'semPlot' was built under R version 4.0.5## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car## Loading required package: MVN## Warning: package 'MVN' was built under R version 4.0.3## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## sROC 0.1-2 loaded## Loading required package: semTools## ## ################################################################################# This is semTools 0.5-3## All users of R (or SEM) are invited to submit functions or ideas for functions.## #################################################################################
## Attaching package: 'semTools'## The following object is masked from 'package:psych':
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## skew## The following object is masked from 'package:parameters':
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## kurtosis## parameters apa haven ggplot2 ggpubr gridExtra
## TRUE TRUE TRUE TRUE TRUE TRUE
## apaTables reshape GPArotation mvtnorm psych psychometric
## TRUE TRUE TRUE TRUE TRUE TRUE
## lavaan nFactors semPlot lavaan MVN semTools
## TRUE TRUE TRUE TRUE TRUE TRUElibrary(nFactors)
#--------------------------------------------------------------------------
# CONTENIDO
# 0. Entorno de trabajo
# 1. Cargar base
# 2. Ajuste del modelo
# 2.1 Normalizacion
# 2.2 Prueba Kaiser-Meyer-Olkin
# 3. Factorial
# 4. Interpretación
#--------------------------------------------------------------------------#0. Entorno de trabajo
rm(list=ls())
graphics.off()
library(foreign)
library(factoextra)## Warning: package 'factoextra' was built under R version 4.0.3## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(ggplot2)
library(psych)
library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
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## select## The following object is masked from 'package:nlme':
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## collapse## The following object is masked from 'package:reshape':
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## rename## The following object is masked from 'package:gridExtra':
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## combine## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionLa base de datos se puede descargar aquí pacientes.csv
pacientes<-read.csv("E:/pacientes.csv", sep=",",header = TRUE)
View(pacientes)matriz_correlaciones<-cor(pacientes, use = "pairwise.complete.obs")
matriz_correlaciones## hrs_ejercicio glucosa peso presion
## hrs_ejercicio 1.00000000 0.4104293 0.76485171 0.09928331
## glucosa 0.41042932 1.0000000 0.71368010 0.19943737
## peso 0.76485171 0.7136801 1.00000000 0.32805124
## presion 0.09928331 0.1994374 0.32805124 1.00000000
## dias_dolor_cabeza -0.11831738 0.1830099 0.09655858 0.72525552
## dias_dolor_cabeza
## hrs_ejercicio -0.11831738
## glucosa 0.18300987
## peso 0.09655858
## presion 0.72525552
## dias_dolor_cabeza 1.00000000#2. Ajuste del modelo
#2.1 Normalización
norm01 <- function(x){(x-min(x))/(max(x)-min(x))}
pacientes_norm<-data.frame(apply(pacientes,2,norm01))
apply(pacientes_norm, 2, max)%>%round(2)## hrs_ejercicio glucosa peso presion
## 1 1 1 1
## dias_dolor_cabeza
## 1#2.2 Prueba Kaiser-Meyer-Olkin
# La prueba de 0 a 1 e indica si las correlaciones entre variables son
# suficientemente pequñas. Valores menores a 0.5 no deben ser incluidos
KMO(pacientes_norm)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = pacientes_norm)
## Overall MSA = 0.5
## MSA for each item =
## hrs_ejercicio glucosa peso presion
## 0.55 0.52 0.50 0.46
## dias_dolor_cabeza
## 0.47#2.3 Numero de factores (Grafica de sedimentacion)
corr<-round(cor(pacientes_norm),2)
aucor=eigen(corr)
plot(1:5,aucor$values,type="l",xlab="Factores",ylab="Autovalores")
#n factors ASI
library(nFactors)
results_nfactorASI<-n_factors(pacientes, rotate = "varimax", fm = "mle", n = NULL)
plot(results_nfactorASI)
#3. Factorial
fit <- factanal(pacientes, 2, rotation="varimax")
fit##
## Call:
## factanal(x = pacientes, factors = 2, rotation = "varimax")
##
## Uniquenesses:
## hrs_ejercicio glucosa peso presion
## 0.376 0.477 0.005 0.405
## dias_dolor_cabeza
## 0.005
##
## Loadings:
## Factor1 Factor2
## hrs_ejercicio 0.786
## glucosa 0.689 0.220
## peso 0.986 0.150
## presion 0.219 0.740
## dias_dolor_cabeza 0.996
##
## Factor1 Factor2
## SS loadings 2.116 1.616
## Proportion Var 0.423 0.323
## Cumulative Var 0.423 0.746
##
## Test of the hypothesis that 2 factors are sufficient.
## The chi square statistic is 12.3 on 1 degree of freedom.
## The p-value is 0.000454#4. Interpretacion
load <- fit$loadings[,1:2]
load## Factor1 Factor2
## hrs_ejercicio 0.78636910 -0.07656247
## glucosa 0.68857506 0.22025637
## peso 0.98612802 0.15025207
## presion 0.21890756 0.73987305
## dias_dolor_cabeza -0.05383748 0.99604341modelo_varimax<-fa(matriz_correlaciones,nfactors = 2,rotate = "varimax",
fa="minres")## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefullyfa.diagram(modelo_varimax)