library(xlsx)
library(tidySEM)
library(lavaan)
library(ggplot2)
library(dplyr)
library(psych)
library(mirt)
library(knitr)
library(semPlot)
library(semtree)
library(tidyverse)
df1 = read.xlsx(file = "Analise Coleta Ver05.xlsx", encoding = "UTF-8", sheetIndex = 2)
df = df1
head(df)
## ID idade genero prof Desemp_Aposent_DoLar Empreg_Priv_Publico Socio_Autonomo
## 1 1 3 0 1 0 1 0
## 2 2 3 0 4 0 0 1
## 3 3 4 0 1 0 1 0
## 4 4 3 1 7 0 0 0
## 5 5 3 0 1 0 1 0
## 6 6 3 1 2 0 1 0
## Estudante escol Escolaridade_Baixa Escolaridade_Media Escolaridade_Alta cnh
## 1 0 6 0 0 1 1
## 2 0 6 0 0 1 1
## 3 0 6 0 0 1 1
## 4 1 6 0 0 1 1
## 5 0 5 0 0 1 1
## 6 0 6 0 0 1 1
## carros_cas motos_casa pessoas_ca criancas_c adol_casa renda_fam_ Renda_Baixa
## 1 1 0 2 0 0 5 0
## 2 1 1 3 1 0 3 0
## 3 1 0 3 0 0 4 0
## 4 0 0 2 0 0 1 1
## 5 1 0 3 1 0 2 1
## 6 0 0 1 0 0 2 1
## Renda_Media Renda_Alta PR_Hora_or PR_Hora_O_h PR_Hora_O_min PR_Hora_de
## 1 0 1 07:40 7 40 08:05
## 2 1 0 11:00 11 0 11:25
## 3 1 0 08:00 8 0 08:15
## 4 0 0 08:00 8 0 08:20
## 5 0 0 13:00 13 0 15:00
## 6 0 0 14:00 14 0 14:06
## PR_Hora_D_h PR_Hora_D_min PR_Dist PR_Motivo PR_Modo PR_CarroMoto_Custo
## 1 8 5 3263 2 6 1
## 2 11 25 5951 2 6 0
## 3 8 15 1166 2 11 NA
## 4 8 20 2621 2 9 NA
## 5 15 0 9549 2 1 NA
## 6 14 6 257 1 11 NA
## SATISF_PR_CarroMoto_Acid SATISF_PR_CarroMoto_Roubo_Veic
## 1 3 2
## 2 3 1
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
## SATISF_PR_CarroMoto_Roubo_Acesso SATISF_PR_Bici_Acid
## 1 1 NA
## 2 2 NA
## 3 NA NA
## 4 NA 3
## 5 NA NA
## 6 NA NA
## SATISF_PR_Bici_Roubo_Veic SATISF_PR_Bici_Roubo_Acesso SATISF_PR_Ape_Acid
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA 2
## 4 4 5 NA
## 5 NA NA NA
## 6 NA NA 3
## PR_APP_Tempo_Espera PR_APP_Tempo_Veic PR_APP_Custo SATISF_PR_APP_Acid
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 NA NA NA NA
## 6 NA NA NA NA
## SATISF_PR_APP_Roubo_Veic SATISF_PR_APP_Roubo_Acesso PR_TC_Tempo_Caminh_O
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA 1
## 6 NA NA NA
## PR_TC_Tempo_Espera PR_TC_Tempo_Veic PR_TC_Tempo_Caminh_D PR_TC_Custo
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 NA NA NA NA
## 5 2 5 2 1
## 6 NA NA NA NA
## SATISF_PR_TC_Acid SATISF_PR_TC_Roubo_Veic SATISF_PR_TC_Roubo_Acesso
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 3 2 2
## 6 NA NA NA
## SATISF_PR_TC_Limpeza SATISF_PR_TC_Lotacao SATISF_PR_TC_Comodo freq_bus
## 1 NA NA NA 0
## 2 NA NA NA 0
## 3 NA NA NA 1
## 4 NA NA NA 1
## 5 3 1 2 1
## 6 NA NA NA 0
## freq_trem freq_carro freq_taxi freq_bike freq_pe freq_app
## 1 0 3 0 0 0 1
## 2 0 3 0 0 0 3
## 3 0 3 0 0 3 1
## 4 0 0 0 3 3 1
## 5 0 3 0 0 2 2
## 6 0 3 1 3 3 3
## freq_app_BIN_usa_nusa freq_app_BIN_usam_usap A_wifi A_smartphone A_novas_tec
## 1 1 0 4 5 1
## 2 1 1 5 5 2
## 3 1 0 4 5 3
## 4 1 0 3 2 2
## 5 1 1 5 5 2
## 6 1 1 1 5 2
## A_transporte A_transpor_1 A_transp_pub A_seg_carro A_carro_need A_carro_poss
## 1 3 3 1 4 4 5
## 2 2 2 1 5 5 5
## 3 2 2 1 5 4 5
## 4 3 4 1 4 1 1
## 5 5 3 5 4 4 3
## 6 2 1 1 5 1 2
## P_transp_p_1 P_colegas_tr P_fam_amigos P_fam_amig_1 P_facil_tran P_poss_usar_
## 1 4 4 4 4 2 3
## 2 1 5 3 5 1 1
## 3 2 4 3 5 4 5
## 4 2 2 5 5 4 5
## 5 3 3 4 4 4 5
## 6 1 5 4 5 4 5
## P_dificil_us P_transp_p_2 P_evit_trans P_plan_trans x y long lat
## 1 1 1 2 4 -5 -3 -51.2219 -30.0292
## 2 1 2 1 1 -5 -3 -51.1897 -30.0538
## 3 2 2 2 2 -5 -3 -51.2217 -30.0320
## 4 2 2 5 1 -5 -3 -51.2217 -30.0320
## 5 2 4 4 4 -5 -3 -51.2187 -30.0333
## 6 1 1 4 1 -5 -3 -51.2077 -30.0291
## CD_GEOCODI NM_BAIRRO Comerc Serv Paradas Decliv_ab
## 1 431490205000035 Centro Histórico 211 720 5 0.043623909
## 2 431490205000261 Jardim Botânico 19 33 1 0.009080742
## 3 431490205000037 Centro Histórico 80 518 13 0.019960212
## 4 431490205000037 Centro Histórico 80 518 13 0.019960212
## 5 431490205002165 Farroupilha 77 121 8 0.005543480
## 6 431490205001428 Independência 36 34 1 0.042424815
# Correla??o
cor(na.omit(df$renda_fam_), na.omit(df$PR_Motivo))
## [1] -0.07047039
#Sys.setenv(JAVA_HOME=paste(Sys.getenv("JAVA_HOME"), "jre", sep="\\"))
df$CD_GEOCODI = NULL
df$NM_BAIRRO = NULL
df$PR_Hora_or = NULL
df$PR_Hora_de = NULL
# traz registros com algum valor nas colunas
df = df[!is.na(df[, c("A_carro_poss", "A_carro_need", "A_seg_carro", "SATISF_PR_CarroMoto_Acid", "SATISF_PR_CarroMoto_Roubo_Veic",
"A_transp_pub", "A_transpor_1", "A_transporte", "P_transp_p_1", "P_colegas_tr", "P_fam_amigos", "P_facil_tran",
"P_poss_usar_", "P_transp_p_2", "P_evit_trans", "P_plan_trans", "Comerc", "Serv", "Paradas", "Decliv_ab", "idade",
"genero", "prof", "escol", "cnh", "carros_cas", "motos_casa", "pessoas_ca", "criancas_c", "adol_casa", "renda_fam_")]),]
# mant~em apenas linhas completas
df = df[complete.cases(df[1:31]),]
df = df[, c("A_carro_poss", "A_carro_need", "A_seg_carro", "SATISF_PR_CarroMoto_Acid", "SATISF_PR_CarroMoto_Roubo_Veic",
"A_transp_pub", "A_transpor_1", "A_transporte", "P_transp_p_1", "P_colegas_tr", "P_fam_amigos", "P_facil_tran",
"P_poss_usar_", "P_transp_p_2", "P_evit_trans", "P_plan_trans", "Comerc", "Serv", "Paradas", "Decliv_ab", "idade",
"genero", "prof", "escol", "cnh", "carros_cas", "motos_casa", "pessoas_ca", "criancas_c", "adol_casa", "renda_fam_")]
names(df) = c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17",
"V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "V29", "V30", "V31")
df = df[complete.cases(df[17:18]),]
#View(df)
glimpse(df)
## Rows: 127
## Columns: 31
## $ V1 <dbl> 5, 5, 4, 5, 5, 5, 5, 5, 5, 2, 4, 5, 5, 3, 5, 5, 1, 5, 4, 5, 5, 5, …
## $ V2 <dbl> 4, 5, 2, 4, 5, 5, 5, 5, 5, 3, 3, 3, 1, 4, 5, 5, 3, 5, 4, 3, 3, 3, …
## $ V3 <dbl> 4, 5, 3, 4, 5, 3, 5, 4, 4, 3, 3, 5, 5, 3, 2, 3, 3, 4, 4, 4, 4, 4, …
## $ V4 <dbl> 3, 3, 2, 2, 4, 4, 4, 5, 3, 3, 1, 5, 3, 3, 3, 3, 3, 5, 4, 3, 5, 4, …
## $ V5 <dbl> 2, 1, 3, 1, 3, 2, 4, 3, 2, 2, 1, 5, 5, 3, 2, 3, 3, 4, 4, 4, 5, 3, …
## $ V6 <dbl> 1, 1, 5, 1, 1, 2, 1, 2, 3, 2, 1, 1, 1, 2, 1, 1, 3, 2, 1, 1, 1, 1, …
## $ V7 <dbl> 3, 2, 4, 1, 1, 3, 1, 3, 4, 3, 2, 1, 1, 2, 2, 1, 3, 4, 3, 3, 1, 3, …
## $ V8 <dbl> 3, 2, 4, 1, 1, 3, 1, 3, 4, 3, 2, 1, 1, 2, 2, 1, 3, 4, 2, 2, 1, 2, …
## $ V9 <dbl> 4, 1, 4, 2, 3, 1, 1, 4, 3, 2, 1, 4, 1, 3, 2, 1, 3, 2, 4, 1, 1, 2, …
## $ V10 <dbl> 4, 5, 3, 3, 3, 1, 1, 3, 3, 2, 3, 3, 3, 3, 5, 5, 2, 4, 3, 3, 3, 3, …
## $ V11 <dbl> 4, 3, 4, 4, 4, 2, 3, 4, 3, 2, 4, 1, 1, 3, 4, 3, 3, 4, 3, 5, 2, 4, …
## $ V12 <dbl> 2, 1, 4, 2, 3, 1, 3, 3, 3, 2, 1, 1, 1, 3, 2, 2, 4, 2, 1, 3, 2, 3, …
## $ V13 <dbl> 3, 1, 4, 2, 3, 2, 4, 1, 3, 2, 5, 4, 2, 3, 5, 5, 5, 2, 3, 4, 4, 5, …
## $ V14 <dbl> 1, 2, 4, 3, 3, 2, 1, 3, 4, 2, 1, 1, 1, 4, 3, 1, 3, 4, 2, 5, 3, 3, …
## $ V15 <dbl> 2, 1, 4, 3, 3, 1, 1, 4, 4, 2, 3, 1, 2, 3, 3, 1, 2, 5, 3, 5, 2, 3, …
## $ V16 <dbl> 4, 1, 4, 2, 1, 1, 1, 3, 3, 2, 1, 1, 3, 4, 3, 1, 3, 5, 3, 5, 3, 3, …
## $ V17 <dbl> 211, 19, 335, 77, 30, 77, 12, 60, 77, 0, 3, 14, 375, 405, 1, 3, 12…
## $ V18 <dbl> 720, 33, 315, 121, 39, 121, 70, 37, 121, 0, 0, 15, 1568, 263, 2, 0…
## $ V19 <dbl> 5, 1, 26, 8, 3, 8, 2, 5, 8, 0, 1, 8, 22, 36, 1, 1, 0, 2, 8, 9, 10,…
## $ V20 <dbl> 0.043623909, 0.009080742, 0.000211000, 0.000000000, 0.013432000, 0…
## $ V21 <dbl> 3, 3, 3, 6, 3, 6, 3, 6, 4, 5, 4, 7, 4, 6, 3, 3, 4, 6, 5, 5, 4, 4, …
## $ V22 <dbl> 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, …
## $ V23 <dbl> 1, 4, 1, 1, 7, 2, 2, 1, 1, 1, 1, 1, 7, 2, 2, 8, 2, 2, 1, 1, 4, 1, …
## $ V24 <dbl> 6, 6, 6, 5, 5, 6, 5, 6, 5, 6, 6, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 5, …
## $ V25 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ V26 <dbl> 1, 1, 1, 2, 3, 2, 1, 2, 1, 1, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 2, 1, …
## $ V27 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ V28 <dbl> 2, 3, 2, 2, 4, 2, 2, 2, 3, 3, 2, 2, 5, 3, 3, 3, 3, 4, 4, 3, 2, 1, …
## $ V29 <dbl> 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 4, 0, 1, 0, 1, 0, 1, 1, 0, 0, …
## $ V30 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ V31 <dbl> 5, 3, 3, 5, 5, 3, 3, 5, 2, 2, 3, 3, 4, 5, 3, 3, 4, 2, 3, 4, 5, 3, …
#df$V17 = log10(df$V17)
#df$V18 = log10(df$V18)
#df$V19 = log10(df$V19)
library(corrplot)
M<-cor(df)
#View(M)
corrplot(M, method="pie")
#corrplot(M, method="number")
#corrplot(M, method = 'color', order = 'alphabet')
#corrplot(M)
#corrplot(M, order = 'AOE')
#corrplot(M, method = 'square', order = 'FPC', type = 'lower', diag = FALSE)
#corrplot.mixed(M, order = 'AOE')
#corrplot.mixed(M, lower = 'shade', upper = 'pie', order = 'hclust')
#corrplot(M, order = 'hclust', addrect = 2)
#col1 = colorRampPalette(c('#7F0000', 'red', '#FF7F00', 'yellow', 'white',cyan', '#007FFF', 'blue', '#00007F'))
#col2 = colorRampPalette(c('red', 'white', 'blue'))
library(RColorBrewer)
#corrplot(M, order = 'AOE', addCoef.col = 'black', tl.pos = 'd', cl.pos = 'n', col = brewer.pal(n = 10, name = 'PRGn'))
# cor.mtest <- function(mat, ...) {
# mat <- as.matrix(mat)
# n <- ncol(mat)
# p.mat<- matrix(NA, n, n)
# diag(p.mat) <- 0
# for (i in 1:(n - 1)) {
# for (j in (i + 1):n) {
# tmp <- cor.test(mat[, i], mat[, j], ...)
# p.mat[i, j] <- p.mat[j, i] <- tmp$p.value
# }
# }
# colnames(p.mat) <- rownames(p.mat) <- colnames(mat)
# p.mat
# }
# p.mat <- cor.mtest(df)
#
# col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))
# corrplot(M, method="color", col=col(200),
# type="upper", order="hclust",
# addCoef.col = "black",
# tl.col="black", tl.srt=45,
# p.mat = p.mat, sig.level = 0.01, insig = "blank",
# diag=FALSE
# )
Modelo Original
modelo='
Car =~ V1 + V2 + V3 + V4 + V5 + Amb
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16 + Amb
Amb =~ V17 + V18 + V19 + V20
Socio =~ V21 + V22 + V23 + V24 + V25 + V26 + V27 + V28 + V29 + V30 + V31 + Amb
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 1244 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 68
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 983.304
## Degrees of freedom 428
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.099 0.906
## V2 0.604 NA 0.663 0.547
## V3 0.359 NA 0.394 0.432
## V4 0.172 NA 0.189 0.203
## V5 -0.023 NA -0.025 -0.020
## Amb 2.400 NA 0.041 0.041
## TP =~
## V6 1.000 0.484 0.489
## V7 1.583 NA 0.766 0.679
## V8 1.662 NA 0.804 0.745
## V9 0.936 NA 0.453 0.421
## V10 0.495 NA 0.239 0.211
## V11 0.670 NA 0.324 0.299
## V12 1.306 NA 0.632 0.486
## V13 0.810 NA 0.392 0.294
## V14 2.070 NA 1.002 0.847
## V15 1.683 NA 0.814 0.679
## V16 2.052 NA 0.993 0.821
## Amb 34.110 NA 0.256 0.256
## Amb =~
## V17 1.000 64.473 0.778
## V18 3.529 NA 227.541 0.997
## V19 0.051 NA 3.276 0.425
## V20 0.000 NA 0.002 0.134
## Socio =~
## V21 1.000 0.042 0.032
## V22 0.472 NA 0.020 0.040
## V23 -1.067 NA -0.045 -0.021
## V24 0.241 NA 0.010 0.019
## V25 -0.104 NA -0.004 -0.035
## V26 -1.716 NA -0.073 -0.105
## V27 -0.221 NA -0.009 -0.023
## V28 -3.045 NA -0.129 -0.125
## V29 -2.936 NA -0.125 -0.179
## V30 -2.518 NA -0.107 -0.336
## V31 0.134 NA 0.006 0.006
## Amb -1779.038 NA -1.172 -1.172
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.163 NA -0.306 -0.306
## Socio -0.003 NA -0.058 -0.058
## TP ~~
## Socio 0.002 NA 0.093 0.093
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.264 NA 0.264 0.179
## .V2 1.032 NA 1.032 0.701
## .V3 0.679 NA 0.679 0.814
## .V4 0.835 NA 0.835 0.959
## .V5 1.542 NA 1.542 1.000
## .V6 0.746 NA 0.746 0.761
## .V7 0.685 NA 0.685 0.538
## .V8 0.519 NA 0.519 0.445
## .V9 0.952 NA 0.952 0.823
## .V10 1.225 NA 1.225 0.955
## .V11 1.072 NA 1.072 0.911
## .V12 1.291 NA 1.291 0.764
## .V13 1.625 NA 1.625 0.914
## .V14 0.395 NA 0.395 0.282
## .V15 0.774 NA 0.774 0.538
## .V16 0.476 NA 0.476 0.325
## .V17 2706.102 NA 2706.102 0.394
## .V18 277.036 NA 277.036 0.005
## .V19 48.620 NA 48.620 0.819
## .V20 0.000 NA 0.000 0.982
## .V21 1.727 NA 1.727 0.999
## .V22 0.249 NA 0.249 0.998
## .V23 4.719 NA 4.719 1.000
## .V24 0.278 NA 0.278 1.000
## .V25 0.015 NA 0.015 0.999
## .V26 0.475 NA 0.475 0.989
## .V27 0.163 NA 0.163 0.999
## .V28 1.060 NA 1.060 0.984
## .V29 0.470 NA 0.470 0.968
## .V30 0.090 NA 0.090 0.887
## .V31 0.977 NA 0.977 1.000
## Car 1.208 NA 1.000 1.000
## TP 0.234 NA 1.000 1.000
## .Amb -1600.562 NA -0.385 -0.385
## Socio 0.002 NA 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
Modelo 1
modelo='
Car =~ V1 + V2 + V3
TP =~ V6 + V7 + V8
Amb =~ V17 + V18 + V19
Socio =~ V21 + V22 + V23
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 182 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 30
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 167.080
## Degrees of freedom 48
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.185 0.977
## V2 0.526 0.150 3.499 0.000 0.623 0.513
## V3 0.307 0.098 3.140 0.002 0.364 0.398
## TP =~
## V6 1.000 0.538 0.543
## V7 1.672 0.278 6.010 0.000 0.899 0.797
## V8 1.836 0.319 5.749 0.000 0.988 0.915
## Amb =~
## V17 1.000 8.036 0.097
## V18 -0.294 0.286 -1.027 0.304 -2.363 -0.010
## V19 6.649 153.966 0.043 0.966 53.433 6.701
## Socio =~
## V21 1.000 0.955 0.727
## V22 0.041 0.062 0.666 0.505 0.039 0.079
## V23 -1.056 0.561 -1.881 0.060 -1.009 -0.464
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.179 0.069 -2.578 0.010 -0.280 -0.280
## Amb -0.218 5.104 -0.043 0.966 -0.023 -0.023
## Socio 0.123 0.136 0.903 0.366 0.108 0.108
## TP ~~
## Amb 0.076 1.767 0.043 0.966 0.017 0.017
## Socio 0.007 0.064 0.112 0.911 0.014 0.014
## Amb ~~
## Socio 0.265 6.198 0.043 0.966 0.035 0.035
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.068 0.344 0.198 0.843 0.068 0.046
## .V2 1.084 0.166 6.522 0.000 1.084 0.737
## .V3 0.703 0.094 7.459 0.000 0.703 0.841
## .V6 0.691 0.093 7.401 0.000 0.691 0.705
## .V7 0.463 0.108 4.300 0.000 0.463 0.364
## .V8 0.190 0.112 1.706 0.088 0.190 0.163
## .V17 6770.635 1717.877 3.941 0.000 6770.635 0.991
## .V18 52661.995 6609.239 7.968 0.000 52661.995 1.000
## .V19 -2791.549 66013.553 -0.042 0.966 -2791.549 -43.906
## .V21 0.817 0.487 1.677 0.093 0.817 0.472
## .V22 0.248 0.031 7.936 0.000 0.248 0.994
## .V23 3.703 0.706 5.243 0.000 3.703 0.784
## Car 1.403 0.390 3.598 0.000 1.000 1.000
## TP 0.289 0.094 3.082 0.002 1.000 1.000
## Amb 64.578 1497.673 0.043 0.966 1.000 1.000
## Socio 0.913 0.513 1.780 0.075 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
Modelo 2
modelo='
Car =~ V1 + V2
TP =~ V6 + V7
Amb =~ V17 + V18
Socio =~ V21 + V22
'
fit2 = cfa(modelo,data = df)
summary(fit2,standardized = TRUE)
## lavaan 0.6-9 ended normally after 85 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 22
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 205.993
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 0.735 0.605
## V2 1.371 0.585 2.344 0.019 1.008 0.830
## TP =~
## V6 1.000 1.032 1.018
## V7 0.460 0.218 2.107 0.035 0.475 0.419
## Amb =~
## V17 1.000 0.732 0.013
## V18 -1.778 1.010 -1.760 0.078 -1.301 -0.008
## Socio =~
## V21 1.000 1.555 1.182
## V22 0.011 0.305 0.037 0.970 0.018 0.035
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.214 0.107 -1.990 0.047 -0.282 -0.282
## Amb -2.661 3.880 -0.686 0.493 -4.948 -4.948
## Socio 0.118 0.108 1.092 0.275 0.103 0.103
## TP ~~
## Amb 17.111 5.469 3.129 0.002 22.651 22.651
## Socio -0.071 0.119 -0.603 0.547 -0.045 -0.045
## Amb ~~
## Socio -2.154 5.759 -0.374 0.708 -1.892 -1.892
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.933 0.252 3.701 0.000 0.933 0.633
## .V2 0.459 0.424 1.085 0.278 0.459 0.312
## .V6 -0.037 0.461 -0.080 0.937 -0.037 -0.036
## .V7 1.057 0.165 6.417 0.000 1.057 0.824
## .V17 3413.855 638.350 5.348 0.000 3413.855 1.000
## .V18 26333.992 3627.310 7.260 0.000 26333.992 1.000
## .V21 -0.688 64.605 -0.011 0.992 -0.688 -0.398
## .V22 0.249 0.032 7.698 0.000 0.249 0.999
## Car 0.540 0.265 2.036 0.042 1.000 1.000
## TP 1.065 0.479 2.225 0.026 1.000 1.000
## Amb 0.536 473.302 0.001 0.999 1.000 1.000
## Socio 2.418 64.605 0.037 0.970 1.000 1.000
semPaths(fit2,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
Comparacão de Modelos (Indicadores de Qualidade do Modelo)
- CHISQ: (Indicador de Aderência do modelo) Ideal < 2
- AIC: (Indicador de Aderência do modelo) Quanto Menor melhor | Quanto menor mais aderente é o modelo
- BIC: (Indicador de Aderência do modelo) Quanto Menor melhor | Quanto menor mais aderente é o modelo
- CFI: (Comparação de Modelos) > 0.95 para aceitacão
- TLI: (Comparação de Modelos) > 0.95 para aceitacão
- RMSEA: < 0.6
- SMMR: Desejável < 0.9
library("semTools")
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 167.080† 48 .000 .140† .699† .586† .121† 7324.386 7409.712
Modelo 3
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8
Amb =~ V17 + V18 + V19
Socio =~ V21 + V22 + V23
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 196 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 34
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 236.142
## Degrees of freedom 71
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.129 0.931
## V2 0.575 0.146 3.949 0.000 0.649 0.535
## V3 0.339 0.097 3.497 0.000 0.383 0.419
## V4 0.163 0.083 1.966 0.049 0.184 0.198
## V5 -0.028 0.104 -0.269 0.788 -0.032 -0.026
## TP =~
## V6 1.000 0.538 0.543
## V7 1.670 0.278 6.013 0.000 0.899 0.797
## V8 1.837 0.319 5.752 0.000 0.988 0.915
## Amb =~
## V17 1.000 7.937 0.096
## V18 -0.298 0.286 -1.040 0.298 -2.362 -0.010
## V19 6.825 162.263 0.042 0.966 54.165 6.789
## Socio =~
## V21 1.000 0.962 0.732
## V22 0.041 0.061 0.673 0.501 0.040 0.079
## V23 -1.041 0.553 -1.881 0.060 -1.001 -0.461
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.179 0.069 -2.589 0.010 -0.295 -0.295
## Amb -0.210 5.044 -0.042 0.967 -0.023 -0.023
## Socio 0.130 0.135 0.962 0.336 0.120 0.120
## TP ~~
## Amb 0.073 1.764 0.042 0.967 0.017 0.017
## Socio 0.007 0.064 0.107 0.915 0.013 0.013
## Amb ~~
## Socio 0.259 6.204 0.042 0.967 0.034 0.034
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.197 0.272 0.725 0.468 0.197 0.134
## .V2 1.051 0.161 6.541 0.000 1.051 0.714
## .V3 0.688 0.093 7.421 0.000 0.688 0.824
## .V4 0.836 0.106 7.912 0.000 0.836 0.961
## .V5 1.541 0.193 7.968 0.000 1.541 0.999
## .V6 0.691 0.093 7.401 0.000 0.691 0.705
## .V7 0.464 0.108 4.315 0.000 0.464 0.365
## .V8 0.189 0.112 1.697 0.090 0.189 0.162
## .V17 6772.120 1720.071 3.937 0.000 6772.120 0.991
## .V18 52661.995 6609.307 7.968 0.000 52661.995 1.000
## .V19 -2870.173 69654.363 -0.041 0.967 -2870.173 -45.094
## .V21 0.804 0.492 1.633 0.102 0.804 0.465
## .V22 0.248 0.031 7.936 0.000 0.248 0.994
## .V23 3.718 0.701 5.300 0.000 3.718 0.788
## Car 1.274 0.327 3.902 0.000 1.000 1.000
## TP 0.289 0.094 3.083 0.002 1.000 1.000
## Amb 62.991 1499.971 0.042 0.967 1.000 1.000
## Socio 0.926 0.519 1.784 0.074 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 236.142 71 .000 .135† .628† .523† .124† 8086.159 8182.861
Modelo 4
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10
Amb =~ V17 + V18 + V19
Socio =~ V21 + V22 + V23
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 184 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 38
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 271.860
## Degrees of freedom 98
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.139 0.939
## V2 0.565 0.146 3.873 0.000 0.644 0.531
## V3 0.333 0.097 3.437 0.001 0.379 0.415
## V4 0.160 0.082 1.945 0.052 0.182 0.195
## V5 -0.029 0.103 -0.286 0.775 -0.033 -0.027
## TP =~
## V6 1.000 0.535 0.541
## V7 1.751 0.293 5.972 0.000 0.937 0.831
## V8 1.775 0.299 5.945 0.000 0.950 0.880
## V9 0.801 0.214 3.749 0.000 0.429 0.398
## V10 0.327 0.205 1.589 0.112 0.175 0.154
## Amb =~
## V17 1.000 8.242 0.100
## V18 -0.286 0.287 -0.999 0.318 -2.360 -0.010
## V19 6.285 136.010 0.046 0.963 51.799 6.513
## Socio =~
## V21 1.000 0.960 0.730
## V22 0.041 0.061 0.676 0.499 0.040 0.080
## V23 -1.044 0.555 -1.883 0.060 -1.003 -0.462
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.174 0.069 -2.528 0.011 -0.284 -0.284
## Amb -0.229 5.017 -0.046 0.964 -0.024 -0.024
## Socio 0.129 0.135 0.953 0.341 0.118 0.118
## TP ~~
## Amb 0.088 1.921 0.046 0.964 0.020 0.020
## Socio 0.009 0.064 0.133 0.894 0.017 0.017
## Amb ~~
## Socio 0.281 6.143 0.046 0.964 0.035 0.035
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.173 0.283 0.613 0.540 0.173 0.118
## .V2 1.057 0.161 6.549 0.000 1.057 0.718
## .V3 0.691 0.093 7.433 0.000 0.691 0.828
## .V4 0.837 0.106 7.918 0.000 0.837 0.962
## .V5 1.541 0.193 7.968 0.000 1.541 0.999
## .V6 0.694 0.093 7.428 0.000 0.694 0.708
## .V7 0.393 0.097 4.044 0.000 0.393 0.309
## .V8 0.263 0.092 2.869 0.004 0.263 0.226
## .V9 0.974 0.126 7.728 0.000 0.974 0.841
## .V10 1.251 0.158 7.938 0.000 1.251 0.976
## .V17 6767.103 1695.589 3.991 0.000 6767.103 0.990
## .V18 52662.035 6609.076 7.968 0.000 52662.035 1.000
## .V19 -2619.844 57971.338 -0.045 0.964 -2619.844 -41.413
## .V21 0.807 0.491 1.645 0.100 0.807 0.467
## .V22 0.248 0.031 7.935 0.000 0.248 0.994
## .V23 3.715 0.702 5.292 0.000 3.715 0.787
## Car 1.298 0.336 3.862 0.000 1.000 1.000
## TP 0.287 0.093 3.073 0.002 1.000 1.000
## Amb 67.929 1472.553 0.046 0.963 1.000 1.000
## Socio 0.923 0.517 1.785 0.074 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 271.860 98 .000 .118† .631† .548† .117† 8844.809 8952.888
Modelo 5
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12
Amb =~ V17 + V18 + V19
Socio =~ V21 + V22 + V23
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 199 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 42
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 330.470
## Degrees of freedom 129
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.138 0.938
## V2 0.567 0.146 3.883 0.000 0.645 0.532
## V3 0.333 0.097 3.441 0.001 0.379 0.415
## V4 0.160 0.082 1.948 0.051 0.182 0.195
## V5 -0.030 0.103 -0.287 0.774 -0.034 -0.027
## TP =~
## V6 1.000 0.553 0.559
## V7 1.668 0.268 6.232 0.000 0.923 0.818
## V8 1.720 0.272 6.311 0.000 0.952 0.881
## V9 0.775 0.203 3.818 0.000 0.429 0.399
## V10 0.346 0.198 1.744 0.081 0.191 0.169
## V11 0.359 0.191 1.885 0.059 0.199 0.183
## V12 1.202 0.259 4.649 0.000 0.665 0.512
## Amb =~
## V17 1.000 8.034 0.097
## V18 -0.289 0.286 -1.010 0.313 -2.323 -0.010
## V19 6.616 149.887 0.044 0.965 53.156 6.682
## Socio =~
## V21 1.000 0.962 0.731
## V22 0.042 0.061 0.683 0.494 0.040 0.081
## V23 -1.041 0.553 -1.881 0.060 -1.001 -0.461
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.181 0.070 -2.570 0.010 -0.287 -0.287
## Amb -0.218 4.980 -0.044 0.965 -0.024 -0.024
## Socio 0.130 0.136 0.956 0.339 0.118 0.118
## TP ~~
## Amb 0.088 2.020 0.044 0.965 0.020 0.020
## Socio 0.000 0.066 0.002 0.998 0.000 0.000
## Amb ~~
## Socio 0.267 6.104 0.044 0.965 0.035 0.035
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.177 0.281 0.630 0.529 0.177 0.120
## .V2 1.056 0.161 6.540 0.000 1.056 0.717
## .V3 0.691 0.093 7.434 0.000 0.691 0.828
## .V4 0.837 0.106 7.917 0.000 0.837 0.962
## .V5 1.541 0.193 7.968 0.000 1.541 0.999
## .V6 0.674 0.091 7.402 0.000 0.674 0.688
## .V7 0.420 0.086 4.873 0.000 0.420 0.330
## .V8 0.260 0.078 3.331 0.001 0.260 0.223
## .V9 0.973 0.126 7.739 0.000 0.973 0.841
## .V10 1.245 0.157 7.933 0.000 1.245 0.971
## .V11 1.138 0.144 7.927 0.000 1.138 0.966
## .V12 1.248 0.166 7.530 0.000 1.248 0.738
## .V17 6770.020 1689.185 4.008 0.000 6770.020 0.991
## .V18 52662.229 6609.151 7.968 0.000 52662.229 1.000
## .V19 -2762.254 63912.083 -0.043 0.966 -2762.254 -43.648
## .V21 0.804 0.492 1.634 0.102 0.804 0.465
## .V22 0.248 0.031 7.935 0.000 0.248 0.993
## .V23 3.719 0.701 5.304 0.000 3.719 0.788
## Car 1.294 0.335 3.864 0.000 1.000 1.000
## TP 0.306 0.095 3.207 0.001 1.000 1.000
## Amb 64.549 1464.716 0.044 0.965 1.000 1.000
## Socio 0.925 0.519 1.784 0.074 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 330.470 129 .000 .111† .621† .551† .116† 9625.288 9744.743
Modelo 6
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14
Amb =~ V17 + V18 + V19
Socio =~ V21 + V22 + V23
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 206 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 46
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 403.415
## Degrees of freedom 164
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.116 0.920
## V2 0.588 0.147 3.989 0.000 0.656 0.541
## V3 0.346 0.098 3.527 0.000 0.386 0.422
## V4 0.169 0.085 1.996 0.046 0.188 0.202
## V5 -0.027 0.106 -0.257 0.797 -0.030 -0.025
## TP =~
## V6 1.000 0.573 0.579
## V7 1.569 0.243 6.467 0.000 0.900 0.798
## V8 1.621 0.242 6.691 0.000 0.930 0.861
## V9 0.766 0.193 3.969 0.000 0.439 0.408
## V10 0.349 0.190 1.832 0.067 0.200 0.177
## V11 0.413 0.184 2.247 0.025 0.237 0.218
## V12 1.247 0.247 5.047 0.000 0.715 0.550
## V13 0.595 0.228 2.609 0.009 0.341 0.256
## V14 1.474 0.243 6.060 0.000 0.845 0.715
## Amb =~
## V17 1.000 8.075 0.098
## V18 -0.287 0.287 -1.000 0.317 -2.321 -0.010
## V19 6.514 144.551 0.045 0.964 52.600 6.627
## Socio =~
## V21 1.000 0.964 0.733
## V22 0.042 0.061 0.686 0.493 0.040 0.081
## V23 -1.036 0.551 -1.880 0.060 -0.999 -0.460
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.192 0.072 -2.653 0.008 -0.300 -0.300
## Amb -0.219 4.918 -0.045 0.964 -0.024 -0.024
## Socio 0.132 0.135 0.974 0.330 0.122 0.122
## TP ~~
## Amb 0.097 2.180 0.045 0.964 0.021 0.021
## Socio 0.000 0.068 0.002 0.998 0.000 0.000
## Amb ~~
## Socio 0.271 6.076 0.045 0.964 0.035 0.035
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.226 0.264 0.856 0.392 0.226 0.154
## .V2 1.041 0.161 6.483 0.000 1.041 0.708
## .V3 0.686 0.093 7.397 0.000 0.686 0.822
## .V4 0.835 0.106 7.903 0.000 0.835 0.959
## .V5 1.541 0.193 7.968 0.000 1.541 0.999
## .V6 0.652 0.088 7.388 0.000 0.652 0.665
## .V7 0.462 0.079 5.831 0.000 0.462 0.363
## .V8 0.302 0.066 4.576 0.000 0.302 0.259
## .V9 0.965 0.125 7.741 0.000 0.965 0.833
## .V10 1.242 0.157 7.932 0.000 1.242 0.969
## .V11 1.121 0.142 7.912 0.000 1.121 0.952
## .V12 1.179 0.158 7.471 0.000 1.179 0.697
## .V13 1.662 0.211 7.889 0.000 1.662 0.935
## .V14 0.684 0.101 6.736 0.000 0.684 0.489
## .V17 6768.883 1675.816 4.039 0.000 6768.883 0.990
## .V18 52662.207 6609.099 7.968 0.000 52662.207 1.000
## .V19 -2703.720 61298.136 -0.044 0.965 -2703.720 -42.918
## .V21 0.801 0.494 1.620 0.105 0.801 0.463
## .V22 0.248 0.031 7.934 0.000 0.248 0.993
## .V23 3.723 0.700 5.320 0.000 3.723 0.789
## Car 1.245 0.320 3.896 0.000 1.000 1.000
## TP 0.329 0.098 3.366 0.001 1.000 1.000
## Amb 65.205 1449.409 0.045 0.964 1.000 1.000
## Socio 0.929 0.521 1.784 0.074 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 403.415 164 .000 .107† .629† .571† .113† 10391.845 10522.677
Modelo 7
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16
Amb =~ V17 + V18 + V19
Socio =~ V21 + V22 + V23
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 180 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 50
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 530.025
## Degrees of freedom 203
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.106 0.912
## V2 0.597 0.147 4.055 0.000 0.660 0.544
## V3 0.352 0.098 3.579 0.000 0.389 0.426
## V4 0.174 0.086 2.034 0.042 0.193 0.207
## V5 -0.024 0.108 -0.224 0.823 -0.027 -0.022
## TP =~
## V6 1.000 0.486 0.491
## V7 1.586 0.310 5.119 0.000 0.771 0.683
## V8 1.665 0.312 5.344 0.000 0.809 0.749
## V9 0.931 0.244 3.808 0.000 0.452 0.420
## V10 0.489 0.229 2.138 0.033 0.237 0.210
## V11 0.660 0.228 2.889 0.004 0.321 0.295
## V12 1.315 0.310 4.238 0.000 0.639 0.491
## V13 0.805 0.280 2.872 0.004 0.391 0.293
## V14 2.067 0.368 5.616 0.000 1.005 0.849
## V15 1.665 0.327 5.088 0.000 0.809 0.675
## V16 2.026 0.366 5.531 0.000 0.985 0.814
## Amb =~
## V17 1.000 8.738 0.106
## V18 -0.297 0.292 -1.018 0.309 -2.598 -0.011
## V19 5.501 103.583 0.053 0.958 48.066 6.083
## Socio =~
## V21 1.000 0.934 0.710
## V22 0.045 0.064 0.704 0.482 0.042 0.084
## V23 -1.103 0.562 -1.963 0.050 -1.030 -0.474
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.164 0.063 -2.608 0.009 -0.305 -0.305
## Amb -0.259 4.940 -0.052 0.958 -0.027 -0.027
## Socio 0.126 0.133 0.942 0.346 0.122 0.122
## TP ~~
## Amb 0.096 1.830 0.052 0.958 0.023 0.023
## Socio 0.026 0.057 0.456 0.648 0.057 0.057
## Amb ~~
## Socio 0.320 6.086 0.053 0.958 0.039 0.039
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.248 0.255 0.973 0.330 0.248 0.169
## .V2 1.036 0.160 6.473 0.000 1.036 0.704
## .V3 0.684 0.093 7.384 0.000 0.684 0.819
## .V4 0.833 0.106 7.896 0.000 0.833 0.957
## .V5 1.542 0.193 7.968 0.000 1.542 1.000
## .V6 0.744 0.097 7.678 0.000 0.744 0.759
## .V7 0.678 0.095 7.159 0.000 0.678 0.533
## .V8 0.511 0.075 6.776 0.000 0.511 0.438
## .V9 0.953 0.123 7.772 0.000 0.953 0.823
## .V10 1.226 0.155 7.927 0.000 1.226 0.956
## .V11 1.074 0.136 7.881 0.000 1.074 0.913
## .V12 1.283 0.167 7.677 0.000 1.283 0.759
## .V13 1.625 0.206 7.883 0.000 1.625 0.914
## .V14 0.389 0.070 5.543 0.000 0.389 0.278
## .V15 0.782 0.109 7.196 0.000 0.782 0.544
## .V16 0.492 0.080 6.118 0.000 0.492 0.337
## .V17 6757.786 1666.946 4.054 0.000 6757.786 0.989
## .V18 52660.866 6608.920 7.968 0.000 52660.866 1.000
## .V19 -2247.934 43424.868 -0.052 0.959 -2247.934 -35.999
## .V21 0.858 0.449 1.908 0.056 0.858 0.496
## .V22 0.247 0.031 7.929 0.000 0.247 0.993
## .V23 3.660 0.703 5.204 0.000 3.660 0.775
## Car 1.223 0.312 3.925 0.000 1.000 1.000
## TP 0.236 0.084 2.805 0.005 1.000 1.000
## Amb 76.358 1440.790 0.053 0.958 1.000 1.000
## Socio 0.872 0.475 1.834 0.067 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 530.025 203 .000 .113† .621† .569† .112† 11083.773 11225.982
Modelo 8
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16
Amb =~ V17 + V18 + V19 + V20
Socio =~ V21 + V22 + V23
'
df$V20 = df$V20*100
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 168 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 52
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 559.764
## Degrees of freedom 224
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.106 0.912
## V2 0.598 0.148 4.049 0.000 0.661 0.545
## V3 0.352 0.098 3.576 0.000 0.389 0.426
## V4 0.174 0.086 2.032 0.042 0.193 0.207
## V5 -0.024 0.108 -0.223 0.823 -0.027 -0.021
## TP =~
## V6 1.000 0.486 0.491
## V7 1.586 0.310 5.120 0.000 0.771 0.683
## V8 1.665 0.312 5.345 0.000 0.809 0.750
## V9 0.931 0.244 3.808 0.000 0.452 0.420
## V10 0.489 0.229 2.137 0.033 0.237 0.210
## V11 0.660 0.228 2.889 0.004 0.321 0.295
## V12 1.315 0.310 4.239 0.000 0.639 0.491
## V13 0.805 0.280 2.872 0.004 0.391 0.293
## V14 2.067 0.368 5.617 0.000 1.004 0.849
## V15 1.665 0.327 5.089 0.000 0.809 0.675
## V16 2.026 0.366 5.531 0.000 0.984 0.814
## Amb =~
## V17 1.000 9.393 0.114
## V18 -0.241 0.293 -0.822 0.411 -2.263 -0.010
## V19 4.676 76.384 0.061 0.951 43.919 5.609
## V20 -0.001 0.002 -0.478 0.633 -0.010 -0.006
## Socio =~
## V21 1.000 0.939 0.714
## V22 0.044 0.063 0.697 0.486 0.041 0.083
## V23 -1.090 0.566 -1.926 0.054 -1.024 -0.471
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.164 0.063 -2.608 0.009 -0.305 -0.305
## Amb -0.299 4.947 -0.060 0.952 -0.029 -0.029
## Socio 0.127 0.134 0.950 0.342 0.122 0.122
## TP ~~
## Amb 0.111 1.842 0.060 0.952 0.024 0.024
## Socio 0.025 0.057 0.448 0.654 0.056 0.056
## Amb ~~
## Socio 0.371 6.136 0.060 0.952 0.042 0.042
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.249 0.255 0.975 0.330 0.249 0.169
## .V2 1.035 0.160 6.467 0.000 1.035 0.703
## .V3 0.683 0.093 7.381 0.000 0.683 0.818
## .V4 0.833 0.106 7.895 0.000 0.833 0.957
## .V5 1.542 0.193 7.968 0.000 1.542 1.000
## .V6 0.744 0.097 7.678 0.000 0.744 0.759
## .V7 0.678 0.095 7.159 0.000 0.678 0.533
## .V8 0.511 0.075 6.775 0.000 0.511 0.438
## .V9 0.953 0.123 7.772 0.000 0.953 0.823
## .V10 1.226 0.155 7.927 0.000 1.226 0.956
## .V11 1.074 0.136 7.881 0.000 1.074 0.913
## .V12 1.283 0.167 7.677 0.000 1.283 0.758
## .V13 1.625 0.206 7.883 0.000 1.625 0.914
## .V14 0.390 0.070 5.544 0.000 0.390 0.279
## .V15 0.783 0.109 7.196 0.000 0.783 0.544
## .V16 0.492 0.080 6.119 0.000 0.492 0.337
## .V17 6743.598 1668.724 4.041 0.000 6743.598 0.987
## .V18 52662.603 6608.619 7.969 0.000 52662.603 1.000
## .V19 -1867.558 31443.491 -0.059 0.953 -1867.558 -30.461
## .V20 3.094 0.388 7.969 0.000 3.094 1.000
## .V21 0.847 0.462 1.833 0.067 0.847 0.490
## .V22 0.248 0.031 7.930 0.000 0.248 0.993
## .V23 3.671 0.707 5.191 0.000 3.671 0.778
## Car 1.222 0.312 3.918 0.000 1.000 1.000
## TP 0.236 0.084 2.805 0.005 1.000 1.000
## Amb 88.232 1444.765 0.061 0.951 1.000 1.000
## Socio 0.882 0.488 1.808 0.071 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 559.764 224 .000 .109† .614† .565† .110† 11591.464 11739.361
Modelo 9
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16
Amb =~ V17 + V18 + V19 + V20
Socio =~ V21 + V22 + V23 + Amb
'
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 780 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 50
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 512.557
## Degrees of freedom 226
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.104 0.910
## V2 0.599 NA 0.661 0.545
## V3 0.355 NA 0.392 0.429
## V4 0.171 NA 0.188 0.202
## V5 -0.023 NA -0.025 -0.020
## TP =~
## V6 1.000 0.482 0.487
## V7 1.581 NA 0.762 0.676
## V8 1.665 NA 0.803 0.743
## V9 0.937 NA 0.452 0.420
## V10 0.496 NA 0.239 0.211
## V11 0.667 NA 0.322 0.296
## V12 1.315 NA 0.634 0.487
## V13 0.813 NA 0.392 0.294
## V14 2.086 NA 1.005 0.850
## V15 1.689 NA 0.814 0.679
## V16 2.062 NA 0.994 0.822
## Amb =~
## V17 1.000 63.731 0.769
## V18 3.601 NA 229.505 0.997
## V19 0.052 NA 3.311 0.429
## V20 0.004 NA 0.238 0.135
## Socio =~
## V21 1.000 0.070 0.053
## V22 -0.264 NA -0.018 -0.037
## V23 0.903 NA 0.063 0.029
## Amb 1163.088 NA 1.275 1.275
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.162 NA -0.304 -0.304
## Socio 0.002 NA 0.022 0.022
## TP ~~
## Socio 0.004 NA 0.107 0.107
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.252 NA 0.252 0.172
## .V2 1.035 NA 1.035 0.703
## .V3 0.681 NA 0.681 0.816
## .V4 0.835 NA 0.835 0.959
## .V5 1.542 NA 1.542 1.000
## .V6 0.748 NA 0.748 0.763
## .V7 0.691 NA 0.691 0.543
## .V8 0.522 NA 0.522 0.447
## .V9 0.953 NA 0.953 0.824
## .V10 1.225 NA 1.225 0.955
## .V11 1.074 NA 1.074 0.912
## .V12 1.289 NA 1.289 0.762
## .V13 1.625 NA 1.625 0.914
## .V14 0.388 NA 0.388 0.277
## .V15 0.774 NA 0.774 0.539
## .V16 0.473 NA 0.473 0.324
## .V17 2813.670 NA 2813.670 0.409
## .V18 323.228 NA 323.228 0.006
## .V19 48.576 NA 48.576 0.816
## .V20 3.038 NA 3.038 0.982
## .V21 1.752 NA 1.752 0.997
## .V22 0.249 NA 0.249 0.999
## .V23 4.717 NA 4.717 0.999
## Car 1.219 NA 1.000 1.000
## TP 0.232 NA 1.000 1.000
## .Amb -2540.075 NA -0.625 -0.625
## Socio 0.005 NA 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 512.557 226 .000 .100† .671† .632† .102† 11540.256 11682.466
Modelo 10
modelo='
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16
Amb =~ V17 + V18 + V19 + V20
Socio =~ V21 + V22 + V23 + V24 + V25 + V27 + V28 + V29 + V30 + V31 + Amb
'
# V26
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 657 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 878.734
## Degrees of freedom 401
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car =~
## V1 1.000 1.101 0.908
## V2 0.602 0.156 3.847 0.000 0.663 0.546
## V3 0.358 0.103 3.463 0.001 0.394 0.431
## V4 0.171 0.087 1.967 0.049 0.189 0.202
## V5 -0.023 0.109 -0.210 0.834 -0.025 -0.020
## TP =~
## V6 1.000 0.482 0.487
## V7 1.582 0.313 5.058 0.000 0.763 0.676
## V8 1.665 0.315 5.284 0.000 0.803 0.743
## V9 0.938 0.247 3.793 0.000 0.452 0.420
## V10 0.496 0.231 2.151 0.031 0.239 0.211
## V11 0.669 0.231 2.897 0.004 0.322 0.297
## V12 1.313 0.313 4.192 0.000 0.633 0.487
## V13 0.813 0.283 2.869 0.004 0.392 0.294
## V14 2.084 0.374 5.569 0.000 1.005 0.850
## V15 1.690 0.333 5.070 0.000 0.815 0.680
## V16 2.063 0.375 5.506 0.000 0.994 0.823
## Amb =~
## V17 1.000 64.302 0.776
## V18 3.535 0.425 8.323 0.000 227.339 0.996
## V19 0.051 0.010 4.958 0.000 3.297 0.428
## V20 0.004 0.002 1.498 0.134 0.234 0.133
## Socio =~
## V21 1.000 0.067 0.050
## V22 -0.296 0.648 -0.456 0.648 -0.020 -0.039
## V23 0.611 2.368 0.258 0.796 0.041 0.019
## V24 -0.200 0.602 -0.331 0.740 -0.013 -0.025
## V25 0.059 0.150 0.393 0.694 0.004 0.032
## V27 0.169 0.472 0.359 0.720 0.011 0.028
## V28 1.717 2.551 0.673 0.501 0.114 0.110
## V29 1.442 2.074 0.695 0.487 0.096 0.138
## V30 1.465 2.007 0.730 0.465 0.098 0.306
## V31 0.084 1.016 0.082 0.934 0.006 0.006
## Amb 1239.413 2183.113 0.568 0.570 1.283 1.283
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.162 0.062 -2.599 0.009 -0.305 -0.305
## Socio 0.002 0.006 0.278 0.781 0.024 0.024
## TP ~~
## Socio 0.004 0.007 0.521 0.602 0.112 0.112
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V1 0.260 0.273 0.951 0.341 0.260 0.176
## .V2 1.033 0.164 6.293 0.000 1.033 0.702
## .V3 0.680 0.093 7.295 0.000 0.680 0.814
## .V4 0.835 0.106 7.894 0.000 0.835 0.959
## .V5 1.542 0.193 7.968 0.000 1.542 1.000
## .V6 0.748 0.097 7.686 0.000 0.748 0.763
## .V7 0.690 0.096 7.196 0.000 0.690 0.543
## .V8 0.522 0.076 6.829 0.000 0.522 0.448
## .V9 0.953 0.123 7.774 0.000 0.953 0.823
## .V10 1.225 0.155 7.926 0.000 1.225 0.955
## .V11 1.073 0.136 7.881 0.000 1.073 0.912
## .V12 1.291 0.168 7.687 0.000 1.291 0.763
## .V13 1.625 0.206 7.883 0.000 1.625 0.914
## .V14 0.389 0.070 5.554 0.000 0.389 0.278
## .V15 0.773 0.108 7.181 0.000 0.773 0.538
## .V16 0.472 0.079 6.014 0.000 0.472 0.323
## .V17 2730.902 524.450 5.207 0.000 2730.902 0.398
## .V18 452.532 4958.447 0.091 0.927 452.532 0.009
## .V19 48.507 6.182 7.846 0.000 48.507 0.817
## .V20 3.039 0.381 7.967 0.000 3.039 0.982
## .V21 1.748 0.219 7.975 0.000 1.748 0.997
## .V22 0.249 0.031 7.973 0.000 0.249 0.998
## .V23 4.719 0.592 7.970 0.000 4.719 1.000
## .V24 0.278 0.035 7.971 0.000 0.278 0.999
## .V25 0.015 0.002 7.972 0.000 0.015 0.999
## .V27 0.163 0.020 7.971 0.000 0.163 0.999
## .V28 1.064 0.134 7.955 0.000 1.064 0.988
## .V29 0.476 0.060 7.901 0.000 0.476 0.981
## .V30 0.092 0.015 6.028 0.000 0.092 0.906
## .V31 0.977 0.123 7.969 0.000 0.977 1.000
## Car 1.212 0.326 3.715 0.000 1.000 1.000
## TP 0.232 0.084 2.781 0.005 1.000 1.000
## .Amb -2674.025 7262.666 -0.368 0.713 -0.647 -0.647
## Socio 0.004 0.013 0.338 0.735 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 878.734 401 .000 .097† .561† .524† .103† 12758.761 12940.789
Modelo 11
modelo='
Amb =~ V17 + V18 + V19 + V20
Car =~ V1 + V2 + V3 + V4 + V5 + Amb
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16
Socio =~ V21 + V22 + V23 + V24 + V25 + V27 + V28 + V29 + V30 + V31
'
# V26
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 456 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 942.883
## Degrees of freedom 401
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Amb =~
## V17 1.000 0.019 0.000
## V18 -8662.531 NA -160.494 -0.701
## V19 -253.790 NA -4.702 -0.610
## V20 -4.532 NA -0.084 -0.048
## Car =~
## V1 1.000 1.087 0.896
## V2 0.620 NA 0.674 0.555
## V3 0.361 NA 0.392 0.429
## V4 0.178 NA 0.193 0.207
## V5 -0.023 NA -0.025 -0.020
## Amb 0.002 NA 0.093 0.093
## TP =~
## V6 1.000 0.485 0.490
## V7 1.582 NA 0.768 0.681
## V8 1.667 NA 0.809 0.749
## V9 0.930 NA 0.451 0.420
## V10 0.491 NA 0.238 0.211
## V11 0.659 NA 0.320 0.295
## V12 1.316 NA 0.639 0.491
## V13 0.809 NA 0.392 0.294
## V14 2.071 NA 1.005 0.850
## V15 1.668 NA 0.810 0.675
## V16 2.030 NA 0.985 0.815
## Socio =~
## V21 1.000 0.241 0.183
## V22 0.058 NA 0.014 0.028
## V23 -1.200 NA -0.289 -0.133
## V24 0.049 NA 0.012 0.022
## V25 0.054 NA 0.013 0.105
## V27 -0.218 NA -0.052 -0.130
## V28 -4.275 NA -1.029 -0.991
## V29 -1.696 NA -0.408 -0.586
## V30 -0.514 NA -0.124 -0.389
## V31 -0.641 NA -0.154 -0.156
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.165 NA -0.312 -0.312
## Socio -0.015 NA -0.058 -0.058
## TP ~~
## Socio -0.000 NA -0.000 -0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V17 6832.559 NA 6832.559 1.000
## .V18 26695.383 NA 26695.383 0.509
## .V19 37.379 NA 37.379 0.628
## .V20 3.088 NA 3.088 0.998
## .V1 0.290 NA 0.290 0.197
## .V2 1.018 NA 1.018 0.692
## .V3 0.681 NA 0.681 0.816
## .V4 0.833 NA 0.833 0.957
## .V5 1.542 NA 1.542 1.000
## .V6 0.745 NA 0.745 0.760
## .V7 0.682 NA 0.682 0.536
## .V8 0.511 NA 0.511 0.439
## .V9 0.954 NA 0.954 0.824
## .V10 1.225 NA 1.225 0.956
## .V11 1.075 NA 1.075 0.913
## .V12 1.283 NA 1.283 0.759
## .V13 1.624 NA 1.624 0.913
## .V14 0.388 NA 0.388 0.277
## .V15 0.782 NA 0.782 0.544
## .V16 0.490 NA 0.490 0.336
## .V21 1.672 NA 1.672 0.967
## .V22 0.249 NA 0.249 0.999
## .V23 4.637 NA 4.637 0.982
## .V24 0.278 NA 0.278 0.999
## .V25 0.015 NA 0.015 0.989
## .V27 0.160 NA 0.160 0.983
## .V28 0.019 NA 0.019 0.018
## .V29 0.319 NA 0.319 0.657
## .V30 0.086 NA 0.086 0.849
## .V31 0.953 NA 0.953 0.976
## .Amb 0.000 NA 0.991 0.991
## Car 1.181 NA 1.000 1.000
## TP 0.236 NA 1.000 1.000
## Socio 0.058 NA 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 942.883 401 .000 .103† .502† .460† .111† 12822.910 13004.938
Modelo 12
modelo='
Amb =~ V17 + V18 + V19 + V20
Car =~ V1 + V2 + V3 + V4 + V5
TP =~ V6 + V7 + V8 + V9 + V10 + V11 + V12 + V13 + V14 + V15 + V16 + Amb
Socio =~ V21 + V22 + V23 + V24 + V25 + V27 + V28 + V29 + V30 + V31
'
# V26
fit1 = cfa(modelo,data = df)
summary(fit1,standardized = TRUE)
## lavaan 0.6-9 ended normally after 268 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 64
##
## Number of observations 127
##
## Model Test User Model:
##
## Test statistic 901.600
## Degrees of freedom 401
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Amb =~
## V17 1.000 12.115 0.147
## V18 -0.235 0.319 -0.736 0.462 -2.843 -0.012
## V19 2.749 43.052 0.064 0.949 33.298 4.286
## V20 -0.002 0.003 -0.917 0.359 -0.028 -0.016
## Car =~
## V1 1.000 1.096 0.904
## V2 0.609 0.157 3.871 0.000 0.668 0.550
## V3 0.357 0.103 3.463 0.001 0.392 0.429
## V4 0.174 0.088 1.987 0.047 0.191 0.205
## V5 -0.022 0.110 -0.198 0.843 -0.024 -0.019
## TP =~
## V6 1.000 0.487 0.492
## V7 1.585 0.309 5.130 0.000 0.772 0.684
## V8 1.664 0.311 5.354 0.000 0.810 0.750
## V9 0.929 0.244 3.810 0.000 0.452 0.420
## V10 0.485 0.228 2.128 0.033 0.236 0.209
## V11 0.658 0.228 2.887 0.004 0.320 0.295
## V12 1.317 0.310 4.251 0.000 0.641 0.493
## V13 0.806 0.280 2.878 0.004 0.392 0.294
## V14 2.063 0.367 5.626 0.000 1.004 0.849
## V15 1.659 0.326 5.091 0.000 0.807 0.674
## V16 2.021 0.365 5.539 0.000 0.984 0.814
## Amb 0.834 13.353 0.062 0.950 0.034 0.034
## Socio =~
## V21 1.000 0.241 0.183
## V22 0.058 0.188 0.310 0.756 0.014 0.028
## V23 -1.199 0.987 -1.215 0.224 -0.289 -0.133
## V24 0.049 0.198 0.250 0.803 0.012 0.023
## V25 0.054 0.053 1.023 0.306 0.013 0.105
## V27 -0.218 0.182 -1.197 0.231 -0.052 -0.130
## V28 -4.271 2.244 -1.904 0.057 -1.028 -0.991
## V29 -1.696 0.842 -2.013 0.044 -0.408 -0.586
## V30 -0.514 0.270 -1.902 0.057 -0.124 -0.389
## V31 -0.640 0.476 -1.344 0.179 -0.154 -0.156
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## Car ~~
## TP -0.166 0.063 -2.631 0.009 -0.310 -0.310
## Socio -0.016 0.027 -0.585 0.558 -0.060 -0.060
## TP ~~
## Socio 0.000 0.011 0.002 0.999 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V17 6684.033 2444.018 2.735 0.006 6684.033 0.979
## .V18 52659.431 6608.634 7.968 0.000 52659.431 1.000
## .V19 -1048.408 17343.492 -0.060 0.952 -1048.408 -17.370
## .V20 3.094 0.388 7.966 0.000 3.094 1.000
## .V1 0.269 0.270 0.999 0.318 0.269 0.183
## .V2 1.026 0.164 6.252 0.000 1.026 0.697
## .V3 0.682 0.093 7.307 0.000 0.682 0.816
## .V4 0.834 0.106 7.890 0.000 0.834 0.958
## .V5 1.542 0.193 7.968 0.000 1.542 1.000
## .V6 0.744 0.097 7.677 0.000 0.744 0.758
## .V7 0.677 0.095 7.156 0.000 0.677 0.532
## .V8 0.510 0.075 6.773 0.000 0.510 0.438
## .V9 0.953 0.123 7.773 0.000 0.953 0.823
## .V10 1.226 0.155 7.927 0.000 1.226 0.956
## .V11 1.075 0.136 7.882 0.000 1.075 0.913
## .V12 1.280 0.167 7.675 0.000 1.280 0.757
## .V13 1.625 0.206 7.882 0.000 1.625 0.914
## .V14 0.390 0.070 5.552 0.000 0.390 0.279
## .V15 0.785 0.109 7.202 0.000 0.785 0.546
## .V16 0.494 0.081 6.129 0.000 0.494 0.338
## .V21 1.672 0.210 7.951 0.000 1.672 0.966
## .V22 0.249 0.031 7.969 0.000 0.249 0.999
## .V23 4.637 0.582 7.963 0.000 4.637 0.982
## .V24 0.278 0.035 7.969 0.000 0.278 0.999
## .V25 0.015 0.002 7.966 0.000 0.015 0.989
## .V27 0.160 0.020 7.963 0.000 0.160 0.983
## .V28 0.020 0.226 0.087 0.931 0.020 0.018
## .V29 0.319 0.054 5.943 0.000 0.319 0.657
## .V30 0.086 0.011 7.617 0.000 0.086 0.849
## .V31 0.953 0.120 7.958 0.000 0.953 0.976
## .Amb 146.601 2297.095 0.064 0.949 0.999 0.999
## Car 1.202 0.323 3.718 0.000 1.000 1.000
## TP 0.237 0.084 2.810 0.005 1.000 1.000
## Socio 0.058 0.057 1.008 0.313 1.000 1.000
semPaths(fit1,
whatLabels = "std",
edge.label.cex=.6,
node.width = 1.1
)
summary(compareFit(fit1, fit2, nested=FALSE))
## ####################### Model Fit Indices ###########################
## chisq df pvalue rmsea cfi tli srmr aic bic
## fit2 205.993† 14 .000 .329 .000 -1.127 .182 5445.918† 5508.490†
## fit1 901.600 401 .000 .099† .540† .501† .107† 12781.627 12963.655