práctica para control 3
Luis Dávila
2022-11-24
##Limpieza y jalar datos
library(rio)
demo = import("demo.csv", encoding = "UTF-8")
idh = import("idh2.csv", encoding = "UTF-8")demo[,]=lapply(demo[,], trimws,whitespace = "[\\h\\v]")
idh[,]=lapply(idh[,], trimws,whitespace = "[\\h\\v]")
str(demo)## 'data.frame': 167 obs. of 9 variables:
## $ Puesto : chr "1" "2" "3" "4" ...
## $ País : chr "Noruega" "Nueva Zelanda" "Finlandia" "Suecia" ...
## $ Puntuación : chr "9,75" "9,37" "9,27" "9,26" ...
## $ Proceso electoraly pluralismo: chr "10,00" "10,00" "10,00" "9,58" ...
## $ Funcionamientodel gobierno : chr "9,64" "8,93" "9,29" "9,29" ...
## $ Participaciónpolítica : chr "10,00" "9,44" "8,89" "8,33" ...
## $ Culturapolítica : chr "10,00" "8,75" "8,75" "10,00" ...
## $ Derechosciviles : chr "9,12" "9,71" "9,41" "9,12" ...
## $ Categoría : chr "Democracia plena" "Democracia plena" "Democracia plena" "Democracia plena" ...names(idh)## [1] "País >> IDH Muy Alto >> IDH Alto >> IDH Medio >> IDH Bajo"
## [2] "Componentes para Indice de Desarrollo Humano >> Esperanza de Vida (85 años) >> IDH Muy Alto >> IDH Alto >> IDH Medio >> IDH Bajo"
## [3] "Componentes para Indice de Desarrollo Humano >> Años Esperados de Escolaridad ( 18 años) >> IDH Muy Alto >> IDH Alto >> IDH Medio >> IDH Bajo"
## [4] "Componentes para Indice de Desarrollo Humano >> Años Promedio de Escolaridad (15 años) >> IDH Muy Alto >> IDH Alto >> IDH Medio >> IDH Bajo"
## [5] "Componentes para Indice de Desarrollo Humano >> PIB per capita PPP (2017) (75,000) >> IDH Muy Alto >> IDH Alto >> IDH Medio >> IDH Bajo"names(demo)## [1] "Puesto" "País"
## [3] "Puntuación" "Proceso electoraly pluralismo"
## [5] "Funcionamientodel gobierno" "Participaciónpolítica"
## [7] "Culturapolítica" "Derechosciviles"
## [9] "Categoría"colnames(idh)[1] = "Pais"
colnames(idh)[2] = "EsperanzaVida"
colnames(idh)[3] = "EscolaridadDuracion"
colnames(idh)[4] = "EscolaridadPromedio"
colnames(idh)[5] = "PBI"
names(idh)## [1] "Pais" "EsperanzaVida" "EscolaridadDuracion"
## [4] "EscolaridadPromedio" "PBI"library(stringr)
names(demo)=str_to_title(names(demo))library(stringi)
names(demo)=gsub(" ","",names(demo))
names(demo)=stri_trans_general(str = names(demo),
id = "Latin-ASCII")
idh[,]=lapply(idh[,], trimws,whitespace = "[\\h\\v]")
demo[,]=lapply(demo[,], trimws,whitespace = "[\\h\\v]")
names(demo)## [1] "Puesto" "Pais"
## [3] "Puntuacion" "ProcesoElectoralyPluralismo"
## [5] "FuncionamientodelGobierno" "Participacionpolitica"
## [7] "Culturapolitica" "Derechosciviles"
## [9] "Categoria"names(idh)## [1] "Pais" "EsperanzaVida" "EscolaridadDuracion"
## [4] "EscolaridadPromedio" "PBI"str(demo)## 'data.frame': 167 obs. of 9 variables:
## $ Puesto : chr "1" "2" "3" "4" ...
## $ Pais : chr "Noruega" "Nueva Zelanda" "Finlandia" "Suecia" ...
## $ Puntuacion : chr "9,75" "9,37" "9,27" "9,26" ...
## $ ProcesoElectoralyPluralismo: chr "10,00" "10,00" "10,00" "9,58" ...
## $ FuncionamientodelGobierno : chr "9,64" "8,93" "9,29" "9,29" ...
## $ Participacionpolitica : chr "10,00" "9,44" "8,89" "8,33" ...
## $ Culturapolitica : chr "10,00" "8,75" "8,75" "10,00" ...
## $ Derechosciviles : chr "9,12" "9,71" "9,41" "9,12" ...
## $ Categoria : chr "Democracia plena" "Democracia plena" "Democracia plena" "Democracia plena" ...str(idh)## 'data.frame': 191 obs. of 5 variables:
## $ Pais : chr "Suiza" "Noruega" "Islandia" "Hong Kong" ...
## $ EsperanzaVida : chr "84" "83.2" "82.7" "85.5" ...
## $ EscolaridadDuracion: chr "16.5" "18.2" "19.2" "17.3" ...
## $ EscolaridadPromedio: chr "13.9" "13" "13.8" "12.2" ...
## $ PBI : chr "66,933" "64,660" "55,782" "62,607" ...idh$PBI=gsub(',','',idh$PBI)
idh[,-1]=lapply(idh[,-1], as.numeric)## Warning in lapply(idh[, -1], as.numeric): NAs introducidos por coerciónstr(idh)## 'data.frame': 191 obs. of 5 variables:
## $ Pais : chr "Suiza" "Noruega" "Islandia" "Hong Kong" ...
## $ EsperanzaVida : num 84 83.2 82.7 85.5 84.5 81.4 83 82 80.6 81.7 ...
## $ EscolaridadDuracion: num 16.5 18.2 19.2 17.3 21.1 18.7 19.4 18.9 17 18.7 ...
## $ EscolaridadPromedio: num 13.9 13 13.8 12.2 12.7 13 12.6 11.6 14.1 12.6 ...
## $ PBI : num 66933 64660 55782 62607 49238 ...demo[,-c(2,9)]=lapply(demo[,-c(2,9)],
function(x){gsub(",",".",x)})
demo[,-c(2,9)]=lapply(demo[,-c(2,9)], as.numeric)
str(demo)## 'data.frame': 167 obs. of 9 variables:
## $ Puesto : num 1 2 3 4 5 6 7 8 9 9 ...
## $ Pais : chr "Noruega" "Nueva Zelanda" "Finlandia" "Suecia" ...
## $ Puntuacion : num 9.75 9.37 9.27 9.26 9.18 9.09 9 8.99 8.9 8.9 ...
## $ ProcesoElectoralyPluralismo: num 10 10 10 9.58 10 10 10 10 10 9.58 ...
## $ FuncionamientodelGobierno : num 9.64 8.93 9.29 9.29 8.21 8.93 7.86 9.64 8.57 8.93 ...
## $ Participacionpolitica : num 10 9.44 8.89 8.33 8.89 8.33 8.33 7.78 7.78 7.78 ...
## $ Culturapolitica : num 10 8.75 8.75 10 9.38 9.38 9.38 8.13 8.75 9.38 ...
## $ Derechosciviles : num 9.12 9.71 9.41 9.12 9.41 8.82 9.41 9.41 9.41 8.82 ...
## $ Categoria : chr "Democracia plena" "Democracia plena" "Democracia plena" "Democracia plena" ...idh[!complete.cases(idh),]## Pais EsperanzaVida EscolaridadDuracion EscolaridadPromedio PBI
## 151 Camerún 60.3 NA 6.2 3621demo[!complete.cases(demo),]## [1] Puesto Pais
## [3] Puntuacion ProcesoElectoralyPluralismo
## [5] FuncionamientodelGobierno Participacionpolitica
## [7] Culturapolitica Derechosciviles
## [9] Categoria
## <0 rows> (or 0-length row.names)idh=idh[complete.cases(idh),]
demo=demo[complete.cases(demo),]
idh$Pais=stri_trans_general(str = idh$Pais,
id = "Latin-ASCII")
demo[,c(2,9)]=lapply(demo[,c(2,9)],
stri_trans_general,
id = "Latin-ASCII")
setdiff(demo$Pais,idh$Pais)## [1] "Republica de China" "" "Suazilandia"
## [4] "Camerun" "Rd del Congo" "Corea del Norte"setdiff(idh$Pais,demo$Pais)## [1] "Liechtenstein" "Andorra"
## [3] "San Marino" "Brunei Darusalam"
## [5] "Bahamas" "Panama"
## [7] "Granada" "Barbados"
## [9] "Antigua y Barbuda" "Seychelles"
## [11] "San Cristobal y Nieves" "Palaos"
## [13] "San Vicente y las Granadinas" "Maldivas"
## [15] "Tonga" "Dominica"
## [17] "Santa Lucia" "Palestina"
## [19] "Samoa" "Belice"
## [21] "Tuvalu" "Islas Marshall"
## [23] "Micronesia" "Kiribati"
## [25] "Santo Tome y Principe" "Vanuatu"
## [27] "Esuatini" "Islas Salomon"
## [29] "Pakistan" "Republica Democratica del Congo"
## [31] "Sudan del Sur"##Merge
control3=merge(idh,demo)
str(control3)## 'data.frame': 159 obs. of 13 variables:
## $ Pais : chr "Afganistan" "Albania" "Alemania" "Angola" ...
## $ EsperanzaVida : num 62 76.5 80.6 61.6 76.9 76.4 75.4 72 84.5 81.6 ...
## $ EscolaridadDuracion : num 10.3 14.4 17 12.2 16.1 14.6 17.9 13.1 21.1 16 ...
## $ EscolaridadPromedio : num 3 11.3 14.1 5.4 11.3 8.1 11.1 11.3 12.7 12.3 ...
## $ PBI : num 1824 14131 54534 5466 46112 ...
## $ Puesto : num 167 68 15 122 152 113 50 89 9 20 ...
## $ Puntuacion : num 0.32 6.11 8.67 3.37 2.08 3.77 6.81 5.49 8.9 8.07 ...
## $ ProcesoElectoralyPluralismo: num 0 7 9.58 1.33 0 3.08 9.17 7.5 10 9.58 ...
## $ FuncionamientodelGobierno : num 0.07 6.43 8.21 2.86 3.57 2.5 5 5.71 8.57 6.79 ...
## $ Participacionpolitica : num 0 4.44 8.33 5 2.22 4.44 7.22 6.11 7.78 8.89 ...
## $ Culturapolitica : num 1.25 5.63 8.13 5 3.13 5 5 3.13 8.75 6.88 ...
## $ Derechosciviles : num 0.29 7.06 9.12 2.65 1.47 3.82 7.65 5 9.41 8.24 ...
## $ Categoria : chr "Regimen autoritario" "Democracia deficiente" "Democracia plena" "Regimen autoritario" ...dontselect=c("Pais","Puesto","Puntuacion",'Categoria')
select=setdiff(names(control3),dontselect)
theData=control3[,select]###Análisis factorial exploratorio (EFA) ##Matriz de correlación
library(polycor)
library(ggcorrplot)## Loading required package: ggplot2corMatrix=polycor::hetcor(theData)$correlations
ggcorrplot(corMatrix)
##¿Se pueden factorizar?
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:polycor':
##
## polyserialpsych::KMO(corMatrix) ## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.9
## MSA for each item =
## EsperanzaVida EscolaridadDuracion
## 0.90 0.90
## EscolaridadPromedio PBI
## 0.90 0.89
## ProcesoElectoralyPluralismo FuncionamientodelGobierno
## 0.83 0.95
## Participacionpolitica Culturapolitica
## 0.95 0.88
## Derechosciviles
## 0.86##¿La matriz de correlación es adecuada? Pruebas para saberlo
library(matrixcalc)
cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05## [1] FALSEis.singular.matrix(corMatrix)## [1] FALSE##¿Cuántos factores se pueden tener?
fa.parallel(theData, fa = 'fa',correct = T,plot = F)## 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 carefully## 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 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 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 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 carefully## 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.## Parallel analysis suggests that the number of factors = 2 and the number of components = NA##Redimensionar a los factores que nos sugiere
library(GPArotation)
resfa <- fa(theData,
nfactors = 2,
cor = 'mixed',
rotate = "varimax",
fm="minres")
print(resfa$loadings)##
## Loadings:
## MR1 MR2
## EsperanzaVida 0.278 0.868
## EscolaridadDuracion 0.392 0.816
## EscolaridadPromedio 0.300 0.792
## PBI 0.290 0.804
## ProcesoElectoralyPluralismo 0.909 0.228
## FuncionamientodelGobierno 0.762 0.492
## Participacionpolitica 0.776 0.333
## Culturapolitica 0.524 0.401
## Derechosciviles 0.909 0.319
##
## MR1 MR2
## SS loadings 3.516 3.360
## Proportion Var 0.391 0.373
## Cumulative Var 0.391 0.764print(resfa$loadings,cutoff = 0.5)##
## Loadings:
## MR1 MR2
## EsperanzaVida 0.868
## EscolaridadDuracion 0.816
## EscolaridadPromedio 0.792
## PBI 0.804
## ProcesoElectoralyPluralismo 0.909
## FuncionamientodelGobierno 0.762
## Participacionpolitica 0.776
## Culturapolitica 0.524
## Derechosciviles 0.909
##
## MR1 MR2
## SS loadings 3.516 3.360
## Proportion Var 0.391 0.373
## Cumulative Var 0.391 0.764##Gráfico de las variables ya redimensionadas
fa.diagram(resfa,main = "Resultados del EFA")
##¿Qué variable aportó más?
sort(resfa$communality)## Culturapolitica Participacionpolitica
## 0.4354346 0.7130596
## EscolaridadPromedio PBI
## 0.7165864 0.7313910
## EscolaridadDuracion FuncionamientodelGobierno
## 0.8204914 0.8221573
## EsperanzaVida ProcesoElectoralyPluralismo
## 0.8298232 0.8787391
## Derechosciviles
## 0.9286240##¿Qué variables contribuyeron a más de un factor?
sort(resfa$complexity)## ProcesoElectoralyPluralismo EsperanzaVida
## 1.125234 1.202724
## Derechosciviles PBI
## 1.242485 1.256253
## EscolaridadPromedio Participacionpolitica
## 1.280643 1.356853
## EscolaridadDuracion FuncionamientodelGobierno
## 1.438349 1.710639
## Culturapolitica
## 1.870422Dos índices que resuman los valores encontrados
library(magrittr)
as.data.frame(resfa$scores)%>%head()## MR1 MR2
## 1 -1.7791494 -0.8633563
## 2 0.3172115 0.2849561
## 3 1.0373841 1.1681259
## 4 -0.7575112 -0.6720571
## 5 -1.9986785 1.5867070
## 6 -0.8755320 0.4176185##Gráficos
control3$demos_efa=resfa$scores[,1]
control3$desahu_efa=resfa$scores[,2]
ggplot(data=control3,aes(x=Puntuacion,y=demos_efa)) + geom_point() + theme_minimal() + labs(x="Indice de Democracia (original)", y="Indice de Democracia EFA")
library(BBmisc)##
## Attaching package: 'BBmisc'## The following object is masked from 'package:base':
##
## isFALSEefa_scores_ok=normalize(resfa$scores,
method = "range",
margin=2, # by column
range = c(0, 10))
control3$demos_efa_ok=efa_scores_ok[,1]
control3$desahu_efa_ok=efa_scores_ok[,2]
ggplot(data=control3,aes(x=Puntuacion,y=demos_efa_ok)) + geom_point() + theme_minimal() + labs(x="Indice de Democracia (original)", y="Indice de Democracia EFA (cambiado)")
###Análisis factorial confirmatorio (CFA)
modelCFA <- ' democracia =~ ProcesoElectoralyPluralismo + FuncionamientodelGobierno + Participacionpolitica + Culturapolitica + Derechosciviles
desaHumano=~EsperanzaVida+EscolaridadDuracion+EscolaridadPromedio+PBI'theDataNorm=scale(theData)
library(lavaan)## This is lavaan 0.6-12
## lavaan is FREE software! Please report any bugs.##
## Attaching package: 'lavaan'## The following object is masked from 'package:psych':
##
## cor2covcfa_fit <- cfa(modelCFA, data=theDataNorm,
std.lv=TRUE,
missing="fiml")
summary(cfa_fit)## lavaan 0.6-12 ended normally after 30 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 28
##
## Number of observations 159
## Number of missing patterns 1
##
## Model Test User Model:
##
## Test statistic 122.450
## Degrees of freedom 26
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## democracia =~
## PrcsElctrlyPlr 0.927 0.060 15.466 0.000
## FuncnmntdlGbrn 0.880 0.062 14.090 0.000
## Participcnpltc 0.836 0.064 12.991 0.000
## Culturapolitic 0.637 0.072 8.894 0.000
## Derechoscivils 0.961 0.058 16.467 0.000
## desaHumano =~
## EsperanzaVida 0.891 0.063 14.250 0.000
## EscolariddDrcn 0.920 0.061 15.053 0.000
## EscolariddPrmd 0.868 0.063 13.672 0.000
## PBI 0.823 0.066 12.520 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## democracia ~~
## desaHumano 0.673 0.048 13.946 0.000
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|)
## .PrcsElctrlyPlr -0.000 0.079 -0.000 1.000
## .FuncnmntdlGbrn -0.000 0.079 -0.000 1.000
## .Participcnpltc 0.000 0.079 0.000 1.000
## .Culturapolitic 0.000 0.079 0.000 1.000
## .Derechoscivils 0.000 0.079 0.000 1.000
## .EsperanzaVida 0.000 0.079 0.000 1.000
## .EscolariddDrcn -0.000 0.079 -0.000 1.000
## .EscolariddPrmd -0.000 0.079 -0.000 1.000
## .PBI -0.000 0.079 -0.000 1.000
## democracia 0.000
## desaHumano 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .PrcsElctrlyPlr 0.134 0.020 6.676 0.000
## .FuncnmntdlGbrn 0.220 0.029 7.459 0.000
## .Participcnpltc 0.294 0.037 7.995 0.000
## .Culturapolitic 0.587 0.068 8.651 0.000
## .Derechoscivils 0.071 0.016 4.356 0.000
## .EsperanzaVida 0.200 0.032 6.349 0.000
## .EscolariddDrcn 0.148 0.028 5.330 0.000
## .EscolariddPrmd 0.241 0.034 7.066 0.000
## .PBI 0.317 0.042 7.458 0.000
## democracia 1.000
## desaHumano 1.000##¿Qué tan bien salió el modelo?
allParamCFA=parameterEstimates(cfa_fit,standardized = T)
allFitCFA=as.list(fitMeasures(cfa_fit))##ChiSquare - pvalue debe ser mayor a 0.05
allFitCFA[c("chisq", "df", "pvalue")]## $chisq
## [1] 122.4497
##
## $df
## [1] 26
##
## $pvalue
## [1] 1.84297e-14##Índice de Tucker Lewis - debe ser mayor a 0.9
allFitCFA$tli## [1] 0.9063776##La raíz del error cuadrático medio de aproximación debe ser menor a 0.05
allFitCFA[c('rmsea.ci.lower','rmsea' ,'rmsea.ci.upper')] ## $rmsea.ci.lower
## [1] 0.1260951
##
## $rmsea
## [1] 0.1527444
##
## $rmsea.ci.upper
## [1] 0.1805155##Aunque no se cumpla a cabalidad, igual se calculan las puntuaciones
scorescfa=normalize(lavPredict(cfa_fit),
method = "range",
margin=2, # by column
range = c(0, 10))
control3$demos_cfa_ok=scorescfa[,1]
control3$desahu_cfa_ok=scorescfa[,2]ggplot(data=control3,aes(x=Puntuacion,y=demos_cfa_ok)) + geom_point() + theme_minimal() + labs(x="Indice de Democracia (original)", y="Indice de Democracia CFA (cambiado)")
##Resultados del CFA
library(lavaanPlot)
lavaanPlot(model = cfa_fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = T)##Regresión normal
hipotesis=formula(demos_cfa_ok~desahu_cfa_ok)
reg1=lm(hipotesis, data=control3)
summary(reg1)##
## Call:
## lm(formula = hipotesis, data = control3)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.098 -1.188 0.575 1.208 3.484
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5856 0.3488 4.546 1.09e-05 ***
## desahu_cfa_ok 0.7655 0.0616 12.428 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.911 on 157 degrees of freedom
## Multiple R-squared: 0.4959, Adjusted R-squared: 0.4927
## F-statistic: 154.4 on 1 and 157 DF, p-value: < 2.2e-16modelSEM <- ' democracia =~ ProcesoElectoralyPluralismo + FuncionamientodelGobierno + Participacionpolitica + Culturapolitica + Derechosciviles
desaHumano=~EsperanzaVida+EscolaridadDuracion+EscolaridadPromedio+PBI
democracia~desaHumano'sem_fit <- sem(modelSEM,
data=theDataNorm)
summary(sem_fit)## lavaan 0.6-12 ended normally after 32 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 19
##
## Number of observations 159
##
## Model Test User Model:
##
## Test statistic 122.450
## Degrees of freedom 26
## 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|)
## democracia =~
## PrcsElctrlyPlr 1.000
## FuncnmntdlGbrn 0.949 0.051 18.502 0.000
## Participcnpltc 0.902 0.056 16.196 0.000
## Culturapolitic 0.687 0.070 9.762 0.000
## Derechoscivils 1.036 0.042 24.599 0.000
## desaHumano =~
## EsperanzaVida 1.000
## EscolariddDrcn 1.033 0.058 17.897 0.000
## EscolariddPrmd 0.974 0.062 15.783 0.000
## PBI 0.924 0.065 14.167 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## democracia ~
## desaHumano 0.701 0.074 9.410 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .PrcsElctrlyPlr 0.134 0.021 6.462 0.000
## .FuncnmntdlGbrn 0.220 0.029 7.662 0.000
## .Participcnpltc 0.294 0.036 8.090 0.000
## .Culturapolitic 0.587 0.068 8.683 0.000
## .Derechoscivils 0.071 0.016 4.318 0.000
## .EsperanzaVida 0.200 0.031 6.528 0.000
## .EscolariddDrcn 0.148 0.027 5.496 0.000
## .EscolariddPrmd 0.241 0.034 7.061 0.000
## .PBI 0.317 0.041 7.665 0.000
## .democracia 0.470 0.065 7.229 0.000
## desaHumano 0.794 0.111 7.140 0.000##Resultado en gráfico
lavaanPlot(model = sem_fit,
node_options = list(shape = "box",
fontname = "Helvetica"),
edge_options = list(color = "grey"), coefs = T,stand = T)##Mismo resultado pero otro gráfico
library(semPlot)
semPaths(sem_fit, residuals=F,
sizeMan=7,sizeLat=12,
what = "std",
nCharNodes=10,
posCol=c("skyblue4", "red"),
edge.color="orange",
edge.label.cex=1.2,layout="circle2")