library(rio)
cocina=import ("INEI_cocina.xlsx")
cocina=cocina[complete.cases(cocina),]
Porcentaje casos positivos
cocina$si_elec_por=cocina$si_elec/cocina$total_elec
cocina$si_gas_por=cocina$si_gas/cocina$total_gas
cocina$si_carbon_por=cocina$si_carbon/cocina$total_carbon
cocina$si_lena_por=cocina$si_lena/cocina$total_lena
Selección de columnas
names(cocina)
## [1] "Código" "DEPARTAMENTO" "PROVINCIA" "no_elec"
## [5] "si_elec" "total_elec" "no_gas" "si_gas"
## [9] "total_gas" "no_carbon" "si_carbon" "total_carbon"
## [13] "no_lena" "si_lena" "total_lena" "si_elec_por"
## [17] "si_gas_por" "si_carbon_por" "si_lena_por"
datos_por=cocina[,c(16,17,18,19)]
str(datos_por)
## 'data.frame': 196 obs. of 4 variables:
## $ si_elec_por : num 0.03743 0.00786 0.01587 0.00142 0.00682 ...
## $ si_gas_por : num 0.694 0.484 0.596 0.156 0.48 ...
## $ si_carbon_por: num 0.01148 0.01412 0.00742 0.00264 0.0025 ...
## $ si_lena_por : num 0.528 0.641 0.7 0.893 0.861 ...
corMatrix=polycor::hetcor(datos_por)$correlations
round(corMatrix,2)
## si_elec_por si_gas_por si_carbon_por si_lena_por
## si_elec_por 1.00 0.47 0.10 -0.46
## si_gas_por 0.47 1.00 0.23 -0.87
## si_carbon_por 0.10 0.23 1.00 -0.31
## si_lena_por -0.46 -0.87 -0.31 1.00
PRUEBAS
library(psych)
psych::KMO(corMatrix)
## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.63
## MSA for each item =
## si_elec_por si_gas_por si_carbon_por si_lena_por
## 0.91 0.59 0.72 0.59
Adecuación
cortest.bartlett(corMatrix,n=nrow(datos_por))$p.value>0.05
## [1] FALSE
library(matrixcalc)
is.singular.matrix(corMatrix)
## [1] FALSE
Determinar factores
fa.parallel(datos_por, fa = 'fa',correct = T,plot = F)
## Parallel analysis suggests that the number of factors = 1 and the number of components = NA
Varimax
library(GPArotation)
##
## Adjuntando el paquete: 'GPArotation'
## The following objects are masked from 'package:psych':
##
## equamax, varimin
fa_result <- fa(datos_por, nfactors = 1, rotate = "varimax", fm = "ml")
print(fa_result)
## Factor Analysis using method = ml
## Call: fa(r = datos_por, nfactors = 1, rotate = "varimax", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 h2 u2 com
## si_elec_por 0.49 0.24 0.757 1
## si_gas_por 0.91 0.83 0.169 1
## si_carbon_por 0.30 0.09 0.910 1
## si_lena_por -0.95 0.90 0.099 1
##
## ML1
## SS loadings 2.07
## Proportion Var 0.52
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 6 with the objective function = 1.76 with Chi Square = 338.91
## df of the model are 2 and the objective function was 0.03
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic n.obs is 196 with the empirical chi square 2.43 with prob < 0.3
## The total n.obs was 196 with Likelihood Chi Square = 5.98 with prob < 0.05
##
## Tucker Lewis Index of factoring reliability = 0.964
## RMSEA index = 0.101 and the 90 % confidence intervals are 0 0.199
## BIC = -4.58
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML1
## Correlation of (regression) scores with factors 0.97
## Multiple R square of scores with factors 0.94
## Minimum correlation of possible factor scores 0.87
Oblimin
library(GPArotation)
fa_result2 <- fa(datos_por, nfactors = 1, rotate = "oblimin", fm = "ml")
print(fa_result)
## Factor Analysis using method = ml
## Call: fa(r = datos_por, nfactors = 1, rotate = "varimax", fm = "ml")
## Standardized loadings (pattern matrix) based upon correlation matrix
## ML1 h2 u2 com
## si_elec_por 0.49 0.24 0.757 1
## si_gas_por 0.91 0.83 0.169 1
## si_carbon_por 0.30 0.09 0.910 1
## si_lena_por -0.95 0.90 0.099 1
##
## ML1
## SS loadings 2.07
## Proportion Var 0.52
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 6 with the objective function = 1.76 with Chi Square = 338.91
## df of the model are 2 and the objective function was 0.03
##
## The root mean square of the residuals (RMSR) is 0.03
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic n.obs is 196 with the empirical chi square 2.43 with prob < 0.3
## The total n.obs was 196 with Likelihood Chi Square = 5.98 with prob < 0.05
##
## Tucker Lewis Index of factoring reliability = 0.964
## RMSEA index = 0.101 and the 90 % confidence intervals are 0 0.199
## BIC = -4.58
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## ML1
## Correlation of (regression) scores with factors 0.97
## Multiple R square of scores with factors 0.94
## Minimum correlation of possible factor scores 0.87
print(fa_result$loadings,cutoff = 0.5)
##
## Loadings:
## ML1
## si_elec_por
## si_gas_por 0.912
## si_carbon_por
## si_lena_por -0.949
##
## ML1
## SS loadings 2.065
## Proportion Var 0.516
print(fa_result2$loadings,cutoff = 0.5)
##
## Loadings:
## ML1
## si_elec_por
## si_gas_por 0.912
## si_carbon_por
## si_lena_por -0.949
##
## ML1
## SS loadings 2.065
## Proportion Var 0.516