FINAL

library(rio)
library(magrittr)
library(polycor)
library(psych)

## 
## Adjuntando el paquete: 'psych'

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

library(matrixcalc)
library(GPArotation)

## 
## Adjuntando el paquete: 'GPArotation'

## The following objects are masked from 'package:psych':
## 
##     equamax, varimin

library(BBmisc)

## 
## Adjuntando el paquete: 'BBmisc'

## The following object is masked from 'package:base':
## 
##     isFALSE

DATA

energia = import("energia_ok.csv")

PORCENTAJES

energia$pct_ELEC = energia$SI_ELEC / energia$TOTAL_ELEC
energia$pct_BGAS = energia$SI_BGAS / energia$TOTAL_BGAS
energia$pct_CARBON = energia$SI_CARBON / energia$TOTAL_CARBON
energia$pct_LENA = energia$SI_LENA / energia$TOTAL_LENA

data for factorial

select = c("pct_ELEC", "pct_BGAS", "pct_CARBON","pct_LENA")
theData = energia [,select]
head(theData,10)

##       pct_ELEC  pct_BGAS  pct_CARBON  pct_LENA
## 1  0.037425833 0.6938123 0.011475517 0.5281998
## 2  0.007863632 0.4843704 0.014115464 0.6406662
## 3  0.015870984 0.5963138 0.007423525 0.6998592
## 4  0.001418871 0.1556704 0.002635046 0.8926725
## 5  0.006817149 0.4801545 0.002499621 0.8611574
## 6  0.007089878 0.4124127 0.002835951 0.8010471
## 7  0.008094467 0.5103641 0.010633908 0.6574929
## 8  0.019911791 0.6552298 0.004713106 0.4648463
## 9  0.007980050 0.2384040 0.001995012 0.8907731
## 10 0.008403361 0.1976063 0.001018589 0.9106188

correlations

corMatrix=polycor::hetcor(theData)$correlations

previous evaluations

KMO(corMatrix) 

## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = corMatrix)
## Overall MSA =  0.63
## MSA for each item = 
##   pct_ELEC   pct_BGAS pct_CARBON   pct_LENA 
##       0.91       0.59       0.72       0.59

cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05 #false

## [1] FALSE

is.singular.matrix(corMatrix) #false

## [1] FALSE

fa.parallel(theData, fa = 'fa',correct = T,plot = F) # 1 factor

## Parallel analysis suggests that the number of factors =  1  and the number of components =  NA

run factor analysis

resfa <- fa(theData,
            nfactors = 1,
            cor = 'mixed',
            rotate = "varimax", #oblimin?
            fm="minres")

see results

print(resfa$loadings)

## 
## Loadings:
##            MR1   
## pct_ELEC    0.491
## pct_BGAS    0.912
## pct_CARBON  0.280
## pct_LENA   -0.956
## 
##                  MR1
## SS loadings    2.065
## Proportion Var 0.516

fa.diagram(resfa,main = "Resultados del EFA")

run factor analysis

resfa_obli <- fa(theData,
            nfactors = 1,
            cor = 'mixed',
            rotate = "oblimin", #oblimin?
            fm="minres")

see results

print(resfa_obli$loadings) ##los resultados son los mismos 

## 
## Loadings:
##            MR1   
## pct_ELEC    0.491
## pct_BGAS    0.912
## pct_CARBON  0.280
## pct_LENA   -0.956
## 
##                  MR1
## SS loadings    2.065
## Proportion Var 0.516

fa.diagram(resfa_obli,main = "Resultados del EFA")