Basics of R- Session 17- Factor analysis

#factorANalysis
library(psych)

#import data set
factoranalysis<-read.csv("C:/Users/Administrator.vaio1/Desktop/factor1.1.csv")


library(psych)
library(Hmisc)
library(stats)
library(HSAUR)
library(FactoMineR)

#method 1---principal component analysis

#use principal
#default options will be used here

# use library(psych)

#bartlet test of speriocity

library(psych)
cortest.bartlett(factoranalysis)

#KMO test
KMO(factoranalysis)

PCA2<-principal(factoranalysis)
PCA2$rotation
PCA2$values
PCA2$communality
PCA2$factors
PCA2$scores

#change the options in Principal

PCA2<-principal(factoranalysis,nfactors = 6, residuals = FALSE, rotate = "varimax", n.obs = 414,covar = FALSE,scores = TRUE, missing = FALSE, impute = "median", oblique.scores = TRUE, method = "regression" )

PCA2$rotation #"none", "varimax", "quatimax", "promax", #"oblimin", "simplimax", and "cluster" are also

PCA2$values
PCA2$communality
PCA2$factors
PCA2$scores
PCA2$loadings
PCA2$weights
PCA2$rot.mat
PCA2$chi