#コレスポンデンス分析
y<-matrix(c(62,12,91,23,15,8,21,61,81,5,89,4,
            38,27,47,15,18,13,17,39,31,8,55,44,
            26,24,22,11,43,41,5,22,27,9,37,85,
            3,2,6,1,2,0,0,2,1,0,2,152),ncol=12,byrow=TRUE)
colnames(y)<-c("信頼","個性的","庶民的","性能悪い","デザイン","センス","品質","シンプル","品揃え","飽きない","安い","知らない")
rownames(y)<-c("ダイソ-","キャンドゥ","セリア","ワッツ")
sum(y)
margin.table(y,margin=1)
margin.table(y,margin=2)

prop.table(y, margin=1)
100*prop.table(y, margin=1)
round(100*prop.table(y, margin=1),1)　

library(MASS)
y.ca <- corresp(y, nf=2)
summary(y.ca)
y.ca$Freq
y.ca$cor
y.eig <- y.ca$cor^2
round(100*y.eig / sum(y.eig),2)
y.ca$cscore　
y.ca$rscore　
biplot(y.ca)


#店舗属性 探索的因子分析・確認的因子分析・SEM
x<-read.delim("clipboard")
result <- factanal(x, factors=3, rotation="promax", scores="regression")
model.cfa <- '
haiti =~ haiti3
kougaku=~kougaku1+kougaku2
kinnitu=~kinnitu2+kinnitu3
sinazoroe=~sinazoroe1+sinazoroe2'
fit<-cfa(model.cfa,data=x)
summary(fit,standardized=T,fit.measures=T)
modindices(fit,sort=T)  

model.sem <- '
haiti =~ haiti3
kougaku=~kougaku1+kougaku2
kinnitu=~kinnitu2+kinnitu3
sinazoroe=~sinazoroe1+sinazoroe2
riyouhindo~haiti+kougaku+kinnitu+sinazoroe+can.do+daiso+seria'
fit <-sem(model.sem, data=x, estimator="ML",meanstructure=TRUE,int.ov.free=T,auto.var=TRUE, auto.fix.first=TRUE,auto.cov.lv.x=TRUE)
summary(object=fit)
summary(object=fit, fit.measure=TRUE)
standardizedSolution(object=fit)
predict(object=fit)


#消費者特性 探索的因子分析・確認的因子分析・SEM
x<-read.delim("clipboard")
result <- factanal(x, factors=6, rotation="promax", scores="regression")

model.cfa <- '
sinnkisei=~sinnkisei1+sinnkisei2+sinnkisei3
kasyobunn=~kasyobunn1+kasyobunn2
opinionn=~opinionn1+opinionn2
hinnsitu=~hinnsitu2+hinnsitu3
doutyou=~doutyou1+doutyou2+doutyou3
seihinnkannyo=~seihinnkannyo1+seihinnkannyo2
osyare=~osyare1+osyare2+osyare3'
fit<-cfa(model.cfa,data=x)
summary(fit,standardized=T,fit.measures=T)
modindices(fit,sort=T)  

model.sem <- '
sinnkisei=~sinnkisei1+sinnkisei2+sinnkisei3
kasyobunn=~kasyobunn1+kasyobunn2
opinionn=~opinionn1+opinionn2
hinnsitu=~hinnsitu2+hinnsitu3
doutyou=~doutyou1+doutyou2+doutyou3
seihinnkannyo=~seihinnkannyo1+seihinnkannyo2
osyare=~osyare1+osyare2+osyare3
riyouhindo~sinnkisei+kasyobunn+opinionn+hinnsitu+doutyou+seihinnkannyo+osyare+seibetu'
fit <-sem(model.sem, data=x, estimator="ML",meanstructure=TRUE,int.ov.free=T,auto.var=TRUE, auto.fix.first=TRUE,auto.cov.lv.x=TRUE)
summary(object=fit)
summary(object=fit, fit.measure=TRUE)
standardizedSolution(object=fit)
predict(object=fit)


#店舗属性重回帰分析
x0<-read.delim("clipboard")
x<-read.delim("clipboard")
x1<-factanal(x, factors = 3, rotation = "promax",scores="regression")#
x1$scores
x2<-data.frame(x0,x1$scores)#x0(元データ)とx1を合成
summary(lm(riyouhindo~  Factor1+Factor2+Factor3,x2))

#消費者特性重回帰分析
y0<-read.delim("clipboard")
y<-read.delim("clipboard")
y1<-factanal(y, factors = 6, rotation = "promax",scores="regression")
y1$scores
y2<-data.frame(y0,y1$scores)
edit(y2)
summary(lm(riyouhindo~  Factor1+Factor2+Factor3+Factor4+Factor5+Factor6+seibetu,y2))