# Afc.r

library(descr)
library(ca)

#############################################################
# Exemple AFC: Nombre d'années d'éducation et type de travail

load("TravEduc.rda")
crosstab(TravEduc$Education,TravEduc$Travail,plot=FALSE)

T <- xtabs(~TravEduc$Education+TravEduc$Travail)
TE_AFC <- ca(T)
summary(TE_AFC)

plot(TE_AFC,mass=c("TRUE","TRUE"))



####################################
# Exemple AFC: Ordination des femmes
load("Ordination.rda")
crosstab(Ordination$Votants,Ordination$Vote,plot=FALSE)

O <- xtabs(~Ordination$Votants+Ordination$Vote)
O_AFC <- ca(O)
summary(O_AFC)

plot(O_AFC,mass=c("TRUE","TRUE"))



####################
# Exemple AFCm: GPEM

load("GPEM.rda")
summary(GPEM)

# Normalisation: Burt
GPEM_AFCm <- mjca(GPEM,ps=": ",lambda="Burt")
summary(GPEM_AFCm)

plot(GPEM_AFCm,what=c("none","all"),mass=c("TRUE","TRUE"))

# Normalisation: adjusted
GPEM_AFCm2 <- mjca(GPEM,ps=": ")
summary(GPEM_AFCm2)

plot(GPEM_AFCm2,what=c("none","all"),mass=c("TRUE","TRUE"))

# Plot des modalités et des observations avec Burt
plot(GPEM_AFCm,what=c("all","all"),mass=c("TRUE","TRUE"))


#################################################
# Exemple AFCm: Revenu, opinion politique, région

load("RevOpReg.rda")
summary(RevOpReg)

# Normalisation: Burt
ROR_AFCm <- mjca(RevOpReg,ps=": ",lambda="Burt")
summary(ROR_AFCm)

plot(ROR_AFCm,what=c("none","all"),mass=c("TRUE","TRUE"))