## ----echo=FALSE,eval=FALSE----------------------------------------------------
# options(width=85)

## ----eval=FALSE---------------------------------------------------------------
# unemployment <- matrix(c(97, 216, 56, 34, 105, 91, 31, 11,
#                   45, 81, 32, 9, 51, 81, 34, 9), nrow=8, ncol=2)
# rownames(unemployment) <- c(paste("male", 1:4), paste("female", 1:4))
# colnames(unemployment) <- c("Short term","Long term")
# unemployment

## ----eval=FALSE---------------------------------------------------------------
# y <- c(rep(1, sum(97, 216, 56, 34, 105, 91, 31, 11)),
#        rep(0, sum(45, 81, 32, 9, 51, 81, 34, 9)))
# 
# G <- c(rep(1, sum(97, 216, 56, 34)), rep(0, sum(105, 91, 31, 11)),
#        rep(1, sum(45, 81, 32, 9)), rep(0, sum(51, 81, 34, 9)))
# 
# L <- factor(c(rep(1, 97), rep(2, 216), rep(3, 56), rep(4, 34),
#        rep(1, 105), rep(2, 91), rep(3, 31), rep(4, 11),
#        rep(1, 45), rep(2, 81), rep(3, 32), rep(4, 9),
#        rep(1, 51), rep(2, 81), rep(3, 34), rep(4, 9)))
# 
# table(G,L,y)

## ----eval=FALSE---------------------------------------------------------------
# unemp_1 <- glm(y ~ 1,family=binomial)
# unemp_G <- glm(y ~ G,family=binomial)
# unemp_L <- glm(y ~ L,family=binomial)
# unemp_LG <- glm(y ~ G + L,family=binomial)
# unemp_sat <- glm(y ~ G * L,family=binomial)
# summary(unemp_sat)

## ----eval=FALSE---------------------------------------------------------------
# anova(unemp_LG, unemp_sat)
# anova(unemp_L, unemp_LG)
# anova(unemp_1, unemp_L)
# 
# anova(unemp_LG, unemp_sat)
# anova(unemp_G, unemp_LG)
# anova(unemp_1, unemp_G)

## ----eval=FALSE---------------------------------------------------------------
# anova(unemp_1, unemp_sat)
# anova(unemp_L, unemp_sat)
# anova(unemp_G, unemp_sat)
# anova(unemp_LG, unemp_sat)

## ----eval=FALSE---------------------------------------------------------------
# genderleveldat<-data.frame("Long term"=unemployment[,1],
# "Short term"=unemployment[,2],"Level"=rep(1:4,2),"Gender"=rep(c(1,0),each=4))
# 
# groupintercept<-glm(cbind(Long.term, Short.term) ~ 1, family=binomial,
#                     data=genderleveldat)
# summary(groupintercept)
# 
# #Corresponding un-grouped model:
# summary(unemp_1)
# 
# groupgender<-glm(cbind(Long.term, Short.term) ~ Gender, family=binomial,
#                  data=genderleveldat)
# summary(groupgender)
# 
# #Corresponding un-grouped model:
# summary(unemp_G)
# 
# 
# grouplevel<-glm(cbind(Long.term, Short.term) ~ as.factor(Level), family=binomial,
#                 data=genderleveldat)
# summary(grouplevel)
# 
# #Corresponding un-grouped model:
# summary(unemp_L)
# 
# 
# groupgenderlevel<-glm(cbind(Long.term, Short.term) ~ as.factor(Gender) +
#   as.factor(Level), family=binomial, data=genderleveldat)
# summary(groupgenderlevel)
# 
# #Corresponding un-grouped model:
# summary(unemp_LG)
# 
# groupsat<-glm(cbind(Long.term, Short.term) ~ as.factor(Gender) * as.factor(Level),
#               family=binomial, data=genderleveldat)
# summary(groupsat)
# 
# #Corresponding un-grouped model:
# summary(unemp_sat)

## ----eval=FALSE---------------------------------------------------------------
# anova(groupgenderlevel, groupsat)
# anova(grouplevel, groupgenderlevel)
# anova(groupintercept, grouplevel)
# 
# 
# anova(groupgenderlevel, groupsat)
# anova(groupgender, groupgenderlevel)
# anova(groupintercept, groupgender)

## ----echo=FALSE,results='hide',eval=FALSE-------------------------------------
# rm(unemployment)