## ----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)