#
# Epage 142
#
# Written by:
# -- 
# John L. Weatherwax                2009-04-21
# 
# email: wax@alum.mit.edu
# 
# Please send comments and especially bug reports to the
# above email address.
# 
#-----

# version$language == "R" for R version$language == NULL for SPlus

if(is.null(version$language) == FALSE){ 
  require(alr3)
}else{
  library(alr3)
}

if(is.null(version$language) == FALSE) data(salary)

attach(salary)

# a scatterplot matrix
#
postscript("../../WriteUp/Graphics/Chapter6/prob_13.eps", onefile=FALSE, horizontal=FALSE)
pairs(Salary~YSdeg+Year)
dev.off()

SF <- factor( salary$Sex, ordered=FALSE )

m0 <- lm( Salary ~ +1      ) # the common intercept model (same mean for each sex)
m1 <- lm( Salary ~ -1 + SF ) # the different intercept model (different means for each sex)

# compare the two models:
anova(m0,m1)

# form the largest of the set of models:
HDF <- factor( salary$Degree, ordered=FALSE )
RF  <- factor( salary$Rank, ordered=FALSE ) 

m0 <- lm( Salary ~ Year + HDF + YSdeg )      # the less general model 
m1 <- lm( Salary ~ Year + HDF + YSdeg + RF ) # the more general model 

# compare the two models
anova(m0,m1)

# compare an ever more general model that uses Sex as a factor:
m2 <- lm( Salary ~ Year + HDF + YSdeg + RF + SF ) # the most general model 

# compare these two models:
anova(m1,m2)