data(NKPS)
attach(NKPS)
#######################################################################################
# Chapter 2
# Table 2.8
Table.2.8 <- array(NA,c(4,4,4)); k <- 0
for (i in levels(P)) for (j in levels(C)){
k <- k+1
Table.2.8[,,k] <- addmargins(t(table(NKPS[,c(3,4)] [C==j & P==i,])))
}
dimnames(Table.2.8) <- list(c(levels(PS),"Total"),c(levels(CS),"Total"),
c("Father-Son","Father-Daughter","Mother-Son","Mother-Daughter"))
Table.2.8
# Table 2.9
Table.2.9 <- cbind(table(PS),table(CS),table(c(CS[C=="Son"],PS[P=="Father"])),
table(c(CS[C=="Daughter"],PS[P=="Mother"])))
dimnames(Table.2.9)[[2]] <- c("Parent","Child","Men","Women")
addmargins(Table.2.9)[,-5]
# Table 2.10
# Table 2.11
########################################################
# Data
recAB = NKPS[,c(3,4)]
recPCAB = NKPS[,c(1,2,3,4)]
recA1A2B1B2 = NKPS[,c(3,4,5,6)]
# list of frequencies in table AB
nAB = c(t(ftable(recAB)))
########################################################
# table AB and GT
# at produces marginal distributions of A and B, or the 2x3 table GT;
# G = generation and T = attititude
at <- MarginalMatrix(c("A", "B"), list(c("A"), c("B")), c(3, 3));
bt <- ConstraintMatrix(c("G", "T"), list(c("G"), c("T")), c(2, 3));
model1 <- list(bt, "log", at);
nkps1 <- MarginalModelFit(dat = recAB, model = model1, ShowParameters = TRUE,
Labels = list(list("G", c("men", "women")), "T"),
CoefficientDimensions = c(2, 3), ShowProgress = 10)
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