housing                 package:MASS                 R Documentation

Frequency Table from a Copenhagen Housing Conditions Survey

Description:

     The 'housing' data frame has 72 rows and 5 variables.

Usage:

     housing

Format:


     'Sat' Satisfaction of householders with their present housing
          circumstances, (High, Medium or Low, ordered factor).

     'Infl' Perceived degree of influence householders have on the
          management of the property (High, Medium, Low).

     'Type' Type of rental accommodation, (Tower, Atrium, Apartment,
          Terrace).

     'Cont' Contact residents are afforded with other residents, (Low,
          High).

     'Freq' Frequencies: the numbers of residents in each class.


Source:

     Madsen, M. (1976) Statistical analysis of multiple contingency
     tables. Two examples. _Scand. J. Statist._ *3*, 97-106.

     Cox, D. R. and Snell, E. J. (1984) _Applied Statistics, Principles
     and Examples_. Chapman & Hall.

References:

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

Examples:

     options(contrasts = c("contr.treatment", "contr.poly"))

     # Surrogate Poisson models
     house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family = poisson,
                       data = housing)
     summary(house.glm0, cor = FALSE)

     addterm(house.glm0, ~. + Sat:(Infl+Type+Cont), test = "Chisq")

     house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont))
     summary(house.glm1, cor = FALSE)

     1 - pchisq(deviance(house.glm1), house.glm1$df.residual)

     dropterm(house.glm1, test = "Chisq")

     addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test  =  "Chisq")

     hnames <- lapply(housing[, -5], levels) # omit Freq
     newData <- expand.grid(hnames)
     newData$Sat <- ordered(newData$Sat)
     house.pm <- predict(house.glm1, newData,
                         type = "response")  # poisson means
     house.pm <- matrix(house.pm, ncol = 3, byrow = TRUE,
                        dimnames = list(NULL, hnames[[1]]))
     house.pr <- house.pm/drop(house.pm %*% rep(1, 3))
     cbind(expand.grid(hnames[-1]), round(house.pr, 2))

     # Iterative proportional scaling
     loglm(Freq ~ Infl*Type*Cont + Sat*(Infl+Type+Cont), data = housing)

     # multinomial model
     library(nnet)
     (house.mult<- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
                            data = housing))
     house.mult2 <- multinom(Sat ~ Infl*Type*Cont, weights = Freq,
                             data = housing)
     anova(house.mult, house.mult2)

     house.pm <- predict(house.mult, expand.grid(hnames[-1]),
                         type = "probs")
     cbind(expand.grid(hnames[-1]), round(house.pm, 2))

     # proportional odds model
     house.cpr <- apply(house.pr, 1, cumsum)
     logit <- function(x) log(x/(1-x))
     house.ld <- logit(house.cpr[2, ]) - logit(house.cpr[1, ])
     (ratio <- sort(drop(house.ld)))
     mean(ratio)

     (house.plr <- polr(Sat ~ Infl + Type + Cont,
                        data = housing, weights = Freq))

     house.pr1 <- predict(house.plr, expand.grid(hnames[-1]),
                        type = "probs")
     cbind(expand.grid(hnames[-1]), round(house.pr1, 2))

     Fr <- matrix(housing$Freq, ncol  =  3, byrow = TRUE)
     2*sum(Fr*log(house.pr/house.pr1))

     house.plr2 <- stepAIC(house.plr, ~.^2)
     house.plr2$anova

