housing {MASS} | R Documentation

## Frequency Table from a Copenhagen Housing Conditions Survey.

### Description

The `housing` data frame has 72 rows and variables.

### Usage

    data(housing)

### Format

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

### Examples

    data(housing)
    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.resid)

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

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

    hnames <- lapply(housing[, -5], levels) # omit Freq
    house.pm <- predict(house.glm1, expand.grid(hnames),
                       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.mult
    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, ])
    sort(drop(house.ld))
    mean(.Last.value)

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

    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