On this R-data statistics page, you will find information about the housing data set which pertains to Frequency Table from a Copenhagen Housing Conditions Survey. The housing data set is found in the MASS R package. You can load the housing data set in R by issuing the following command at the console data("housing"). This will load the data into a variable called housing. If R says the housing data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the housing R data set. The size of this file is about 14,953 bytes.

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

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Dataset imported from https://www.r-project.org.