R Dataset / Package MASS / housing
On this Rdata 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 with the library() command. 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$Sat) house.pm < predict(house.glm1, newData, type = "response")# poisson means house.pm < matrix(house.pm, ncol = 3, byrow = TRUE, dimnames = list(NULL, hnames[[1]])) house.pr < house.pm/drop(house.pm %*% rep(1, 3)) cbind(expand.grid(hnames[1]), round(house.pr, 2))# Iterative proportional scaling loglm(Freq ~ Infl*Type*Cont + Sat*(Infl+Type+Cont), data = housing) # multinomial model library(nnet) (house.mult< multinom(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)) house.mult2 < multinom(Sat ~ Infl*Type*Cont, weights = Freq, data = housing) anova(house.mult, house.mult2)house.pm < predict(house.mult, expand.grid(hnames[1]), type = "probs") cbind(expand.grid(hnames[1]), round(house.pm, 2))# proportional odds model house.cpr < apply(house.pr, 1, cumsum) logit < function(x) log(x/(1x)) house.ld < logit(house.cpr[2, ])  logit(house.cpr[1, ]) (ratio < sort(drop(house.ld))) mean(ratio)(house.plr < polr(Sat ~ Infl + Type + Cont, data = housing, weights = Freq))house.pr1 < predict(house.plr, expand.grid(hnames[1]), type = "probs") cbind(expand.grid(hnames[1]), round(house.pr1, 2))Fr < matrix(housing$Freq, ncol=3, byrow = TRUE) 2*sum(Fr*log(house.pr/house.pr1))house.plr2 < stepAIC(house.plr, ~.^2) house.plr2$anova
Dataset imported from https://www.rproject.org.