housing {MASS} | R Documentation |

The `housing`

data frame has 72 rows and 5 variables.

housing

`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.

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.

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

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$df.resid) dropterm(house.glm1, test = "Chisq") addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test = "Chisq") hnames <- lapply(housing[, -5], levels) # omit Freq newData <- expand.grid(hnames) newData$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

[Package *MASS* version 7.2-23 Index]