EVOLUTION-MANAGER

Edit File: housing.html

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<table width="100%" summary="page for housing {MASS}"><tr><td>housing {MASS}</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>
Frequency Table from a Copenhagen Housing Conditions Survey
</h2>

<h3>Description</h3>

<p>The <code>housing</code> data frame has 72 rows and 5 variables.
</p>

<h3>Usage</h3>

<pre>
housing
</pre>

<h3>Format</h3>

<dl>
<dt><code>Sat</code></dt><dd>
<p>Satisfaction of householders with their present housing
circumstances, (High, Medium or Low, ordered factor).
</p>
</dd>
<dt><code>Infl</code></dt><dd>
<p>Perceived degree of influence householders have on the
management of the property (High, Medium, Low).
</p>
</dd>
<dt><code>Type</code></dt><dd>
<p>Type of rental accommodation, (Tower, Atrium, Apartment, Terrace).
</p>
</dd>
<dt><code>Cont</code></dt><dd>
<p>Contact residents are afforded with other residents, (Low, High).
</p>
</dd>
<dt><code>Freq</code></dt><dd>
<p>Frequencies: the numbers of residents in each class.
</p>
</dd>
</dl>

<h3>Source</h3>

<p>Madsen, M. (1976)
Statistical analysis of multiple contingency tables. Two examples.
<em>Scand. J. Statist.</em> <b>3</b>, 97&ndash;106.
</p>
<p>Cox, D. R. and Snell, E. J. (1984)
<em>Applied Statistics, Principles and Examples</em>.
Chapman &amp; Hall.
</p>

<h3>References</h3>

<p>Venables, W. N. and Ripley, B. D. (2002)
<em>Modern Applied Statistics with S.</em> Fourth edition.  Springer.
</p>

<h3>Examples</h3>

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

# Surrogate Poisson models
house.glm0 &lt;- 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 &lt;- 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 &lt;- lapply(housing[, -5], levels) # omit Freq
newData &lt;- expand.grid(hnames)
newData$Sat &lt;- ordered(newData$Sat)
house.pm &lt;- predict(house.glm1, newData,
                    type = "response")  # poisson means
house.pm &lt;- matrix(house.pm, ncol = 3, byrow = TRUE,
                   dimnames = list(NULL, hnames[[1]]))
house.pr &lt;- 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&lt;- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
                       data = housing))
house.mult2 &lt;- multinom(Sat ~ Infl*Type*Cont, weights = Freq,
                        data = housing)
anova(house.mult, house.mult2)

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

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

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

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

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

house.plr2 &lt;- stepAIC(house.plr, ~.^2)
house.plr2$anova
</pre>

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