# Demo - Housing Data

#### 2023-03-04

This vignette was one of a series of demo() files in the package. It is still there as demo("housing"), but is now presented here with additional commentary and analysis, designed to highlight some aspects of analysis of categorical data and graphical display.

I’ll use the following packages in this vignette.

library(vcdExtra)
library(MASS)
library(effects)

## Housing data

The content here is the dataset MASS::housing, giving a 4-way, $$3 \times 3 \times 4 \times 2$$ frequency table of 1681 individuals from the Copenhagen Housing Conditions Survey, classified by their:

• Satisfaction (Sat) with their housing circumstances (low, medium or high),
• Type of rental dwelling (Tower, Apartment, Atrium or Terrace)
• perceived influence (Infl) on management of the property (low, medium, high), and
• degree of contact (Cont) with other residents (low or high)

data(housing, package="MASS")
str(housing)
## 'data.frame':    72 obs. of  5 variables:
##  $Sat : Ord.factor w/ 3 levels "Low"<"Medium"<..: 1 2 3 1 2 3 1 2 3 1 ... ##$ Infl: Factor w/ 3 levels "Low","Medium",..: 1 1 1 2 2 2 3 3 3 1 ...
##  $Type: Factor w/ 4 levels "Tower","Apartment",..: 1 1 1 1 1 1 1 1 1 2 ... ##$ Cont: Factor w/ 2 levels "Low","High": 1 1 1 1 1 1 1 1 1 1 ...
##  $Freq: int 21 21 28 34 22 36 10 11 36 61 ... ### Variables, levels and models Satisfaction (Sat) of these householders with their present housing circumstances is the outcome variable here. For purposes of analysis, note that Sat is an ordered factor with levels "Low" < "Medium" < "High". Note also that Influence, with the same levels is just a “Factor”, not an ordered one. I consider here just models using glm(..., family=poisson) or the equivalent in MASS::loglm(). The ordering of factor levels is important in graphical displays. We don’t want to see them ordered alphabetically, “High”, “Low”, “Medium”. The housing data.frame was constructed so that the levels of Sat and Infl appear in the dataset in their appropriate order. levels(housing$Sat)
## [1] "Low"    "Medium" "High"
levels(housing\$Infl)
## [1] "Low"    "Medium" "High"

Other models, e.g., the proportional odds model, fit using MASS:polr() can take the ordinal nature of satisfaction into account. In glm() one could re-assign Infl as an ordered factor and examine linear vs. non-linear associations for this factor. But I don’t do this here.

## Null model

The most ignorant model asserts that all the table factors are mutually independent. In symbolic notation, this is [S] [I] [T] [C] where all terms in separate [ ] are supposed to be independent. This is Freq ~ Sat + Infl + Type + Cont as a formula for glm().

house.null <- glm(Freq ~ Sat + Infl + Type + Cont, family = poisson,
data = housing)

## Baseline model

When Sat is the outcome variable, a minimal baseline model should allow for all associations among the predictors, symbolized as [S] [I T C]. That is, Influence, Type and Contact may be associated in arbitrary ways, just as multiple predictors can be correlated in regression models.

In this framework, what remains to be explained is whether/how Sat depends on the combinations of the other variables. The baseline model therefore includes the full three-way term for the predictors.

house.glm0 <- glm(Freq ~ Sat + Infl*Type*Cont, family = poisson,
data = housing)

Both of these models fit terribly, but we can always use anova(mod1, mod2,...) to compare the relative fits of nested models.

anova(house.null, house.glm0, test = "Chisq")
## Analysis of Deviance Table
##
## Model 1: Freq ~ Sat + Infl + Type + Cont
## Model 2: Freq ~ Sat + Infl * Type * Cont
##   Resid. Df Resid. Dev Df Deviance  Pr(>Chi)
## 1        63     295.35
## 2        46     217.46 17   77.896 9.045e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Visualising model fit

The baseline model is shown in the mosaic plot below. Note that this is applied not to the housing data, but rather to the house.glm0 object (of class glm) resulting to a call to vcdExtra::mosaic.glm().

With four variables in the mosaic, labeling of the variable names and factor levels is a bit tricky, because labels must appear on all four sides of the plot. The labeling_args argument can be used to set more informative variable names and abbreviate factor levels where necessary.

# labeling_args for mosaic()
largs <- list(set_varnames = c(
Infl="Influence on management",
Cont="Contact among residents",
Type="Type of dwelling",
Sat="Satisfaction"),
abbreviate=c(Type=3))

mosaic(house.glm0,
labeling_args=largs,
main='Baseline model: [ITC][Sat]')
## Warning: no formula provided, assuming ~Sat + Infl + Type + Cont

In this plot we can see largish positive residuals in the blocks corresponding to (low satisfaction, low influence) and (high satisfaction, high influence) and clusters of largish negative residuals in the opposite corners.

By default, variables are used in the mosaic display in their order in the data table or frequency data.frame. The warning reminds us that the order of conditioning used is ~Sat + Infl + Type + Cont.

### Ordering the variables in the mosaic

For mosaic.glm(), the conditioning order of variables in the mosaic can be set using the formula argument. Here, I rearrange the variables to put Sat as the last variable in the splitting / conditioning sequence. I also use vcdExtra::modFit() to add the LR $$G^2$$ fit statistic to the plot title.

mosaic(house.glm0,
formula = ~ Type + Infl + Cont + Sat,
labeling_args=largs,
main=paste('Baseline model: [ITC][Sat],', modFit(house.glm0))
)

Clearly, satisfaction depends on one or more of the predictors, Infl, Type and Cont and possibly their interactions.

As a first step it is useful to consider sequentially adding the association terms Infl:Sat, Type:Sat, Cont:Sat one at a time. This analysis is carried out using MASS::addterm().

MASS::addterm(house.glm0,
~ . + Sat:(Infl + Type + Cont),
test = "Chisq")
##
## Model:
## Freq ~ Sat + Infl * Type * Cont
##          Df Deviance    AIC     LRT   Pr(Chi)
## <none>        217.46 610.43
## Sat:Infl  4   111.08 512.05 106.371 < 2.2e-16 ***
## Sat:Type  6   156.79 561.76  60.669 3.292e-11 ***
## Sat:Cont  2   212.33 609.30   5.126   0.07708 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Based on this, it is useful to consider a “main-effects” model for satisfaction, adding all three two-way terms involving satisfaction.

The update() method provides an easy way to add (or subtract) terms from a fitted model object. In the model formula, . stands for whatever was on the left side (Freq) or on the right side (Sat + Infl*Type*Cont) of the model (house.glm0) that is being updated.

house.glm1 <- update(house.glm0,
. ~ . + Sat*(Infl + Type + Cont))

For comparison, we note that the same model can be fit using the iterative proportional scaling algorithm of MASS::loglm().

(house.loglm1 <- MASS::loglm(Freq ~ Infl * Type * Cont +
Sat*(Infl + Type + Cont), data = housing))
## Call:
## MASS::loglm(formula = Freq ~ Infl * Type * Cont + Sat * (Infl +
##     Type + Cont), data = housing)
##
## Statistics:
##                       X^2 df  P(> X^2)
## Likelihood Ratio 38.66222 34 0.2671359
## Pearson          38.90831 34 0.2582333

## Did the model get better?

As before, anova() tests the added contribution of each more complex model over the one before. The residual deviance $$G^2$$ has been reduced from $$G^2 (46) = 217.46$$ for the baseline model house.glm0 to $$G^2 (34) = 38.66$$ for the revised model house.glm1. The difference, $$G^2(M1 | M0) = G^2 (12) = 178.79$$ tests the collective additional fit provided by the two-way association of satisfaction with the predictors.

anova(house.glm0, house.glm1, test="Chisq")
## Analysis of Deviance Table
##
## Model 1: Freq ~ Sat + Infl * Type * Cont
## Model 2: Freq ~ Sat + Infl + Type + Cont + Infl:Type + Infl:Cont + Type:Cont +
##     Sat:Infl + Sat:Type + Sat:Cont + Infl:Type:Cont
##   Resid. Df Resid. Dev Df Deviance  Pr(>Chi)
## 1        46    217.456
## 2        34     38.662 12   178.79 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Visualize model glm1

The model house.glm1 fits reasonably well, G^2(34)=38.66, so most residuals are small. In the mosaic below, I use gp=shading_Friendly to shade the tiles so that positive and negative residuals are distinguished by color, and they are filled when the absolute value of the residual is outside $$\pm 2, 4$$.

mosaic(house.glm1,
labeling_args=largs,
main=paste('Model [IS][TS][CS],', modFit(house.glm1) ),
gp=shading_Friendly)

One cell is highlighted here: The combination of medium influence, low contact and tower type, is more likely to give low satisfaction than the model predicts. Is this just an outlier, or is there something that can be interpreted and perhaps improve the model fit? It is hard tell, but the virtues of mosaic displays are that they help to:

• diagnose overall patterns of associations,
• spot unusual cells in relation to lack of fit of a given model.

## Can we drop any terms?

When we add terms using MASS::addterm(), they are added sequentially. It might be the case that once some term is added, a previously added term is no longer important. Running MASS::dropterm() on the housel.glm1 model checks for this.

MASS::dropterm(house.glm1, test = "Chisq")
## Single term deletions
##
## Model:
## Freq ~ Sat + Infl + Type + Cont + Infl:Type + Infl:Cont + Type:Cont +
##     Sat:Infl + Sat:Type + Sat:Cont + Infl:Type:Cont
##                Df Deviance    AIC     LRT   Pr(Chi)
## <none>              38.662 455.63
## Sat:Infl        4  147.780 556.75 109.117 < 2.2e-16 ***
## Sat:Type        6  100.889 505.86  62.227 1.586e-11 ***
## Sat:Cont        2   54.722 467.69  16.060 0.0003256 ***
## Infl:Type:Cont  6   43.952 448.92   5.290 0.5072454
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Note that the three-way term Infl:Type:Cont is not significant. However, with Sat as the response, the associations of all predictors must be included in the model.

The model so far says that each of influence, type and control have separate, additive effects on the level of satisfaction, what I called a “main-effects” model. It might be the case that some of the predictors have interaction effects, e.g., that the effect of influence on satisfaction might vary with the type of dwelling or the level of control.

An easy way to test for these is to update the main-effects model, adding all possible two-way interactions for Sat, one at a time, with addterm().

MASS::addterm(house.glm1,
~. + Sat:(Infl + Type + Cont)^2,
test  =  "Chisq")
##
## Model:
## Freq ~ Sat + Infl + Type + Cont + Infl:Type + Infl:Cont + Type:Cont +
##     Sat:Infl + Sat:Type + Sat:Cont + Infl:Type:Cont
##               Df Deviance    AIC     LRT Pr(Chi)
## <none>             38.662 455.63
## Sat:Infl:Type 12   16.107 457.08 22.5550 0.03175 *
## Sat:Infl:Cont  4   37.472 462.44  1.1901 0.87973
## Sat:Type:Cont  6   28.256 457.23 10.4064 0.10855
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The result shows that adding the term Infl:Type:Sat reduces the deviance $$G^2$$ from 38.66 to 16.11. The difference, $$G^2(M1 + ITS | M1) = G^2 (12) = 22.55$$ reflects a substantial improvement. The remaining two-way interaction terms reduce the deviance by smaller and non-significant amounts, relative to house.glm1.

Model fitting should be guided by substance, not just statistical machinery. Nonetheless, it seems arguably sensible to add one two-way term to the model, giving house.glm2.

house.glm2 <- update(house.glm1,
. ~ . + Sat:Infl:Type)

## Model parsimony: AIC & BIC

Adding more association terms to a model will always improve it. The question is, whether that is “worth it”?

“Worth it” concerns the trade-off between model fit and parsimony. Sometimes we might prefer a model with fewer parameters to one that has a slightly better fit, but requires more model terms and parameters.

The AIC and BIC statistics are designed to adjust our assessment of model fit by penalizing it for using more parameters. Equivalently, they deduct from the likelihood ratio $$G^2$$ a term proportional to the residual $$\text{df}$$ of the model. In any case – smaller is better for both AIC and BIC.

$AIC = G^2 - 2 \: \text{df}$ $BIC = G^2 - \log(n) \: \text{df}$

These measures are provided by AIC(), BIC(), and can be used to compare models using vcdExtra::LRstats().

LRstats(house.glm0, house.glm1, house.glm2)
## Likelihood summary table:
##               AIC    BIC LR Chisq Df Pr(>Chisq)
## house.glm0 610.43 669.62  217.456 46     <2e-16 ***
## house.glm1 455.63 542.15   38.662 34     0.2671
## house.glm2 457.08 570.91   16.107 22     0.8105
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

By these metrics, model house.glm1 is best on both AIC and BIC. The increased goodness-of-fit (smaller $$G^2$$) of model house.glm2 is not worth the extra cost of parameters in the house.glm2 model.