8.2.1 - Example: Housing Satisfaction in SAS

Example: Housing Data (using SAS) Section

apartment building

In this section, we will fit the baseline-category logit model to the data below via PROC LOGISTIC with LINK=GLOGIT in SAS. The model can be also fitted by using PROC GENMOD (see the reference link provided at the top page of this lesson).

The data in "housing.dat" gives information on housing conditions from a survey in Copenhagen. Variables are satisfaction (low, medium, high), perceived influence on management (low, medium, high), type of housing (tower, atrium, apartment, terrace), contact with other residents (low, high), and the frequency count of individuals in that row.

When the data are grouped, as they are in this example, SAS expects the response categories \(1, 2, \ldots , r\) to appear in a single column of the dataset, with another column containing the frequency or count as they are in this case. The lines that have a frequency of zero are not actually used in the modeling, because they contribute nothing to the log-likelihood; as such, we can omit them if we wish.

Let's describe these data by a baseline-category model, with Satisfaction as the outcome (baseline chosen as "medium") and other variables are predictors. Note that with this model, we treat the satisfaction levels as nominal; we'll revisit this data set with satisfaction treated as ordinal in the next section.

We let \(\pi_1\) = probability of low satisfaction, \(\pi_2\) = probability of high satisfaction, and \(\pi_3\) = probability of medium satisfaction so that the equations are

\(\log\left(\dfrac{\pi_j}{\pi_3}\right)=\beta_{0j}+\beta_{1j}x_1+\beta_{2j}x_2+\cdots\)

for \(j = 1,2\). The \(x\)'s represent the predictor terms, including any interactions we wish to fit as well. Note that these predictor terms do not depend on \(j\). Also, treating the predictors as nominal requires

  • two indicators for perceived influence on management
  • three indicators for type of housing
  • one indicator for contact with other residents

With first-order terms and all two-way interactions, this gives us a total of \((3-1)(1+6+11)=36\) parameters (1 intercept, 6 first-order terms, and 11 interaction terms for each of the two non-baseline satisfaction response categories)!


 Specifying the Model

Note! In the model statement, we need to tell SAS about the existence of a count or frequency variable; otherwise, SAS will assume that the data are ungrouped, with each line representing a single observation. We also need to specify which of the categories is the baseline, e.g., model sat (ref="Medium") sets medium satisfaction as the baseline. The link function is glogit, for generalized logit.

To get fit statistics, we include the options aggregate and scale=none. To adjust for overdispersion, we would use scale=pearson.

data housing;
input Sat $ Infl $ Type $ Cont $ Freq;
cards;
     Low    Low     Tower  Low   21
  Medium    Low     Tower  Low   21
    High    Low     Tower  Low   28
     Low Medium     Tower  Low   34
  Medium Medium     Tower  Low   22
    High Medium     Tower  Low   36
     Low   High     Tower  Low   10
  Medium   High     Tower  Low   11
    High   High     Tower  Low   36
    Low    Low Apartment  Low   61
 Medium    Low Apartment  Low   23
   High    Low Apartment  Low   17
    Low Medium Apartment  Low   43
 Medium Medium Apartment  Low   35
   High Medium Apartment  Low   40
    Low   High Apartment  Low   26
 Medium   High Apartment  Low   18
   High   High Apartment  Low   54
    Low    Low    Atrium  Low   13
 Medium    Low    Atrium  Low    9
   High    Low    Atrium  Low   10
    Low Medium    Atrium  Low    8
 Medium Medium    Atrium  Low    8
   High Medium    Atrium  Low   12
    Low   High    Atrium  Low    6
 Medium   High    Atrium  Low    7
   High   High    Atrium  Low    9
    Low    Low   Terrace  Low   18
 Medium    Low   Terrace  Low    6
   High    Low   Terrace  Low    7
    Low Medium   Terrace  Low   15
 Medium Medium   Terrace  Low   13
   High Medium   Terrace  Low   13
    Low   High   Terrace  Low    7
 Medium   High   Terrace  Low    5
   High   High   Terrace  Low   11
    Low    Low     Tower High   14
 Medium    Low     Tower High   19
   High    Low     Tower High   37
    Low Medium     Tower High   17
 Medium Medium     Tower High   23
   High Medium     Tower High   40
    Low   High     Tower High    3
 Medium   High     Tower High    5
   High   High     Tower High   23
    Low    Low Apartment High   78
 Medium    Low Apartment High   46
   High    Low Apartment High   43
    Low Medium Apartment High   48
 Medium Medium Apartment High   45
   High Medium Apartment High   86
    Low   High Apartment High   15
 Medium   High Apartment High   25
   High   High Apartment High   62
    Low    Low    Atrium High   20
 Medium    Low    Atrium High   23
   High    Low    Atrium High   20
    Low Medium    Atrium High   10
 Medium Medium    Atrium High   22
   High Medium    Atrium High   24
    Low   High    Atrium High    7
 Medium   High    Atrium High   10
   High   High    Atrium High   21
    Low    Low   Terrace High   57
 Medium    Low   Terrace High   23
   High    Low   Terrace High   13
    Low Medium   Terrace High   31
 Medium Medium   Terrace High   21
   High Medium   Terrace High   13
    Low   High   Terrace High    5
 Medium   High   Terrace High    6
   High   High   Terrace High   13
; run;

* two-way interactions;
proc logistic data=housing;
freq freq;
class infl (ref="Low") type cont / param=ref;
model sat (ref="Medium") =  infl type cont 
 infl*type infl*cont type*cont /link=glogit 
 aggregate=(infl type cont) scale=none;
run;

* additive model;
proc logistic data=housing;
freq freq;
class infl (ref="Low") type cont / param=ref;
model sat (ref="Medium") =  infl type cont /link=glogit 
 aggregate=(infl type cont) scale=none;
run;

* type and cont only;
proc logistic data=housing;
freq freq;
class type cont / param=ref;
model sat (ref="Medium") = type cont /link=glogit 
 aggregate=(infl type cont) scale=none;
run;

* infl and cont only;
proc logistic data=housing;
freq freq;
class infl (ref="Low") cont / param=ref;
model sat (ref="Medium") = infl cont /link=glogit 
 aggregate=(infl type cont) scale=none;
run;

* infl and type only;
proc logistic data=housing;
freq freq;
class infl (ref="Low") type / param=ref;
model sat (ref="Medium") = infl type /link=glogit 
 aggregate=(infl type cont) scale=none;
run;

* intercept only;
proc logistic data=housing;
freq freq;
class / param=ref;
model sat (ref="Medium") = /link=glogit 
 aggregate=(infl type cont) scale=none;
run;

Output

Here is the output pertaining to the "response profile" that gives us three levels of the satisfaction response and clarifying the baseline as "Medium". For example, there are 668 individuals who responded with "High" satisfaction.

 
Response Profile
Ordered
Value
Sat Total
Frequency
1 High 668
2 Low 567
3 Medium 446

Here is the output pertaining to the goodness of fit.

 
Deviance and Pearson Goodness-of-Fit Statistics
Criterion Value DF Value/DF Pr > ChiSq
Deviance 5.9443 12 0.4954 0.9189
Pearson 5.9670 12 0.4973 0.9177

Number of unique profiles: 24

There are \(N = 24\) profiles (unique combinations of the predictors) in this dataset. Recall that this part of the output tests the fit of the current model versus the saturated model. The saturated model, which fits a separate multinomial distribution to each profile, has \(24\cdot2 = 48\) parameters. Compared with the current model, which has 36 parameters to include up to two-way interactions only, this gives 12 degrees of freedom for the test.

 Stop and Think!
Does this model fit well?

The model fits well because both the residual deviance and Pearson test statistics are small, relative to their chi-square distributions; both p-values are large and indicate very little evidence of lack of fit.

Output pertaining to the significance of the predictors:

 
Joint Tests
Effect DF Wald
Chi-Square
Pr > ChiSq
Infl 4 13.2983 0.0099
Type 6 25.3474 0.0003
Cont 2 5.7270 0.0571
Infl*Type 12 20.8975 0.0519
Infl*Cont 4 0.8772 0.9278
Type*Cont 6 9.4081 0.1519

Note:Under full-rank parameterizations, Type 3 effect tests are replaced by joint tests. The joint test for an effect is a test that all the parameters associated with that effect are zero. Such joint tests might not be equivalent to Type 3 effect tests under GLM parameterization.

The first section (global null hypothesis) tests the fit of the current model against the null or intercept-only model. The null model has two parameters (one-intercept for each non-baseline equation). Therefore, the comparison has \(36 - 2 = 34\) degrees of freedom. This test is highly significant, indicating that at least one of the predictors has an effect on satisfaction.

The next section (Type III analysis of effects) shows the change in fit resulting from discarding any one of the predictors—influence, type, or contact—while keeping the others in the model. For example, the test for influence is equivalent to setting its two indicator coefficients equal to zero in each of the two logit equations; so the test for significance of influence has \(2\cdot2=4\) degrees of freedom. Judging from these tests, we see that

  • Perceived influence on management depends on the type of housing to a degree (interaction), but it's borderline insignificant, given that other terms are already in the model. Other interaction terms are relatively weak.
  • Both influence and housing type are strongly related to satisfaction, while contact is borderline insignificant.

This suggests that we may get a more efficient model by removing one or more interaction terms. Below are the goodness of fit statistics for the additive model (no interaction terms). This seems to fit reasonably well and has considerably fewer parameters: \((3-1)(1+6)=14\). We'll retain this additive model and interpret the parameter estimates next.

 Interpreting Parameter Estimates

Let's look at the estimated coefficients of the current model that contains only the main effects:

 
Analysis of Maximum Likelihood Estimates
Parameter   Sat DF Estimate Standard
Error
Wald
Chi-Square
Pr > ChiSq
Intercept   High 1 0.2805 0.1662 2.8473 0.0915
Intercept   Low 1 0.4192 0.1729 5.8768 0.0153
Infl High High 1 0.9477 0.1681 31.8016 <.0001
Infl High Low 1 -0.6649 0.1863 12.7338 0.0004
Infl Medium High 1 0.2885 0.1448 3.9704 0.0463
Infl Medium Low 1 -0.4464 0.1416 9.9443 0.0016
Type Apartmen High 1 -0.2999 0.1563 3.6834 0.0550
Type Apartmen Low 1 0.4357 0.1725 6.3769 0.0116
Type Atrium High 1 -0.5393 0.1996 7.3033 0.0069
Type Atrium Low 1 -0.1314 0.2231 0.3467 0.5560
Type Terrace High 1 -0.7458 0.2105 12.5494 0.0004
Type Terrace Low 1 0.6666 0.2063 10.4445 0.0012
Cont High High 1 0.1210 0.1293 0.8752 0.3495
Cont High Low 1 -0.3609 0.1324 7.4284 0.0064

The SAS System

The LOGISTIC Procedure

 
Model Information
Data Set WORK.HOUSING
Response Variable Sat
Number of Response Levels 3
Frequency Variable Freq
Model generalized logit
Optimization Technique Newton-Raphson
 
Odds Ratio Estimates
Effect Sat Point Estimate 95% Wald
Confidence Limits
Infl High vs Low High 2.580 1.856 3.586
Infl High vs Low Low 0.514 0.357 0.741
Infl Medium vs Low High 1.334 1.005 1.772
Infl Medium vs Low Low 0.640 0.485 0.845
Type Apartmen vs Tower High 0.741 0.545 1.006
Type Apartmen vs Tower Low 1.546 1.102 2.168
Type Atrium vs Tower High 0.583 0.394 0.862
Type Atrium vs Tower Low 0.877 0.566 1.358
Type Terrace vs Tower High 0.474 0.314 0.717
Type Terrace vs Tower Low 1.948 1.300 2.918
Cont High vs Low High 1.129 0.876 1.454
Cont High vs Low Low 0.697 0.538 0.904

How do we interpret them? Recall that there are two logit equations involved: 1) the log-odds of low satisfaction versus medium and 2) the log-odds of high satisfaction versus medium. This baseline reference is in addition to any baselines involved in the predictors. For the two indicators used for perceived influence, the baseline corresponds to "low". So, from the output, the estimated coefficient for high influence is interpreted as follows: for those with high perceived influence on management, the odds of high satisfaction (versus medium) is estimated to be \(e^{.9477}=2.58\) times as high as that for those with low influence. As with other glms we've dealt with to this point, we may change the baselines arbitrarily, which changes the interpretations of the numerical values reported but not the significance of the predictor (all levels together) or the fit of the model as a whole.

The intercepts give the estimated log-odds for the baseline groups. For example, the estimated log-odds of high satisfaction (versus medium) is \(e^{.2805}=1.32\) for birds versus fish in this group is −2.4633 for those with low perceived influence, housing type tower, and low contact with other residents.

Although the above output shows all three predictor main effects are significantly related to satisfaction, we may consider the same model selection techniques here as we used earlier for binary logistic regression.

Model Selection

In the main effects model, the one we fitted above, the Wald statistics reported indicate that each of the three predictors is highly significant, given the others. We can also address this by comparing deviances, full model versus reduced model; we just have to be careful of the number of parameters since each effect we test applies to two (in general, \(r-1\)) logit models. The current (additive) model has deviance \(G^2=38.6622\) with 34 degrees of freedom versus the saturated model as we noted above. If we remove the influence indicators, the deviance increases to \(G^2=147.7797\) with 38 degrees of freedom, and the LRT statistic for comparing these two models would be

\(147.7797 - 38.6622=109.1175\)

which can be compared against a chi-square distribution with \(38-34=4\) degrees of freedom (p-value approximately 0). So, the effect of influence is considered (highly) significant. Similarly, we can compare any two models in this way, provided one is a special case of the other. Repeating the model-fitting for various sets of predictors, we obtain the following analysis-of-deviance table:

Model \(G^2\) df
Saturated 0.00 0
Infl + Type + Cont 38.6622 34
Type + Cont 147.7797 38
Infl + Cont 100.8891 40
Infl + Type 54.7219 36
... ... ...
Null 217.4560 46

We can refine the model by removing terms if doing so does not significantly reduce the fit. 

To compare models that are not of the form full versus reduced, we can use an information criterion, such as AIC. For example, to compare the models with only Type and Cont against the model with only Infl and Cont, the AIC values would be 3599.201 and 3548.311, respectively, in favor of the latter (lower AIC).

Finally, an adjustment for overdispersion could also be implemented as it was with binary logistic regression. The assumption is that the multinomial variance assumed in the model is off by a scale parameter, which we can estimate from the Pearson goodness of fit test. For the additive model, this would be

\(\dfrac{X^2}{df}=\dfrac{38.9104}{34}=1.144\)

Since this is so close to 1, we would not opt to include such an adjustment in this case, but if we did, the effect would be to multiply all variance and standard error estimates by \(1.144\) and \(\sqrt{1.144}\), respectively.