This example illustrates how you can use the GENMOD procedure to fit a model to data measured on an ordinal scale. The following
statements create a SAS data set called Icecream
. The data set contains the results of a hypothetical taste test of three brands of ice cream. The three brands are rated
for taste on a five-point scale from very good (vg) to very bad (vb). An analysis is performed to assess the differences in
the ratings of the three brands. The variable taste
contains the ratings, and the variable brand
contains the brands tested. The variable count
contains the number of testers rating each brand in each category.
The following statements create the Icecream
data set:
data Icecream; input count brand$ taste$; datalines; 70 ice1 vg 71 ice1 g 151 ice1 m 30 ice1 b 46 ice1 vb 20 ice2 vg 36 ice2 g 130 ice2 m 74 ice2 b 70 ice2 vb 50 ice3 vg 55 ice3 g 140 ice3 m 52 ice3 b 50 ice3 vb ;
The following statements fit a cumulative logit model to the ordinal data with the variable taste
as the response and the variable brand
as a covariate. The variable count
is used as a FREQ variable.
proc genmod data=Icecream rorder=data; freq count; class brand; model taste = brand / dist=multinomial link=cumlogit aggregate=brand type1; estimate 'LogOR12' brand 1 -1 / exp; estimate 'LogOR13' brand 1 0 -1 / exp; estimate 'LogOR23' brand 0 1 -1 / exp; run;
The AGGREGATE=BRAND option in the MODEL statement specifies the variable brand
as defining multinomial populations for computing deviances and Pearson chi-squares. The RORDER=DATA option specifies that
the taste
variable levels be ordered by their order of appearance in the input data set—that is, from very good (vg) to very bad (vb).
By default, the response is sorted in increasing ASCII order. Always check the "Response Profiles" table to verify that response
levels are appropriately ordered. The TYPE1 option requests a Type 1 test for the significance of the covariate brand
.
If is the cumulative probability of the jth or lower taste
category, then the odds ratio comparing to is as follows:
See McCullagh and Nelder (1989, Chapter 5) for details on the cumulative logit model. The ESTIMATE statements compute log odds ratios comparing each of
brands. The EXP option in the ESTIMATE statements exponentiates the log odds ratios to form odds ratio estimates. Standard
errors and confidence intervals are also computed. Output 44.4.1 displays general information about the model and data, the levels of the CLASS variable brand
, and the total number of occurrences of the ordered levels of the response variable taste
.
Output 44.4.1: Ordinal Model Information
Output 44.4.2 displays estimates of the intercept terms and covariates and associated statistics. The intercept terms correspond to the
four cumulative logits defined on the taste categories in the order shown in Output 44.4.1. That is, Intercept1
is the intercept for the first cumulative logit, , Intercept2
is the intercept for the second cumulative logit, , and so forth.
Output 44.4.2: Parameter Estimates
Analysis Of Maximum Likelihood Parameter Estimates | ||||||||
---|---|---|---|---|---|---|---|---|
Parameter | DF | Estimate | Standard Error |
Wald 95% Confidence Limits | Wald Chi-Square | Pr > ChiSq | ||
Intercept1 | 1 | -1.8578 | 0.1219 | -2.0967 | -1.6189 | 232.35 | <.0001 | |
Intercept2 | 1 | -0.8646 | 0.1056 | -1.0716 | -0.6576 | 67.02 | <.0001 | |
Intercept3 | 1 | 0.9231 | 0.1060 | 0.7154 | 1.1308 | 75.87 | <.0001 | |
Intercept4 | 1 | 1.8078 | 0.1191 | 1.5743 | 2.0413 | 230.32 | <.0001 | |
brand | ice1 | 1 | 0.3847 | 0.1370 | 0.1162 | 0.6532 | 7.89 | 0.0050 |
brand | ice2 | 1 | -0.6457 | 0.1397 | -0.9196 | -0.3719 | 21.36 | <.0001 |
brand | ice3 | 0 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | . | . |
Scale | 0 | 1.0000 | 0.0000 | 1.0000 | 1.0000 |
Note: | The scale parameter was held fixed. |
The Type 1 test displayed in Output 44.4.3 indicates that Brand
is highly significant; that is, there are significant differences among the brands. The log odds ratios and odds ratios in
the "ESTIMATE Statement Results" table indicate the relative differences among the brands. For example, the odds ratio of
2.8 in the "Exp(LogOR12)" row indicates that the odds of brand 1 being in lower taste categories is 2.8 times the odds of
brand 2 being in lower taste categories. Since, in this ordering, the lower categories represent the more favorable taste
results, this indicates that brand 1 scored significantly better than brand 2. This is also apparent from the data in this
example.
Output 44.4.3: Type 1 Tests and Odds Ratios
Contrast Estimate Results | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Label | Mean Estimate | Mean | L'Beta Estimate | Standard Error |
Alpha | L'Beta | Chi-Square | Pr > ChiSq | ||
Confidence Limits | Confidence Limits | |||||||||
LogOR12 | 0.7370 | 0.6805 | 0.7867 | 1.0305 | 0.1401 | 0.05 | 0.7559 | 1.3050 | 54.11 | <.0001 |
Exp(LogOR12) | 2.8024 | 0.3926 | 0.05 | 2.1295 | 3.6878 | |||||
LogOR13 | 0.5950 | 0.5290 | 0.6577 | 0.3847 | 0.1370 | 0.05 | 0.1162 | 0.6532 | 7.89 | 0.0050 |
Exp(LogOR13) | 1.4692 | 0.2013 | 0.05 | 1.1233 | 1.9217 | |||||
LogOR23 | 0.3439 | 0.2850 | 0.4081 | -0.6457 | 0.1397 | 0.05 | -0.9196 | -0.3719 | 21.36 | <.0001 |
Exp(LogOR23) | 0.5243 | 0.0733 | 0.05 | 0.3987 | 0.6894 |