The HPLOGISTIC Procedure

Example 55.3 Ordinal Logistic Regression

(View the complete code for this example.)

Consider a study of the effects of various cheese additives on taste. Researchers tested four cheese additives and obtained 52 response ratings for each additive. Each response was measured on a scale of nine categories ranging from strong dislike (1) to excellent taste (9). The data, given in McCullagh and Nelder (1989, p. 175) in the form of a two-way frequency table of additive by rating, are saved in the data set Cheese by using the following program. The variable y contains the response rating. The variable Additive specifies the cheese additive (1, 2, 3, or 4). The variable freq gives the frequency with which each additive received each rating.

data Cheese;
   do Additive = 1 to 4;
      do y = 1 to 9;
         input freq @@;
         output;
      end;
   end;
   label y='Taste Rating';
   datalines;
0  0  1  7  8  8 19  8  1
6  9 12 11  7  6  1  0  0
1  1  6  8 23  7  5  1  0
0  0  0  1  3  7 14 16 11
;

The response variable y is ordinally scaled. A cumulative logit model is used to investigate the effects of the cheese additives on taste. The following statements invoke PROC HPLOGISTIC to fit this model with y as the response variable and three indicator variables as explanatory variables, with the fourth additive as the reference level. With this parameterization, each Additive parameter compares an additive to the fourth additive.

proc hplogistic data=Cheese;
   freq freq;
   class Additive(ref='4') / param=ref ;
   model y=Additive;
   title 'Multiple Response Cheese Tasting Experiment';
run;

Results from the logistic analysis are shown in Output 55.3.1 through Output 55.3.3.

The "Response Profile" table in Output 55.3.1 shows that the strong dislike (y=1) end of the rating scale is associated with lower Ordered Values in the "Response Profile" table; hence the probability of disliking the additives is modeled.

Output 55.3.1: Proportional Odds Model Regression Analysis

Multiple Response Cheese Tasting Experiment

The HPLOGISTIC Procedure

Performance Information
Execution ModeSingle-Machine
Number of Threads4

Data Access Information
DataEngineRolePath
WORK.CHEESEV9InputOn Client

Model Information
Data SourceWORK.CHEESE
Response Variabley
Frequency Variablefreq
Class ParameterizationReference
DistributionMultinomial
Link FunctionCumulative Logit
Optimization TechniqueNewton-Raphson with Ridging

Class Level Information
ClassLevelsReference
Value
Values
Additive441 2 3 4

Number of Observations Read36
Number of Observations Used28
Sum of Frequencies Read208
Sum of Frequencies Used208

Response Profile
Ordered
Value
yTotal
Frequency
117
2210
3319
4427
5541
6628
7739
8825
9912

You are modeling the probabilities of levels of y having lower Ordered Values in the Response Profile Table.



Output 55.3.2: Proportional Odds Model Regression Analysis

Iteration History
IterationEvaluationsObjective
Function
ChangeMax Gradient
042.0668312595.0.137412
121.73195603170.334875230.062757
221.71051500480.021441030.008919
321.70997161910.000543390.00035
421.70997092510.000000696.981E-7
521.70997092510.000000002.98E-12

Convergence criterion (GCONV=1E-8) satisfied.

Dimensions
Columns in X11
Number of Effects2
Max Effect Columns3
Rank of Cross-product Matrix11
Parameters in Optimization11

Fit Statistics
-2 Log Likelihood711.35
AIC (smaller is better)733.35
AICC (smaller is better)734.69
BIC (smaller is better)770.06

Testing Global Null Hypothesis: BETA=0
TestChi-SquareDFPr > ChiSq
Likelihood Ratio148.45393<.0001


The positive value (1.6128) for the parameter estimate for Additive=1 in Output 55.3.3 indicates a tendency toward the lower-numbered categories of the first cheese additive relative to the fourth. In other words, the fourth additive tastes better than the first additive. Similarly, the second and third additives are both less favorable than the fourth additive. The relative magnitudes of these slope estimates imply the preference ordering: fourth, first, third, second.

Output 55.3.3: Proportional Odds Model Regression Analysis

Parameter Estimates
ParameterTaste
Rating
EstimateStandard
Error
DFt ValuePr > |t|
Intercept1-7.08020.5640Infty-12.55<.0001
Intercept2-6.02500.4764Infty-12.65<.0001
Intercept3-4.92540.4257Infty-11.57<.0001
Intercept4-3.85680.3880Infty-9.94<.0001
Intercept5-2.52060.3453Infty-7.30<.0001
Intercept6-1.56850.3122Infty-5.02<.0001
Intercept7-0.066880.2738Infty-0.240.8071
Intercept81.49300.3357Infty4.45<.0001
Additive 1 1.61280.3805Infty4.24<.0001
Additive 2 4.96460.4767Infty10.41<.0001
Additive 3 3.32270.4218Infty7.88<.0001