Example -1
PROC LOGISTIC DATA=sales descending;
CLASS gender (param=ref ref='M');
MODEL purchase = gender;
RUN;
descending; The descending option will model the probability that a customer places an order of $100 or more (response 1). Otherwise, by default, the response 0 would be modeled.
param=ref ref='M' The param option specifies the parametrization of the model that will be used, which in this example is reference cell coding, i.e. the females will be compared to the males (reference group because of ref='Male').
If there are more than two categories in our independent variable, for interpretation we have to use param option.
Example -2
http://www.ats.ucla.edu/stat/sas/faq/proc_logistic_coding.htm
proc logistic data = mydir.hsb2m descending;
class ses (ref='3') / param = ref ;
model hiread = write ses ;
run ;
Looking at the output (below), the coding system shown in the "Class Level Information" section of the output is for two dummy variables, one for category 1 versus 3, and one for category 2 versus 3. Note two other things in the output below. First, that the coefficients in this model are consistent with the odds ratios. That is, exp(-0.9204) = 0.398 and exp(-0.3839) = 0.681. The second thing to notice is that the odds ratios from this model are the same as the odds ratios above. This is expected, since, SAS always uses dummy coding to compute odds ratios, all that has changed is how the categorical variable ses is being parameterized in the part of parameter estimates.
Class Level Information
Class Value Variables Design
SES 1 1 0
2 0 1
3 0 0
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 -7.6872 1.3697 31.4984 <.0001
WRITE 1 0.1438 0.0236 37.0981 <.0001
SES 1 1 -0.9204 0.4897 3.5328 0.0602
SES 2 1 -0.3839 0.3975 0.9330 0.3341
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
WRITE 1.155 1.102 1.209
SES 1 vs 3 0.398 0.153 1.040
SES 2 vs 3 0.681 0.313 1.485
PROC LOGISTIC DATA=sales descending;
CLASS gender (param=ref ref='M');
MODEL purchase = gender;
RUN;
descending; The descending option will model the probability that a customer places an order of $100 or more (response 1). Otherwise, by default, the response 0 would be modeled.
param=ref ref='M' The param option specifies the parametrization of the model that will be used, which in this example is reference cell coding, i.e. the females will be compared to the males (reference group because of ref='Male').
If there are more than two categories in our independent variable, for interpretation we have to use param option.
Example -2
http://www.ats.ucla.edu/stat/sas/faq/proc_logistic_coding.htm
proc logistic data = mydir.hsb2m descending;
class ses (ref='3') / param = ref ;
model hiread = write ses ;
run ;
Looking at the output (below), the coding system shown in the "Class Level Information" section of the output is for two dummy variables, one for category 1 versus 3, and one for category 2 versus 3. Note two other things in the output below. First, that the coefficients in this model are consistent with the odds ratios. That is, exp(-0.9204) = 0.398 and exp(-0.3839) = 0.681. The second thing to notice is that the odds ratios from this model are the same as the odds ratios above. This is expected, since, SAS always uses dummy coding to compute odds ratios, all that has changed is how the categorical variable ses is being parameterized in the part of parameter estimates.
Class Level Information
Class Value Variables Design
SES 1 1 0
2 0 1
3 0 0
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 -7.6872 1.3697 31.4984 <.0001
WRITE 1 0.1438 0.0236 37.0981 <.0001
SES 1 1 -0.9204 0.4897 3.5328 0.0602
SES 2 1 -0.3839 0.3975 0.9330 0.3341
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
WRITE 1.155 1.102 1.209
SES 1 vs 3 0.398 0.153 1.040
SES 2 vs 3 0.681 0.313 1.485
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