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The LOGISTIC Procedure 
proc logistic; model y=x1 x2; run;
The response variable y can be either character or numeric. PROC LOGISTIC enumerates the total number of response categories and orders the response levels according to the ORDER= option in the PROC LOGISTIC statement. The procedure also allows the input of binary response data that are grouped:
proc logistic; model r/n=x1 x2; run;
Here, n represents the number of trials and r represents the number of events.
The following example illustrates the use of PROC LOGISTIC. The data, taken from Cox and Snell (1989, pp. 10 11), consist of the number, r, of ingots not ready for rolling, out of n tested, for a number of combinations of heating time and soaking time. The following invocation of PROC LOGISTIC fits the binary logit model to the grouped data:
data ingots; input Heat Soak r n @@; datalines; 7 1.0 0 10 14 1.0 0 31 27 1.0 1 56 51 1.0 3 13 7 1.7 0 17 14 1.7 0 43 27 1.7 4 44 51 1.7 0 1 7 2.2 0 7 14 2.2 2 33 27 2.2 0 21 51 2.2 0 1 7 2.8 0 12 14 2.8 0 31 27 2.8 1 22 51 4.0 0 1 7 4.0 0 9 14 4.0 0 19 27 4.0 1 16 ; proc logistic data=ingots; model r/n=Heat Soak; run;
The results of this analysis are shown in the following tables.



Using the parameter estimates, you can calculate the estimated logit of p as
If Heat=7 and Soak=1, then logit. Using this logit estimate, you can calculate as follows:
This gives the predicted probability of the event (ingot not ready for rolling) for Heat=7 and Soak=1. Note that PROC LOGISTIC can calculate these statistics for you; use the OUTPUT statement with the P= option.

To illustrate the use of an alternative form of input data, the following program creates the INGOTS data set with new variables NotReady and Freq instead of n and r. The variable NotReady represents the response of individual units; it has a value of 1 for units not ready for rolling (event) and a value of 0 for units ready for rolling (nonevent). The variable Freq represents the frequency of occurrence of each combination of Heat, Soak, and NotReady. Note that, compared to the previous data set, NotReady=1 implies Freq=r, and NotReady=0 implies Freq= nr.
data ingots; input Heat Soak NotReady Freq @@; datalines; 7 1.0 0 10 14 1.0 0 31 14 4.0 0 19 27 2.2 0 21 51 1.0 1 3 7 1.7 0 17 14 1.7 0 43 27 1.0 1 1 27 2.8 1 1 51 1.0 0 10 7 2.2 0 7 14 2.2 1 2 27 1.0 0 55 27 2.8 0 21 51 1.7 0 1 7 2.8 0 12 14 2.2 0 31 27 1.7 1 4 27 4.0 1 1 51 2.2 0 1 7 4.0 0 9 14 2.8 0 31 27 1.7 0 40 27 4.0 0 15 51 4.0 0 1 ;
The following SAS statements invoke PROC LOGISTIC to fit the same model using the alternative form of the input data set.
proc logistic data=ingots descending; model NotReady = Soak Heat; freq Freq; run;
Results of this analysis are the same as the previous one. The displayed output for the two runs are identical except for the background information of the model fit and the "Response Profile" table.
PROC LOGISTIC models the probability of the response level that corresponds to the Ordered Value 1 as displayed in the "Response Profile" table. By default, Ordered Values are assigned to the sorted response values in ascending order.
The DESCENDING option reverses the default ordering of the response values so that NotReady=1 corresponds to the Ordered Value 1 and NotReady=0 corresponds to the Ordered Value 2, as shown in the following table:

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