PROC LOGISTIC can fit a logistic or probit model to a binary or multinomial response. By default, a binary logistic model is fit to a binary response variable, and an ordinal logistic model is fit to a multinomial response variable. To fit a binary or ordinal probit model in these cases, specify the LINK=PROBIT option in the MODEL statement. To fit a nominal (unordered) logistic model to a nominal multinomial response variable, specify the LINK=GLOGIT option. Another approach is to fit a classification tree model. Beginning in SAS^® 9.4 TS1M3, use the HPSPLIT procedure. See the examples in the HPSPLIT documentation.

For a binary response, the CTABLE option in the MODEL statement of PROC LOGISTIC produces actual-by-predicted classification tables for a range of cutoff values applied to the predicted event probabilities for the observations. This option is not available for multinomial responses. For binary or multinomial responses, use the PREDPROBS=INDIVIDUAL option in the OUTPUT statement of PROC LOGISTIC. This option creates a data set with separate variables containing predicted probabilities for the response levels and a variable (_INTO_) containing the predicted response category. You can also request bias-adjusted (cross validated) predicted values and predicted response categories for binary-response models by using the PREDPROBS=CROSSVALIDATE option.

With the data set from either OUTPUT statement option, you can use PROC FREQ to create a cross classification table, often called a confusion matrix, of the actual and predicted response variables for the data used to fit the model. Similarly, an actual by predicted table can be created for a validation data set by using the SCORE statement which also produces a data set containing predicted probability variables and a variable (I_y, where y is the name of your response variable) containing the predicted response category. Note that the validation data set must contain the observed responses in order to produce the table.

Example 1: For the original data

The following uses the example titled "Nominal Response Data: Generalized Logits Model" in the LOGISTIC documentation. The nominal multinomial response, Style, has three levels and PROC LOGISTIC is used to fit a nominal logistic model to the data. The PREDPROBS=INDIVIDUAL option saves the predicted probabilities and the predicted response level (_INTO_) in the data set PREDS. PROC FREQ displays the confusion matrix by cross classifying the actual and predicted response variables. The cell counts of the matrix are saved in data set CellCounts. The subsequent DATA step adds a variable, Match, which indicates when the actual and predicted response levels agree. The mean of Match, computed by PROC MEANS, is the proportion of observations correctly classified by the nominal logistic model.

      proc logistic data=school;
        freq Count; 
        class School Program(ref=first);
        model Style(order=data)=School Program / link=glogit;
        output out=preds predprobs=individual;
        run;
      proc freq data=preds;
        table Style*_INTO_ / out=CellCounts;
        run;
      data CellCounts;
        set CellCounts;
        Match=0;
        if Style=_INTO_ then Match=1;
        run;
      proc means data=CellCounts mean;
        freq count;
        var Match;
        run;

The results show that the nominal logistic model did not classify any of the observations into the TEAM response level and that 33% of the observations were correctly classified by the model.

Frequency Percent Row Pct Col Pct Table of STYLE by _INTO_ STYLE _INTO_(Formatted Value of the Predicted Response) class self Total class 5 27.78 83.33 33.33 1 5.56 16.67 33.33 6 33.33     self 5 27.78 83.33 33.33 1 5.56 16.67 33.33 6 33.33     team 5 27.78 83.33 33.33 1 5.56 16.67 33.33 6 33.33     Total 15 83.33 3 16.67 18 100.00 The MEANS Procedure Analysis Variable : match Mean 0.3333333

Example 2: For original and validation data

The following uses the example titled "Scoring Data Sets" in the LOGISTIC documentation. These statements create a validation data set named NewCrops. It contains five observations from each of the crop types.

      data NewCrops;
         input Crop $ x1-x4;
         datalines;
      Clover        48    54    30    30
      Clover        66    26    39    58
      Clover        55    34    21    66
      Clover        17    -2    18    43
      Clover        34    26    60    90
      Corn          12    27    25    11
      Corn          16    24    19    70
      Corn          15    21    30    32
      Corn          15    25    27    30
      Corn          14    23    27    31
      Cotton        42    52    58    64
      Cotton        30    38    66    32
      Cotton        31    43    -8    78
      Cotton        37    38    -7    35
      Cotton        28    33    11    49
      Soybeans      20    16    19    28
      Soybeans      15    19    28    11
      Soybeans      21    23    23    25
      Soybeans      18    21    23    24
      Soybeans      16    37    23    18
      Sugarbeets    18    29    19    29
      Sugarbeets    43    32    29     7
      Sugarbeets    21    20     1    47
      Sugarbeets    18    43    18    59
      Sugarbeets    32    46    27    17
      ;

In the following statements, the OUTMODEL= option saves the model information to a data set so that it can be used later to score additional data. As in Example 1, the OUTPUT scores the original data and the following steps produce the confusion matrix and the correctly-classified proportion. The SCORE statement uses the fitted model to score the NewCrops data set and saves the result in a data set named NewCropPred.

      proc logistic data=Crops outmodel=CropModel;
        model Crop=x1-x4 / link=glogit;
        output out=preds predprobs=individual;
        score data=NewCrops out=NewCropPred;
        run;
      proc freq data=preds;
        table Crop*_INTO_ / out=CellCounts;
        run;
      data CellCounts;
        set CellCounts;
        Match=0;
        if Crop=_INTO_ then Match=1;
        run;
      proc means data=CellCounts mean;
        freq count;
        var Match;
        run;

The results show that the model correctly classified approximately 53% of the observations in the original data set.

Frequency Percent Row Pct Col Pct Table of CROP by _INTO_ CROP _INTO_(Formatted Value of the Predicted Response) Clover Corn Cotton Soybeans Sugarbeets Total Clover 6 16.67 54.55 46.15 0 0.00 0.00 0.00 2 5.56 18.18 50.00 2 5.56 18.18 25.00 1 2.78 9.09 33.33 11 30.56     Corn 0 0.00 0.00 0.00 7 19.44 100.00 87.50 0 0.00 0.00 0.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 7 19.44     Cotton 4 11.11 66.67 30.77 0 0.00 0.00 0.00 1 2.78 16.67 25.00 1 2.78 16.67 12.50 0 0.00 0.00 0.00 6 16.67     Soybeans 1 2.78 16.67 7.69 1 2.78 16.67 12.50 1 2.78 16.67 25.00 3 8.33 50.00 37.50 0 0.00 0.00 0.00 6 16.67     Sugarbeets 2 5.56 33.33 15.38 0 0.00 0.00 0.00 0 0.00 0.00 0.00 2 5.56 33.33 25.00 2 5.56 33.33 66.67 6 16.67     Total 13 36.11 8 22.22 4 11.11 8 22.22 3 8.33 36 100.00 The MEANS Procedure Analysis Variable : Match Mean 0.5277778

Similarly, these statements produce the confusion matrix and correct classification proportion for the validation data set, NewCrops. Note that the variable containing the predicted response from the SCORE statement is I_Crop rather than _INTO_ as produced by the OUTPUT statement.

      proc freq data=NewCropPred;
         table Crop*I_Crop / out=CellCounts;
         run;
      data CellCounts;
         set CellCounts;
         Match=0;
         if Crop=I_Crop then Match=1;
         run;
      proc means data=CellCounts mean;
         freq count;
         var Match;
         run;

The results indicate that the model was able to correctly classify 52% of the observations in the validation data set.

Frequency Percent Row Pct Col Pct Table of Crop by I_CROP Crop I_CROP(Into: CROP) Clover Corn Cotton Soybeans Sugarbeets Total Clover 3 12.00 60.00 60.00 1 4.00 20.00 16.67 1 4.00 20.00 33.33 0 0.00 0.00 0.00 0 0.00 0.00 0.00 5 20.00     Corn 0 0.00 0.00 0.00 4 16.00 80.00 66.67 0 0.00 0.00 0.00 0 0.00 0.00 0.00 1 4.00 20.00 20.00 5 20.00     Cotton 0 0.00 0.00 0.00 0 0.00 0.00 0.00 2 8.00 40.00 66.67 0 0.00 0.00 0.00 3 12.00 60.00 60.00 5 20.00     Soybeans 0 0.00 0.00 0.00 1 4.00 20.00 16.67 0 0.00 0.00 0.00 4 16.00 80.00 66.67 0 0.00 0.00 0.00 5 20.00     Sugarbee 2 8.00 40.00 40.00 0 0.00 0.00 0.00 0 0.00 0.00 0.00 2 8.00 40.00 33.33 1 4.00 20.00 20.00 5 20.00     Total 5 20.00 6 24.00 3 12.00 6 24.00 5 20.00 25 100.00 The MEANS Procedure Analysis Variable : Match Mean 0.5200000

Should you need to score additional data sets, you can use the saved model information from the OUTMODEL= option. For example, the following statements score a data set named MoreCrops.

      proc logistic inmodel=CropModel;
        score data=MoreCrops out=MoreCropPred;
        run;

Operating System and Release Information

Product Family	Product	System	SAS Release
			Reported	Fixed*
SAS System	SAS/STAT	All	n/a

* For software releases that are not yet generally available, the Fixed Release is the software release in which the problem is planned to be fixed.

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Type:	Usage Note
Priority:	low
Topic:	SAS Reference ==> Procedures ==> LOGISTIC Analytics ==> Categorical Data Analysis Analytics ==> Regression

Date Modified:	2019-04-12 10:18:55
Date Created:	2002-12-16 10:56:39