The LOGISTIC Procedure

TEST Statement

  • <label:> TEST equation1 <, equation2, …> </ option>;

The TEST statement tests linear hypotheses about the regression coefficients. The Wald test is used to perform a joint test of the null hypotheses upper H 0 colon bold upper L bold-italic beta equals bold c specified in a single TEST statement, where bold-italic beta is the vector of intercept and slope parameters. When bold c equals bold 0 you should specify a CONTRAST statement instead.

Each equation specifies a linear hypothesis (a row of the bold upper L matrix and the corresponding element of the bold c vector). Multiple equations are separated by commas. The label, which must be a valid SAS name, is used to identify the resulting output and should always be included. You can submit multiple TEST statements.

The form of an equation is as follows:

term<bold plus-or-minus term bold ellipsis> <bold equals bold plus-or-minusterm <bold plus-or-minustermbold ellipsis>>

where term is a parameter of the model, or a constant, or a constant times a parameter. Intercept and CLASS variable parameter names should be specified as described in the section Parameter Names in the OUTEST= Data Set. For multinomial response models, this form enables you to construct tests of parameters from specific logits. When no equal sign appears, the expression is set to 0. The following statements illustrate possible uses of the TEST statement:

proc logistic;
   model y= a1 a2 a3 a4;
   test1: test intercept + .5 * a2 = 0;
   test2: test intercept + .5 * a2;
   test3: test a1=a2=a3;
   test4: test a1=a2, a2=a3;
run;

Note that the first and second TEST statements are equivalent, as are the third and fourth TEST statements.

You can specify the following option in the TEST statement after a slash(/):

PRINT

displays intermediate calculations in the testing of the null hypothesis upper H 0 colon bold upper L bold-italic beta equals bold c. These calculations include bold upper L ModifyingAbove bold upper V With caret left-parenthesis ModifyingAbove bold-italic beta With caret right-parenthesis bold upper L prime bordered by left-parenthesis bold upper L ModifyingAbove bold-italic beta With caret minus bold c right-parenthesis and left-bracket bold upper L ModifyingAbove bold upper V With caret left-parenthesis ModifyingAbove bold-italic beta With caret right-parenthesis bold upper L prime right-bracket Superscript negative 1 bordered by left-bracket bold upper L ModifyingAbove bold upper V With caret left-parenthesis ModifyingAbove bold-italic beta With caret right-parenthesis bold upper L prime right-bracket Superscript negative 1 Baseline left-parenthesis bold upper L ModifyingAbove bold-italic beta With caret minus bold c right-parenthesis, where ModifyingAbove bold-italic beta With caret is the maximum likelihood estimator of bold-italic beta and ModifyingAbove bold upper V With caret left-parenthesis ModifyingAbove bold-italic beta With caret right-parenthesis is the estimated covariance matrix of ModifyingAbove bold-italic beta With caret.

For more information, see the section Testing Linear Hypotheses about the Regression Coefficients.

Last updated: December 09, 2022