The GLM Procedure

(View the complete code for this example.)

This example, reported by Stenstrom (1940), analyzes an experiment to investigate how snapdragons grow in various soils. To eliminate the effect of local fertility variations, the experiment is run in blocks, with each soil type sampled in each block. Since these data are balanced, the Type I and Type III SS are the same and are equal to the traditional ANOVA SS.

First, the standard analysis is shown, followed by an analysis that uses the SOLUTION option and includes MEANS and CONTRAST statements. The ORDER=DATA option in the second PROC GLM statement is used so that the ordering of coefficients in the CONTRAST statement can correspond to the ordering in the input data. The SOLUTION option requests a display of the parameter estimates, which are produced by default only if there are no CLASS variables. A MEANS statement is used to request a table of the means with two multiple-comparison procedures requested. In experiments with focused treatment questions, CONTRAST statements are preferable to general means comparison methods. The following statements produce Output 47.1.1 through Output 47.1.4.

title 'Balanced Data from Randomized Complete Block';
data plants;
   input Type $ @;
   do Block = 1 to 3;
      input StemLength @;
      output;
   end;
   datalines;
Clarion  32.7 32.3 31.5
Clinton  32.1 29.7 29.1
Knox     35.7 35.9 33.1
O'Neill  36.0 34.2 31.2
Compost  31.8 28.0 29.2
Wabash   38.2 37.8 31.9
Webster  32.5 31.1 29.7
;

proc glm;
   class Block Type;
   model StemLength = Block Type;
run;

proc glm order=data;
   class Block Type;
   model StemLength = Block Type / solution;

   /*----------------------------------clrn-cltn-knox-onel-cpst-wbsh-wstr */
   contrast 'Compost vs. others'  Type   -1   -1   -1   -1    6   -1   -1;
   contrast 'River soils vs. non' Type   -1   -1   -1   -1    0    5   -1,
                                  Type   -1    4   -1   -1    0    0   -1;
   contrast 'Glacial vs. drift'   Type   -1    0    1    1    0    0   -1;
   contrast 'Clarion vs. Webster' Type   -1    0    0    0    0    0    1;
   contrast "Knox vs. O'Neill"    Type    0    0    1   -1    0    0    0;
run;

   means Type / waller regwq;
run;

This analysis shows that the stem length is significantly different for the different soil types. In addition, there are significant differences in stem length among the three blocks in the experiment.

The GLM procedure is invoked again, this time with the ORDER=DATA option. This enables you to write accurate contrast statements more easily because you know the order SAS is using for the levels of the variable Type. The standard analysis is displayed again, this time including the tests for contrasts that you specified as well as the estimated parameters. These additional results are shown in Output 47.1.2.

The contrast label, degrees of freedom, sum of squares, Mean Square, F Value, and Pr > F are shown for each contrast requested. In this example, the contrast results indicate the following inferences, at the 5% significance level:

In addition to the estimates for the parameters of the model, the results of t tests about the parameters are also displayed. The ‘B’ following the parameter estimates indicates that the estimates are biased and do not represent a unique solution to the normal equations.

The final two pages of output (Output 47.1.3 and Output 47.1.4) present results of the Waller-Duncan and REGWQ multiple-comparison procedures. For each test, notes and information pertinent to the test are given in the output. The Type means are arranged from highest to lowest. Means with the same letter are not significantly different. For this example, while some pairs of means are significantly different, there are no clear equivalence classes among the different soils.

For an alternative method of analyzing and displaying mean differences, including high-resolution graphics, see Example 47.3.