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Getting Started

The following DATA step statements create the SAS data set Gossypol:

   data Gossypol;
      input Dose n;
      do i=1 to n;
         input Gain @@;
         output;
      end;
      datalines;
    0 16
      228 229 218 216 224 208 235 229 233 219 224 220 232 200 208 232

   .04 11
      186 229 220 208 228 198 222 273 216 198 213
   .07 12
      179 193 183 180 143 204 114 188 178 134 208 196
   .10 17
      130 87 135 116 118 165 151 59 126 64 78 94 150 160 122 110 178
   .13 11
      154 130 130 118 118 104 112 134 98 100 104
   ;

The data set Gossypol contains the variable Dose, which represents the amount of gossypol additive, and the variable Gain, which represents the weight gain.

Researchers are interested in whether there is a difference in weight gain among the different dose levels of gossypol. The following statements invoke the NPAR1WAY procedure to perform a nonparametric analysis of this problem.

   proc npar1way data=Gossypol;
      class Dose;
      var Gain;
   run;

The variable Dose is the CLASS variable, and the VAR statement specifies the variable Gain is the response variable. The CLASS statement is required, and you must name only one CLASS variable. You may name one or more analysis variables in the VAR statement. If you omit the VAR statement, PROC NPAR1WAY analyzes all numeric variables in the data set except for the CLASS variable, the FREQ variable, and the BY variables.

Since no analysis options are specified in the PROC NPAR1WAY statement, the WILCOXON, MEDIAN, VW, SAVAGE, and EDF options are invoked by default. The results of these analyses are shown in the following tables.

These tables are produced with the ANOVA option. For each level of the CLASS variable Dose, PROC NPAR1WAY displays the number of observations and the mean of the analysis variable Gain. PROC NPAR1WAY displays a standard analysis of variance on the raw data. This gives the same results as the GLM and ANOVA procedures. The p-value for the F test is <.0001, which indicates that Dose accounts for a significant portion of the variability in the dependent variable Gain.

Next PROC NPAR1WAY displays the one-way ANOVA statistic, which for Wilcoxon scores is known as the Kruskal-Wallis test. The statistic equals 52.6656, with four degrees of freedom, which is the number of class levels minus one. The p-value, or probability of a larger statistic under the null hypothesis, is <.0001. This leads to rejection of the null hypothesis that there is no difference in location for Gain among the levels of Dose. This p-value is asymptotic, computed from the asymptotic chi-square distribution of the test statistic. For certain data sets it may also be useful to compute the exact p-value; for example, for small data sets, or data sets that are sparse, skewed, or heavily tied. You can use the EXACT statement to request exact p-values for any of the location or scale tests available in PROC NPAR1WAY.

In the preceding example, the CLASS variable Dose has five levels, and the analyses examines possible differences among these five levels, or samples. The following statements invoke the NPAR1WAY procedure to perform a nonparametric analysis of the two lowest levels of Dose.

   proc npar1way data=Gossypol;
      where Dose <= .04;
      class Dose;
      var Gain;
   run;

The following tables show the results.

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