title: Multinomial/Ordinal hypotheses tests in SAS/R
body: <p>I'm trying to recreate some SAS output in R. I'm doing ordinal/multinomial regression using the polr and multinom functions from the MASS and nnet packages respectively. </p>
  
  <p>The output I want to recreate in R from SAS is the test of the global null via LRT, Score, and Wald tests, as well as the type 3 analysis of effects, i.e. basically the test of the interaction (all interaction terms tested together) and of the main effects. I tried to use the wald.test function from the aod package but it was giving me errors about L and V not being conformable arrays, though I made sure L was a matrix of the same size as the matrix of coefficients entered into the function for the b = argument. </p>
  
  <p>Lastly, is there a quick way to test the proportional odds assumption in R?</p>
  
  <p>Any help/guidance is appreciated. Thanks!</p>
  
  <p>Some example data:</p>
  
  <pre><code>educ &lt;- runif(21483, min = 0, max = 20)
  df &lt;- cbind(gss_cat[, c("marital", "race")], educ)
  
  model &lt;- multinom(marital ~ race*educ, data = df)
  </code></pre>
  
  <p>Basically what I'm trying to reproduce from SAS are the following command lines:</p>
  
  <pre><code>proc logistic data=in desc;
      class race /param=ref;
      model marital = educ race educ*race /link=glogit;
      output out=predicted predprobs=individual;
  run;
  </code></pre>