Specifying which category is the reference level for glmfit

When using glmfit() with categorical predictor variables, how is reference level determined? Is there a way to specify which category should be the reference level instead of the default?

The equivalent in R is the relevel() command.

Answer 1

[Tom Lane]

There is not currently a way to specify the reference category for categorical predictors in functions like fitglm. The glmfit function doesn't directly support categorical predictors, so you have some control there in the sense that you could use dummyvar and omit any category you want. Of course you could do the same thing with fitglm, and bypass the categorical support.

But you can accomplish this by making your categorical predictor ordinal, and changing the order. First start with the default order:

>> load fisheriris
>> t = array2table(meas);
>> t.species = ordinal(species);
>> fitlm(t,'meas1~meas2+species')
...

                            Estimate      SE       tStat       pValue  
                            ________    _______    ______    __________
      (Intercept)            2.2514     0.36975    6.0889    9.5681e-09
      meas2                 0.80356     0.10634    7.5566    4.1873e-12
      species_versicolor     1.4587     0.11211    13.012    3.4782e-26
      species_virginica      1.9468     0.10001    19.465    2.0945e-42

Now change the order to make 'virginica' first and fit again:

>> t.species = reordercats(t.species,{'virginica' 'versicolor' 'setosa'});
>> fitlm(t,'meas1~meas2+species')
...
                          Estimate       SE        tStat       pValue  
                          ________    ________    _______    __________
    (Intercept)             4.1982     0.32226     13.027    3.1675e-26
    meas2                  0.80356     0.10634     7.5566    4.1873e-12
    species_versicolor    -0.48807    0.090238    -5.4088    2.5354e-07
    species_setosa         -1.9468     0.10001    -19.465    2.0945e-42

You can see that the omitted category changed. As a check, let's verify that the fitted value for the versicolor category at meas2=0 is the same in both fits:

>> 2.2514+1.4587
ans =
    3.7101
>> 4.1982-.48807
ans =
    3.7101

[the cyclist]

Thanks for the answer, Tom. I ended up just reordering my cell array manually (using ismember and then sort), such that the first element was the one I wanted as the reference. It was a bit kludgy, but perhaps no more so than your solution.