non-binary categorical logistic regression coefficients

[Rita Ryan]

I am trying to see indivual category logistic regression coefficients for a class I am in- the class uses SPSS and they are easily seen there. I looked around and found the commands estout and esttab. I love Stata and asked if I could use it instead of having to learn a new program. estout former gives the same coefficients as in the main logit output so doesn't add anything and esttab also no individual category coefficients. Help please and thanks in advance.

[Clyde Schechter]

What exactly are you looking for in the output that Stata doesn't provide? You refer to them as "individual category coefficients." But when you have a categorical variable as a regressor in -logit- you do get regression coefficients for every category of the regressor except the base category (and the omission of the base category is a matter of mathematical necessity, which also must occur in other statistical packages).

. webuse nhanes2

. logit diabetes i.region

Iteration 0:  Log likelihood = -1999.7591  
Iteration 1:  Log likelihood =  -1996.842  
Iteration 2:  Log likelihood = -1996.8276  
Iteration 3:  Log likelihood = -1996.8276  

Logistic regression                                     Number of obs = 10,349
                                                        LR chi2(3)    =   5.86
                                                        Prob > chi2   = 0.1185
Log likelihood = -1996.8276                             Pseudo R2     = 0.0015

------------------------------------------------------------------------------
    diabetes | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      region |
         MW  |  -.0388123   .1381393    -0.28   0.779    -.3095604    .2319358
          S  |   .1977985   .1314819     1.50   0.132    -.0599012    .4554983
          W  |  -.0698666   .1407068    -0.50   0.620    -.3456469    .2059138
             |
       _cons |  -3.014434   .1034642   -29.14   0.000     -3.21722   -2.811648
------------------------------------------------------------------------------

So what is it you would like to see that isn't there?

[Richard Williams]

Like Clyde, I am not sure what Rita wants that she isn't getting. Perhaps she wants the odds ratios rather than, or in addition to, the coefficients? You can get those by adding the or option.

. webuse nhanes2

. logit diabetes i.region, or nolog

Logistic regression                                     Number of obs = 10,349
                                                        LR chi2(3)    =   5.86
                                                        Prob > chi2   = 0.1185
Log likelihood = -1996.8276                             Pseudo R2     = 0.0015

------------------------------------------------------------------------------
    diabetes | Odds ratio   Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      region |
         MW  |   .9619312   .1328805    -0.28   0.779     .7337694    1.261039
          S  |   1.218717   .1602392     1.50   0.132     .9418576    1.576959
          W  |   .9325183   .1312117    -0.50   0.620     .7077624    1.228647
             |
       _cons |   .0490736   .0050774   -29.14   0.000     .0400663    .0601059
------------------------------------------------------------------------------
Note: _cons estimates baseline odds.

When using esttab, you use the eform option if you want the exponentiated coefficients presented. e.g.

. webuse nhanes2

. logit diabetes i.region

Iteration 0:  Log likelihood = -1999.7591  
Iteration 1:  Log likelihood =  -1996.842  
Iteration 2:  Log likelihood = -1996.8276  
Iteration 3:  Log likelihood = -1996.8276  

Logistic regression                                     Number of obs = 10,349
                                                        LR chi2(3)    =   5.86
                                                        Prob > chi2   = 0.1185
Log likelihood = -1996.8276                             Pseudo R2     = 0.0015

------------------------------------------------------------------------------
    diabetes | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
      region |
         MW  |  -.0388123   .1381393    -0.28   0.779    -.3095604    .2319358
          S  |   .1977985   .1314819     1.50   0.132    -.0599012    .4554983
          W  |  -.0698666   .1407068    -0.50   0.620    -.3456469    .2059138
             |
       _cons |  -3.014434   .1034642   -29.14   0.000     -3.21722   -2.811648
------------------------------------------------------------------------------

. esttab, eform

----------------------------
                      (1)   
                 diabetes   
----------------------------
diabetes                    
1.region                1   
                      (.)   

2.region            0.962   
                  (-0.28)   

3.region            1.219   
                   (1.50)   

4.region            0.933   
                  (-0.50)   
----------------------------
N                   10349   
----------------------------
Exponentiated coefficients; t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001