How to combine categories after mlogtest

[Francis Onyango]

I'm interested in combining outcome variables after running the command: mlogtest, combine

My data is as follows


. dataex CSA $X2list in 1/10

----------------------- copy starting from the next line -----------------------

* Example generated by -dataex-. To install: ssc install dataex
clear
input float CSA double(LnRainfall LnTempMax) byte(ReceiveEW DairySystm CoopMember)
4 7.30459072871209  3.2576040571261 1 1 1
4  7.3047311520909 3.25763483969567 1 1 0
4 7.30484719693626 3.25765065610546 1 1 0
4 7.30485105190397 3.25765132788405 1 1 0
4 7.30486224337103 3.25765461388177 1 1 0
4 7.30486569715882 3.26845600368271 0 3 0
4 7.30502120808853 3.25768208097711 0 2 0
3 7.30547671743472 3.26833016211082 0 1 0
4 7.30557414347333 3.26833833888383 0 2 0
1 7.30587754809625 3.25783774133195 1 1 0
end
label values CSA CSA
label def CSA 1 "FoVoCo", modify
label def CSA 3 "FoV1Co", modify
label def CSA 4 "FoVoC1", modify

The results of mlogtest, combine command is as follows;


. mlogtest, combine

Wald tests for combining alternatives (N=447)

Ho: All coefficients except intercepts associated with a given pair
of alternatives are 0 (i.e., alternatives can be combined)

| chi2 df P>chi2
-----------------+-------------------------
FoVoCo & F1VoCo | 14.035 6 0.029
FoVoCo & FoV1Co | 5.612 6 0.468
FoVoCo & FoVoC1 | 13.790 6 0.032
FoVoCo & F1V1Co | 6.722 6 0.347
FoVoCo & F1VoC1 | 32.485 6 0.000
FoVoCo & FoV1C1 | 38.973 6 0.000
FoVoCo & F1V1C1 | 34.625 6 0.000
F1VoCo & FoV1Co | 14.468 6 0.025
F1VoCo & FoVoC1 | 20.372 6 0.002
F1VoCo & F1V1Co | 11.974 6 0.063
F1VoCo & F1VoC1 | 6.962 6 0.324
F1VoCo & FoV1C1 | 30.602 6 0.000
F1VoCo & F1V1C1 | 26.501 6 0.000
FoV1Co & FoVoC1 | 10.893 6 0.092
FoV1Co & F1V1Co | 4.004 6 0.676
FoV1Co & F1VoC1 | 22.222 6 0.001
FoV1Co & FoV1C1 | 10.876 6 0.092
FoV1Co & F1V1C1 | 12.591 6 0.050
FoVoC1 & F1V1Co | 3.701 6 0.717
FoVoC1 & F1VoC1 | 50.407 6 0.000
FoVoC1 & FoV1C1 | 39.307 6 0.000
FoVoC1 & F1V1C1 | 31.340 6 0.000
F1V1Co & F1VoC1 | 9.935 6 0.127
F1V1Co & FoV1C1 | 2.042 6 0.916
F1V1Co & F1V1C1 | 1.371 6 0.968
F1VoC1 & FoV1C1 | 63.720 6 0.000
F1VoC1 & F1V1C1 | 49.980 6 0.000
FoV1C1 & F1V1C1 | 8.244 6 0.221

This results indeed indicates that some outcomes must be combined. Kindly assist me learn how to combine the outcomes.

Thank you

[Andrew Musau]

mlogtest is from the SPost13 website.

net describe spost13_ado, from(https://jslsoc.sitehost.iu.edu/stata)

You do not provide the mlogit command that precedes the test, so I will use one of Stata's datasets.

webuse sysdsn1, clear
mlogit insure age male nonwhite i.site
lab list `:val lab insure'
mlogtest, combine

Res.:

. lab list `:val lab insure'
insure:
           1 Indemnity
           2 Prepaid
           3 Uninsure

.
. mlogtest, combine

Wald tests for combining alternatives (N=615)

  Ho: All coefficients except intercepts associated with a given pair
      of alternatives are 0 (i.e., alternatives can be combined)

                 |      chi2    df   P>chi2
-----------------+-------------------------
Indemnit & Pre~d |    28.844     5    0.000
Indemnit & Uni~e |     9.310     5    0.097
Prepaid & Unin~e |    13.805     5    0.017

The null hypothesis of the test is that the alternatives can be combined. At the 5% level of significance, we fail to reject the null that Indemnity and Uninsure can be combined. We reject the null for each of the other combinations (as P<0.05). To combine these categories, we simply use recode.

mlogit insure age male nonwhite i.site
lab list `:val lab insure'
mlogtest, combine
recode insure (3=1), g(insure2)

As we originally had 3 categories, after combining Indemnity and Uninsure, we have 2 categories, so we might just as well create one binary variable named prepaid.

g prepaid= insure2==2
logit prepaid age male nonwhite i.site

Res.:

. logit prepaid age male nonwhite i.site

Iteration 0:   log likelihood = -439.51441  
Iteration 1:   log likelihood = -425.46276  
Iteration 2:   log likelihood = -425.42684  
Iteration 3:   log likelihood = -425.42683  

Logistic regression                             Number of obs     =        643
                                                LR chi2(5)        =      28.18
                                                Prob > chi2       =     0.0000
Log likelihood = -425.42683                     Pseudo R2         =     0.0321

------------------------------------------------------------------------------
     prepaid |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |  -.0085536   .0058467    -1.46   0.143    -.0200129    .0029056
        male |   .3744227   .1868547     2.00   0.045     .0081943    .7406511
    nonwhite |    .881216   .2175829     4.05   0.000     .4547614    1.307671
             |
        site |
          2  |   .2920242   .1975406     1.48   0.139    -.0951483    .6791968
          3  |  -.4823571   .2138832    -2.26   0.024    -.9015604   -.0631537
             |
       _cons |  -.1372077   .3100529    -0.44   0.658    -.7449002    .4704847
------------------------------------------------------------------------------

.

[Francis Onyango]

Thank you so much Prof. A. Musau. Let me try it out the update you.

[Francis Onyango]

Thank you once again Prof. Musau. Your guide helped me a great deal.