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Re: st: Prediction for fractional logit


From   S N <liminalzest@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Prediction for fractional logit
Date   Tue, 31 May 2011 08:33:37 -0400

Nick and Maarten,

I am pasting the code and the results below.  Thanks. Shonda


 Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
     prop_gra |        53    .5790939    .4135732          0          1
    prop_obc |        54     .160307    .2915177          0          1
      prop_p1 |        54    .2106382    .2555498          0   .9690722
          land1 |        54    .4508955    .3359333          0          1
          land2 |        54    .1073159    .1590588          0   .7647059
  prop_cash |        53     16.5283    24.05011          0         80
estdc_land1 |        54    .1926632    .3186031          0   .9230769

estd_ext |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         36       65.45       65.45
          1 |         19       34.55      100.00
------------+-----------------------------------

  estd_comm |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         32       58.18       58.18
          1 |         23       41.82      100.00
------------+-----------------------------------


prop_obc, prop_p1, prop_land1, prop_land2  are all proportions
themselves. estd_ext, estd_comm, d are dummies, estdc_land1 is an
interaction term between prop_land1 and estd_comm (so interaction with
a dummy and a proportion).

****

glm prop_gra prop_obc prop_p1 prop_land1 prop_land2 prop_cash estd_ext
estd_comm estdc_land1 d, family (binomial) link (logit)  nolog


Generalized linear models                          No. of obs      =        52
Optimization     : ML                                    Residual df
  =        42

Scale parameter =         1
Deviance         =  24.98012225                    (1/df) Deviance =  .5947648
Pearson          =  25.64304946                    (1/df) Pearson  =  .6105488

Variance function: V(u) = u*(1-u/1)                [Binomial]
Link function    : g(u) = ln(u/(1-u))                  [Logit]


AIC             =   1.20732
Log likelihood   = -21.39033265                    BIC             = -140.9721

------------------------------------------------------------------------------
             |                 OIM
   prop_gra |      Coef.           Std. Err.      z    P>|z|     [95%
Conf. Interval]
-------------+----------------------------------------------------------------
    prop_obc |    1.20795   1.521787     0.79   0.427    -1.774697    4.190597
      prop_p1 |   2.271956   1.642525     1.38   0.167     -.947333    5.491246
 prop_land1 |   1.152989   1.597026     0.72   0.470    -1.977125    4.283103
 prop_land2 |   1.688074   2.455906     0.69   0.492    -3.125414    6.501561
  prop_cash |   .0509537   .0253546     2.01   0.044     .0012596    .1006478
     estd_ext |   1.498499     1.0195     1.47   0.142    -.4996845    3.496682
 estd_comm |   2.725557   1.472281     1.85   0.064    -.1600611    5.611175
estdc_land1 |  -3.777789   2.618245    -1.44   0.149    -8.909454    1.353876
                 d |  -.7853135   .9559989    -0.82   0.411
-2.659037     1.08841
          _cons |  -2.317875    1.36365    -1.70   0.089    -4.990581
  .3548302

****
predict gra_pred, xb

*****
summarize gra_pred

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
    gra_pred |        52    .5611184    1.595445   -2.67838   3.891145

*******


On Tue, May 31, 2011 at 8:09 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> I agree with your implication that this should not happen. Please tell
> us more about what you did, including exact -glm- and -predict-
> commands and -summarize- results for all variables in the model.
>
> Nick
>
> On Tue, May 31, 2011 at 1:04 PM, S N <liminalzest@gmail.com> wrote:
>
>> I am using glm in combination with the link(logit) family(binomial)
>> robust options to estimate proportion [0,1] of households engaged in a
>> specific activity. However, the predict command thereafter provides me
>> with predicted values that takes the values outside [0,1]. What could
>> I be doing wrong?
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>

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