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Re: st: Prediction for fractional logit
From
S N <[email protected]>
To
[email protected]
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 <[email protected]> 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 <[email protected]> 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?
> *
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>
*
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