I can't ... I don't know why but I can't

When I use it:

logit <- glm(bach ~ egp4 + programa, weight=wst7,
family=quasibinomial(link"logit"))

I reach the same betas that in STATA, but the hypothesis test, the t value,
and the std. error is different.

I think that the solution can't be so far from this...


On Fri, Nov 23, 2012 at 9:49 PM, Anthony Damico <ajdam...@gmail.com> wrote:

> from your stata output, it looks like you need to use the survey package
> in R
>
> for step-by-step instructions about how to do this (and comparisons to
> stata), see
>
> http://journal.r-project.org/archive/2009-2/RJournal_2009-2_Damico.pdf
>
> once you're ready to run the regression, use svyglm() instead of glm() and
> drop the weights argument (since it will already be part of the survey
> design)   :)
>
>
>
> On Fri, Nov 23, 2012 at 3:13 PM, Pablo Menese <pmen...@gmail.com> wrote:
>
>> Until a weeks ago I used stata for everything.
>> Now I'm learning R and trying to move. But, in this stage I'm testing R
>> trying to do the same things than I used to do in stata whit the same
>> outputs.
>> I have a problem with the logit, applying weights.
>>
>> in stata I have this output
>> . svy: logit bach job2 mujer i.egp4 programa delay mdeo i.str evprivate
>> (running logit on estimation sample)
>>
>> Survey: Logistic regression
>>
>> Number of strata   =         1                  Number of obs      =
>> 248
>> Number of PSUs     =       248                  Population size    =
>> 5290.1639
>> Design df          =       247
>> F(  11,    237)    =      4.39
>> Prob > F           =    0.0000
>>
>>
>> Linearized
>> bach       Coef.   Std. Err.      t    P>t     [95% Conf. Interval]
>>
>> job2   -.4437446   .4385934    -1.01   0.313    -1.307605    .4201154
>> mujer    1.070595   .4169919     2.57   0.011     .2492812    1.891908
>>
>> egp4
>> 2    -.4839342    .539808    -0.90   0.371    -1.547148    .5792796
>> 3    -1.288947   .5347344    -2.41   0.017    -2.342168   -.2357263
>> 4    -.8569793   .5106425    -1.68   0.095    -1.862748    .1487898
>>
>> programa    .9694352   .5677642     1.71   0.089    -.1488415    2.087712
>> delay   -1.552582   .5714967    -2.72   0.007    -2.678211    -.426954
>> mdeo   -.7938904   .3727571    -2.13   0.034    -1.528078   -.0597025
>>
>> str
>> 2    -1.122691   .5731879    -1.96   0.051     -2.25165    .0062682
>> 3    -2.056682   .6350485    -3.24   0.001    -3.307483   -.8058812
>>
>> evprivate   -1.962431   .5674143    -3.46   0.001    -3.080018   -.8448431
>> _cons    2.308699   .7274924     3.17   0.002     .8758187    3.741578
>>
>>
>> the best that i get in R was:
>>
>> glm(formula = bach ~ job2 + mujer + egp4 + programa + delay +
>>     mdeo + str + evprivate, family = quasibinomial(link = "logit"),
>>     weights = wst7)
>>
>> Deviance Residuals:
>>      Min        1Q    Median        3Q       Max
>> -12.5951   -3.9034   -0.9412    3.8268   11.2750
>>
>> Coefficients:
>>                            Estimate Std. Error t value Pr(>|t|)
>> (Intercept)                  2.3087     0.7173   3.218  0.00147 **
>> job2                        -0.4437     0.4355  -1.019  0.30926
>> mujer                        1.0706     0.3558   3.009  0.00290 **
>> egp4intermediate (iii, iv)  -0.4839     0.4946  -0.978  0.32890
>> egp4skilled manual workers  -1.2889     0.5268  -2.447  0.01514 *
>> egp4working class           -0.8570     0.4625  -1.853  0.06514 .
>> programa                     0.9694     0.4951   1.958  0.05141 .
>> delay                       -1.5526     0.4878  -3.183  0.00166 **
>> mdeo                        -0.7939     0.4207  -1.887  0.06037 .
>> strest. ii                  -1.1227     0.4809  -2.334  0.02042 *
>> strestr. iii                -2.0567     0.5134  -4.006 8.28e-05 ***
>> evprivate                   -1.9624     0.6490  -3.024  0.00277 **
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> (Dispersion parameter for quasibinomial family taken to be 23.14436)
>>
>>     Null deviance: 7318.5  on 246  degrees of freedom
>> Residual deviance: 5692.8  on 235  degrees of freedom
>>   (103 observations deleted due to missingness)
>> AIC: NA
>>
>> Number of Fisher Scoring iterations: 6
>>
>> Warning message:
>> In summary.glm(logit) :
>>   observations with zero weight not used for calculating dispersion
>>
>> this has the same betas but the hypothesis test has differents values...
>>
>>
>> HELP!!!!
>>
>>         [[alternative HTML version deleted]]
>>
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>>
>

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