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]]

