Le mardi 27 novembre 2012 à 18:33 -0300, Pablo Menese a écrit : > 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")) You were advised to use svyglm(), not glm(). It's usually considered polite to read carefully the anwsers you get to your questions...
Regards > 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]] > >> > >> > >> ______________________________________________ > >> R-help@r-project.org mailing list > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > >> > >> > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
