You need to defined a svydesign object that contains all the variables you want to use. This object is passed as the design= argument to all the analysis functions in the survey package. The functions don't have a data= argument, and many of them will not look outside the design= argument for variables.
So if your data set is called 'data', and the weight variable is called 'peso', define the design object: test <- svydesign(id=~1,weights=~peso, data=data) Then use it in svyglm: logit <- svyglm(bach ~ job2 + mujer + egp4 + programa + delay + mdeo + str + evprivate, family=binomial,design=test) or in svymean: svymean(~mujer, design=test) -thomas On Thu, Nov 29, 2012 at 6:20 AM, Pablo Menese <pmen...@gmail.com> wrote: > Dear Milan... are you serious? > Did you read this? > > I have this problem. > > test <- svydesign(id=~1,weights=~peso) > > logit <- svyglm(bach ~ job2 + mujer + egp4 + programa + delay + mdeo + str > + evprivate, family=binomial,design=test) > > then appear: > > Error in svyglm.survey.design(bach ~ job2 + mujer + egp4 + programa + : > all variables must be in design= argument > > I don't know what this mean... > Please help. > > Quotes from a week ago... > I colud not perform anything using svyglm... I wish... but... I don't know > why... > > > On Tue, Nov 27, 2012 at 6:54 PM, Milan Bouchet-Valat <nalimi...@club.fr > >wrote: > > > 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. > > > > > > [[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. > > -- Thomas Lumley Professor of Biostatistics University of Auckland [[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.
