Thank you for the answer but I had already tried that way; when I introduce
weights in the glm appears the error:

Warning: non-integer #successes in a binomial glm!

I tried to run the glm regression using the family quasibinomial:

eq <- glm(pip ~ men + age_pr + age_c + I(age_pr^2) + I(age_c^2),
weights = dfweights, data = df, family = quasibinomial(link =

Do you think it could be a right solution?

2016-09-20 18:23 GMT+02:00 Adams, Jean <>:

> If you want your records to be weighted by the survey weights during the
> analysis, then use the weights= argument of the glm() function.
> Jean
> On Tue, Sep 20, 2016 at 5:04 AM, laura roncaglia <
>> wrote:
>> I am a beginner user of R. I am using a national survey to test what
>> variables influence the partecipation in complementary pensions (the
>> partecipation in complementary pension is voluntary in my country).
>> Since the dependent variable is a dummy (1 if the person partecipate and 0
>> otherwise) I want to run a logit or probit regression; moreover I want to
>> run a fixed effect regression since I subset the survey in order to have
>> only the individuals interviewed more than one time.
>> The data frame is composed by several social and economical variables and
>> it also contain a variable "weight" which is the survey weight (they are
>> weighting coefficients to adjust the results of the sample to the national
>> data).
>>  family pers sex income pension1     10    1   F  10000       12
>> 20    1   F  20000       13     20    2   M  40000       04     30
>> 1   M  25000       05     30    2   F  50000       06     40    1   M
>> 60000       1
>> pers is the component of the family and pension takes 1 if the person
>> partecipate to complementary pension (it is a semplification of the
>> original survey, which contains more variables and observation (aroun 22k
>> observations)).
>> I know how to use the plm and glm functions for a fixed effect or logit
>> regressoin; in this case I don't know what to do since I need to take
>> account of the survey weights.
>> I used the svydesing function to "weight" the data frame:
>> df1 <- svydesign(ids=~1, data=df, weights=~dfweight)
>> I used ids=~1 because there isn't a "cluster" variable in the survey (I
>> know that the towns are ramdomly selected and then individuals are
>> ramdomly
>> selected, but there isn't a variable that indicate the stratification).
>> At this point I am lost: I don't know if it is right to use the survey
>> package and then what function use to run the regression, or there is a
>> way
>> to use the plm or glm functions taking account of the weights.
>> I tried so hard to search a solution on the website but if you could give
>> me an answer I'd be glad.
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