I am a beginner user of R. I am using a national survey to test what variables influence the participation in complementary pensions (the participation in complementary pension is voluntary in my country).
Since the dependent variable is a dummy (1 if the person participate 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 pension 1 10 1 F 10000 1 2 20 1 F 20000 1 3 20 2 M 40000 0 4 30 1 M 25000 0 5 30 2 F 50000 0 6 40 1 M 60000 1
pers is the component of the family and pension takes 1 if the person participate to complementary pension (it is a simplification of the original survey, which contains more variables and observation (around 22k observations)).
I know how to use the plm and glm functions for a fixed effect or logit regression; in this case I don't know what to do since I need to take account of the survey weights.
I used the
svydesign 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.