[R] Run a fixed effect regression and a logit regression on a national survey that need to be "weighted"
laura roncaglia
roncaglia.laura at gmail.com
Tue Sep 20 12:04:24 CEST 2016
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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