st: Ice and Uvis

[SBvanOosten <sannevanoosten@gmail.com>]

Dear all,

For my thesis on the influence of religiosity on working time, I'm dealing
with a dataset with many missing values. In order to counter this I'm trying
to use "ice". Now I want to run a regression of the variables below. Usually
that looks like this:

hrspweek religiosity residencyNL attitudesindex levelofeducation
presenceofchild presenceofpartner [weight]
LFP religiosity residencyNL attitudesindex levelofeducation presenceofchild
presenceofpartner [weight]

But now I want to include the imputed data. Does anyone know how to do that? 

Thank you,

SB van Oosten

P.S. This is what I have done so far:

findit ice

ice LFP hrspweek religiosity residencyNL attitudesindex levelofeducation
presenceofchild presenceofpartner, dryrun

compress LFP hrspweek religiosity residencyNL attitudesindex
levelofeducation presenceofchild presenceofpartner
*In case there isn't enough space on the disk for this. 

ice LFP hrspweek religiosity residencyNL attitudesindex levelofeducation
presenceofchild presenceofpartner using imputevars.dta, cmd(LFP:logit,
hrspweek:regress, religiosity:regress, residencyNL:mlogit,
attitudesindex:regress, levelofeducation:mlogit, presenceofchild:logit,
presenceofpartner:logit) m(8) seed(1407)



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