On 24/07/16 02:49, Cristina Cametti wrote:
Dear all,
I am having problems finding a reliable code for a country fixed
effects model with binary dependent variable. I was able to run it for
another part of my research, because in that case the dependent variable
is continuous.
This is my code for the continuous dependent variable “imwbcrm_rec”:
modelfe2 <- lm(imwbcrm_rec ~ tvpol + victim + agea + gndr + eduyrs +
lrscale_GM + imgfrnd_dum + qfimwht +factor(cntry)-1, data=mydata)
Please don’t mind to how I wrote the variables, they are from the
first wave of the ESS survey. At this point, I have three questions:
- do you think this code is correct?
It is impossible to say; members of this list are not in general telepathic.
You need to describe your problem and your data more clearly, or
possibly supply a reproducible example, in order for anyone to have a
ghost of a chance of being able to advise you.
Since the data are all from the
same year (2002), I did not used the ppm package since it is only for
panel data. The results of the previous code make sense, so I am
satisfied. However, I want to be sure that I am running the right code.
- second questions: someone knows the code for the same analysis,
but
having a BINARY dependent variable (aesfdrk_dummy)? I found very
different information on the internet, and unfortunately, I do not know
how to use STATA, so I need to find a reliable code in r. This is the
code that I have now:
modelfe1 <-lm(aesfdrk_dummy ~mixed_neigh + ethnic_neigh + agea+ gndr
+
eduyrs + domicil + partner + tvpol + hincfel_dum + factor(cntry) -1,
data=mydata)
It is very common to model a binary response using a logit model
(i.e. use glm() with family=binomial). Whether this is appropriate in
the given case is impossible to say.
-last question: I have to add some interaction between country level
variables and individual level variables. So, do you think that this
code is right?
mydata$ppltrst_GMXprison_pop <- mydata$ppltrst_GM*mydata$prison_pop
mydata$ppltrst_GMXforeign_pop <-
mydata$ppltrst_GM*mydata$foreign_pop mydata$victimXprison_pop<-
mydata$victim*mydata$prison_pop mydata$victimXforeign_pop<-
mydata$victim*mydata$foreign_pop mydata$mixed_neighXprison_pop<-
mydata$mixed_neigh*mydata$prison_pop mydata$mixed_neighXforeign_pop
<- mydata$mixed_neigh*mydata$foreign_pop
mydata$ethnic_neighXprison_pop <-
mydata$ethnic_neigh*mydata$prison_pop mydata$ethnic_neighXforeign_pop
<- mydata$ethnic_neigh*mydata$foreign_pop
modelfe1.2<-lm(aesfdrk_dummy ~mixed_neigh + ethnic_neigh + agea +
gndr
+ eduyrs + domicil + partner + tvpol + hincfel_dum + victim + ppltrst +
ppltrstXprison_pop + ppltrst_Xforeign_pop_ + victimXprison_pop_ +
victimXforeign_pop + mixed_neighXprison_pop + mixed_neighXforeign_pop +
ethnic_neighXprison_pop + ethnic_neighXforeign_pop + factor(cntry)-1,
data=mydata)
The foregoing makes no sense at all to me. Perhaps I am just being
obtuse, but I doubt it.
You seem to be very much out of your depth. I think you would be
well-advised to seek local statistical consultation.
cheers,
Rolf Turner
--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
