R help - Jul 2016 - country fixed effects model with binary dependent variable

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? 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)

-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)


Thank you very much for your attention.
Kind Regards,

Cristina

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

> On Jul 23, 2016, at 7:49 AM, Cristina Cametti <cristina.cametti at
gmail.com> 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,
That is the most <bite-my-tongue> <deleted> preamble I have ever
seen. How can it not be important how you constructed your covariates?   ....
?????
> they are from the first wave of the ESS survey. At this point, I have three
questions:
> - do you think this code is correct?
Really? First you ask us not to question your construction of covariates, and
... THEN ask us if it is "correct"?

> 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)
> 
> -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)
> 
> 
> Thank you very much for your attention.
> Kind Regards,
> 
> Cristina 
> ______________________________________________
> R-help at 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.
David Winsemius
Alameda, CA, USA

Hi Cristina,
As Rolf has noted, you probably don't want to persist with "lm"
since
I think you have dichotomized your initial dependent variable. I also
think that you meant "don't worry about the change of variable
names"
with "how I wrote the variables". I also think that you want to test
interactions between the variables you are adding. _Maybe_ something
like this:

modelfe1.2<-glm(aesfdrk_dummy ~ agea + gndr + eduyrs + domicil +
partner + tvpol + hincfel_dum + ppltrst_GM:prison_pop +
ppltrst_GM:foreign_pop_ + victim*prison_pop_ + victim*foreign_pop +
mixed_neigh*prison_pop + mixed_neigh*foreign_pop +
ethnic_neigh*prison_pop + ethnic_neigh*foreign_pop + factor(cntry)-1,
data=mydata, family="binomial")

Jim


On Sun, Jul 24, 2016 at 12:49 AM, Cristina Cametti
<cristina.cametti at gmail.com> 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? 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)
>
> -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)
>
>
> Thank you very much for your attention.
> Kind Regards,
>
> Cristina
> ______________________________________________
> R-help at 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.

Dear all,

thank you very much for all your answers. Probably, I don?t know how to write in
this mailing list, so I apologize if I was not clear.
My questions were all code related: I am not an R user, this means that I
started using R just few months ago. So, I am seeking advise about the coding
and that is why I was writing here.
The part about not to mind how I wrote the variables meant only that the names
may appear weird because I used the same in the ESS survey and in other cases, I
created new names that do not make sense. So it was just a simple suggestions
about my way to write the name of the variables, because the focus of my
questions were others.
So, I am sorry if I upset you and basically, all wrote by Jim was correct about
my questions.
So thank you Jim, in the meantime I was working on the code and in the end I
came up with the same code as you. I used ?glm? and added ?family=binomial?.  So
now that I have the confirmation from an expert that this is a reliable code for
my analysis, I am more sure about my results.

Thank you all again for your attention. 
Kind Regards,

Cristina 


Il giorno 24/lug/2016, alle ore 06:50, Jim Lemon <drjimlemon at gmail.com>
ha scritto:
> Hi Cristina,
> As Rolf has noted, you probably don't want to persist with
"lm" since
> I think you have dichotomized your initial dependent variable. I also
> think that you meant "don't worry about the change of variable
names"
> with "how I wrote the variables". I also think that you want to
test
> interactions between the variables you are adding. _Maybe_ something
> like this:
> 
> modelfe1.2<-glm(aesfdrk_dummy ~ agea + gndr + eduyrs + domicil +
> partner + tvpol + hincfel_dum + ppltrst_GM:prison_pop +
> ppltrst_GM:foreign_pop_ + victim*prison_pop_ + victim*foreign_pop +
> mixed_neigh*prison_pop + mixed_neigh*foreign_pop +
> ethnic_neigh*prison_pop + ethnic_neigh*foreign_pop + factor(cntry)-1,
> data=mydata, family="binomial")
> 
> Jim
> 
> 
> On Sun, Jul 24, 2016 at 12:49 AM, Cristina Cametti
> <cristina.cametti at gmail.com> 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? 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)
>> 
>> -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)
>> 
>> 
>> Thank you very much for your attention.
>> Kind Regards,
>> 
>> Cristina
>> ______________________________________________
>> R-help at 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.