Thank you for considering my email and replying.
Well, I am working with TIMSS 2007 survey data ( Trends in
international mathematics and science study).
TIMSS is a stratified DATA, where the primary sampling unit is the
school
I want to test the effect of some independent variables reflecting the
socio-economic status of students on math achievement
I have 18 independent variables :Female+Age+calculator+computer+desk+
dictionary+internet+work+Book2+Book3+Book4+Book5+Pedu1+Pedu2+Pedu3+Pedu4+Born1+Born2
All my variables are binary variables except for age, and of course the
dependent variable wich is the achievement score of the student (Math1
in my regression)
I have a sample of 3169 observations (i.e students, 3169 achievement
scores)
Data is missing at 23%.
The idea is to test the effect of these variables (known to reflect the
socio-economic status of students) at the lower and upper quantiles
taking into account the survey design features (sampling weight (which
is in my case the studnt weight) clustering and stratification)
Why do I need th psudo-R squared? Because I want to calculate the
increase in the pseudo r squared after introducing the school variales:
I explain
I will do first regressions with the socio-economic variables (SES), and
I calculate the pseudo R squared
In a second stage, I will introduce the school variables ( which are
also binary variables), after controlling for socio-economic status, and
calculate the pseudo R squared. The difference between the two pseudo R
squared will tell me if SES variables account more or less than school
variables for students' achievement.
Why with replicates? To eliminate the intra-cluster correlations between
schools.
I ran the following commands successfully:
mydesign
<-svydesign(ids=~IDSCHOOL,strata=~IDSTRATE,data=TUN,nest=TRUE,weights=~TOTWGT)
bootdesign <- as.svrepdesign(mydesign,type="auto",replicates=150)
fit<-withReplicates(bootdesign,quote(coef(rq(Math1~Female+Age+calculator+computer+desk+
+dictionary+internet+work+Book2+Book3+Book4+Book5+Pedu1+Pedu2+Pedu3+Pedu4+Born1+Born2,tau=0.9,weights=.weights,
method="fn"))))
I want get the pseudo R squared but I failed. I read a query dating
from
August 2006, [R] Pseudo R for Quant Reg and the answer to it:
# from https://stat.ethz.ch/pipermail/r-help/2006-August/110386.html
rho <- function(u,tau=.5)u*(tau - (u < 0))
V <- sum(rho(f$resid, f$tau))
The structure of my fit object is the following:
str (fit)
Class 'svrepstat' atomic [1:19] 713.24 -24.01 -18.37 9.05 7.71
...
..- attr(*, "var")= num [1:19, 1:19] 2839.3 10.2 -122.1 -332.4
-42.3
...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:19] "(Intercept)" "Female" "Age"
"calculator" ...
.. .. ..$ : chr [1:19] "(Intercept)" "Female" "Age"
"calculator" ...
.. ..- attr(*, "means")= Named num [1:19] 710.97 -24.03 -18.3
9.39
7.58 ...
.. .. ..- attr(*, "names")= chr [1:19] "(Intercept)" "Female"
"Age"
"calculator" ...
..- attr(*, "statistic")= chr "theta"
Thank you in advance for replying and for your help
Le 2014-09-19 04:19, Anthony Damico a écrit :
here is a reproducible example, mostly from ?withReplicates. i think
something would have to be done using return.replicates=TRUE to
manually compute survey-adjusted residuals, but i'm not really sure
what nor whether the pseudo r^2 would be meaningful :/
library(survey)
library(quantreg)
data(api)
## one-stage cluster sample
dclus1<-svydesign(id=~dnum, weights=~pw, data=apiclus1, fpc=~fpc)
## convert to bootstrap
bclus1<-as.svrepdesign(dclus1,type="bootstrap", replicates=100)
## median regression
fit <- withReplicates(bclus1, quote(coef(rq(api00~api99, tau=0.5,
weights=.weights,method="fn"))))
# # # no longer from ?withReplicates # # #
# from https://stat.ethz.ch/pipermail/r-help/2006-August/110386.html
[3]
rho <- function(u,tau=.5)u*(tau - (u < 0))
V <- sum(rho(fit$resid, fit$tau)) # # breaks
On Thu, Sep 18, 2014 at 1:55 PM, David L Carlson <dcarl...@tamu.edu>
wrote:
It is hard to say because we do not have enough information. R has
approximately 6,000 packages and you have not told us which ones you
are using. You have not told us much about your data and you have
not told us where to find the query from August 2006. The basic
problem is that your "fit" is not the same as the "f" in the query.
Your fit object is not very complicated. If you look at the output
from str(fit) you will see that fit is an "atomic" vector (note the
wording in your error message) with a series of attributes that are
probably documented in the help pages for the functions you are
using. There is nothing called resid inside fit. It is likely that
the post you are looking at refers to the output from rq(...) or
perhaps predict(rq(...)), but not the output from
withReplicates(..., quote(coef(rq(...)))) which is what fit is.
-------------------------------------
David L Carlson
Department of Anthropology
Texas A&M University
College Station, TX 77840-4352
-----Original Message-----
From: r-help-boun...@r-project.org
[mailto:r-help-boun...@r-project.org] On Behalf Of Donia Smaali
Bouhlila
Sent: Thursday, September 18, 2014 9:54 AM
To: r-help@r-project.org
Subject: [R] Pseudo R squared for quantile regression with
replicates
Hi,
I am a new user of r software. I intend to do quantile regressions
with
complex survey data using replicate method. I have ran the
following
commands successfully:
mydesign
<-svydesign(ids=~IDSCHOOL,strata=~IDSTRATE,data=TUN,nest=TRUE,weights=~TOTWGT)
bootdesign <- as.svrepdesign(mydesign,type="auto",replicates=150)
fit<-
withReplicates(bootdesign,quote(coef(rq(Math1~Female+Age+calculator+computer+desk+
+
dictionary+internet+work+Book2+Book3+Book4+Book5+Pedu1+Pedu2+Pedu3+Pedu4+Born1+Born2,tau=0.5,weights=.weights,
method="fn"))))
I want get the pseudo R squared but I failed. I read a query dating
from
August 2006, [R] Pseudo R for Quant Reg and the answer to it:
rho <- function(u,tau=.5)u*(tau - (u < 0))
V <- sum(rho(f$resid, f$tau))
I copied it and paste it , replacing f by fit I get this error
message:
Error in fit$resid : $ operator is invalid for atomic vectors, I
don't
know what it means
The fit object is likely to be quite complicated I used str() to
see
what it looks like:
str (fit)
Class 'svrepstat' atomic [1:19] 713.24 -24.01 -18.37 9.05 7.71
...
..- attr(*, "var")= num [1:19, 1:19] 2839.3 10.2 -122.1 -332.4
-42.3
...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:19] "(Intercept)" "Female" "Age"
"calculator" ...
.. .. ..$ : chr [1:19] "(Intercept)" "Female" "Age"
"calculator" ...
.. ..- attr(*, "means")= Named num [1:19] 710.97 -24.03 -18.3
9.39
7.58 ...
.. .. ..- attr(*, "names")= chr [1:19] "(Intercept)" "Female"
"Age"
"calculator" ...
..- attr(*, "statistic")= chr "theta"
How can I retrieve the residuals?? and calculate the pseudo R
squared??
Any help please
--
Dr. Donia Smaali Bouhlila
Associate-Professor
Department of Economics
Faculté des Sciences Economiques et de Gestion de Tunis
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help [1]
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html [2]
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help [1]
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html [2]
and provide commented, minimal, self-contained, reproducible code.
Links:
------
[1] https://stat.ethz.ch/mailman/listinfo/r-help
[2] http://www.R-project.org/posting-guide.html
[3] https://stat.ethz.ch/pipermail/r-help/2006-August/110386.html
--
Dr. Donia Smaali Bouhlila
Associate-Professor
Department of Economics
Faculté des Sciences Economiques et de Gestion de Tunis
______________________________________________
R-help@r-project.org mailing list
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.
