R help - Jun 2011 - Rms package - problems with fit.mult.impute

Hi!
Does anyone know how to do the test for goodness of fit of a logistic model (in
rms package) after running fit.mult.impute?

I am using the rms and Hmisc packages to do a multiple imputation followed by a
logistic regression model using lrm.
Everything works fine until I try to run the test for goodness of fit:
residuals(type=c("gof"))
One needs to specify y=T and x=T in the fit. But I get a warning message when I
do that with fit.multiple.impute.

a<-aregImpute(~med.hist.err+
med.discr+newLiving+No.drugs+Days.categ+Los+Age+Ward+Sex, n.impute=20,
nk=0,data=med.err)
ddist<-datadist(Age,No.drugs,Days.categ, Sex, Living, Ward)
options(datadist="ddist")

fmi<-fit.mult.impute(med.hist.err~Age+No.drugs+Days.categ+Sex+Living+Ward,
fitter=lrm, x=T, y=T,a,data=med.err)
Error in 1:n.impute : NA/NaN argument
In addition: Warning message:
In 1:n.impute : numerical expression has 18 elements: only the first used

It works to do the fit.mult.impute without x and y=T but then I get the
following warning message when running residuals
gof<-residuals(fmi, type=c("gof"))
Error in residuals.lrm(fmi, type = c("gof")) :
  you did not specify y=T in the fit

It was no problem to do the goodness of fit test when I ran the lrm on my
complete data set without multiple imputation and fit.mult.impute.
model.lrm<-lrm(med.hist.err~Age+No.drugs+Days +Sex+Living+Ward, x=TRUE,
y=TRUE)
gof<-residuals(model.lrm, type=c("gof"))

Thanks
Lina
_________________
PhD student
Linnaeus University
Sweden




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There is a problem passing x in the ... arguments to fit.mult.impute when the
function has a formal argument starting with "x" (xtrans).  To get
around
this specify xtrans=a to fit.mult.impute instead of just listing a.
Frank


Lina Hellstr?m wrote:
> 
> Hi!
> Does anyone know how to do the test for goodness of fit of a logistic
> model (in rms package) after running fit.mult.impute?
> 
> I am using the rms and Hmisc packages to do a multiple imputation followed
> by a logistic regression model using lrm.
> Everything works fine until I try to run the test for goodness of fit:
> residuals(type=c("gof"))
> One needs to specify y=T and x=T in the fit. But I get a warning message
> when I do that with fit.multiple.impute.
> 
> a<-aregImpute(~med.hist.err+
> med.discr+newLiving+No.drugs+Days.categ+Los+Age+Ward+Sex, n.impute=20,
> nk=0,data=med.err)
> ddist<-datadist(Age,No.drugs,Days.categ, Sex, Living, Ward)
> options(datadist="ddist")
> 
>
fmi<-fit.mult.impute(med.hist.err~Age+No.drugs+Days.categ+Sex+Living+Ward,
> fitter=lrm, x=T, y=T,a,data=med.err)
> Error in 1:n.impute : NA/NaN argument
> In addition: Warning message:
> In 1:n.impute : numerical expression has 18 elements: only the first used
> 
> It works to do the fit.mult.impute without x and y=T but then I get the
> following warning message when running residuals
> gof<-residuals(fmi, type=c("gof"))
> Error in residuals.lrm(fmi, type = c("gof")) :
>   you did not specify y=T in the fit
> 
> It was no problem to do the goodness of fit test when I ran the lrm on my
> complete data set without multiple imputation and fit.mult.impute.
> model.lrm<-lrm(med.hist.err~Age+No.drugs+Days +Sex+Living+Ward, x=TRUE,
> y=TRUE)
> gof<-residuals(model.lrm, type=c("gof"))
> 
> Thanks
> Lina
> _________________
> PhD student
> Linnaeus University
> Sweden
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
> ______________________________________________
> R-help at 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.
> 

-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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Thank you Frank!
Now it works perfectly fine.
Regards
Lina

Message: 73
Date: Thu, 23 Jun 2011 14:13:06 -0700 (PDT)
From: Frank Harrell <f.harrell@vanderbilt.edu>
To: r-help@r-project.org
Subject: Message-ID: <1308863586428-3621045.post@n4.nabble.com>
Content-Type: text/plain; charset=UTF-8

There is a problem passing x in the ... arguments to fit.mult.impute when the
function has a formal argument starting with "x" (xtrans). To get
around
this specify xtrans=a to fit.mult.impute instead of just listing a.
Frank


Lina Hellstr?m wrote:
>
> Hi!
> Does anyone know how to do the test for goodness of fit of a logistic
> model (in rms package) after running fit.mult.impute?
>
> I am using the rms and Hmisc packages to do a multiple imputation followed
> by a logistic regression model using lrm.
> Everything works fine until I try to run the test for goodness of fit:
> residuals(type=c("gof"))
> One needs to specify y=T and x=T in the fit. But I get a warning message
> when I do that with fit.multiple.impute.
>
> a<-aregImpute(~med.hist.err+
> med.discr+newLiving+No.drugs+Days.categ+Los+Age+Ward+Sex, n.impute=20,
> nk=0,data=med.err)
> ddist<-datadist(Age,No.drugs,Days.categ, Sex, Living, Ward)
> options(datadist="ddist")
>
>
fmi<-fit.mult.impute(med.hist.err~Age+No.drugs+Days.categ+Sex+Living+Ward,
> fitter=lrm, x=T, y=T,a,data=med.err)
> Error in 1:n.impute : NA/NaN argument
> In addition: Warning message:
> In 1:n.impute : numerical expression has 18 elements: only the first used
>
> It works to do the fit.mult.impute without x and y=T but then I get the
> following warning message when running residuals
> gof<-residuals(fmi, type=c("gof"))
> Error in residuals.lrm(fmi, type = c("gof")) :
> you did not specify y=T in the fit
>
> It was no problem to do the goodness of fit test when I ran the lrm on my
> complete data set without multiple imputation and fit.mult.impute.
> model.lrm<-lrm(med.hist.err~Age+No.drugs+Days +Sex+Living+Ward, x=TRUE,
> y=TRUE)
> gof<-residuals(model.lrm, type=c("gof"))
>
> Thanks
> Lina
> _________________
> PhD student
> Linnaeus University
> Sweden
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