[R] scope, lme, ns, nlme, splines

[Duncan Murdoch]

On 06/12/2012 2:28 PM, Jacob Wegelin wrote:
> I want to fit a series of lme() regression models that differ only in the
> degrees of freedom of a ns() spline. I want to use a wrapper function to do
> this. The models will be of the form
>
> y ~ ns(x, df=splineDF)
>
> where splineDF is passed as an argument to a wrapper function.
>
> This works fine if the regression function is lm(). But with lme(),
> I get an error.  fitfunction() below demonstrates this.
>
> Why?

Presumably this is a bug in nlme.  Formulas have associated 
environments.  In your example, that would be the local frame of the 
fitfunction, and splineDF lives there.  It looks as though lme is not 
looking in the formula environment for the splineDF value.

Another workaround besides the one you found is to use substitute() to 
put the value of splineDF into the formula, i.e.

fitfunction<-function(splineDF=1) {
       junk$splineDF <- splineDF
      thislm<-lm( y ~ ns(x, df=splineDF), data= junk)
      thislm
      cat("finished thislm\n")
       fixed <- eval(substitute( y ~ ns(x, df=splineDF), 
list(splineDF=splineDF)))
      thislme<-lme(
          fixed= fixed
          , random= ~ 1 | idvar
          , data= junk)
      thislme
}

It's not much better than your kludge in this example, but it might be 
more convenient if you want to work with different formulas.

Duncan Murdoch

>
> KLUDGEfit() below provides a clumsy solution. It turns the lme()
> command, along with the appropriate value of splineDF, into a text
> string and then uses eval(parse(text=mystring)).  But is there not a
> more elegant solution?
>
> set.seed(5)
> junk<-data.frame(x=rnorm(100))
> junk$y<-junk$x + rnorm(nrow(junk))
> junk$idvar<- factor(sample(LETTERS[1:10], size=nrow(junk), replace=TRUE))
> library("nlme")
> library(splines)
>
> fitfunction<-function(splineDF=1) {
>   	thislm<-lm( y ~ ns(x, df=splineDF), data= junk)
>   	thislm
>   	cat("finished thislm\n")
>   	thislme<-lme(
>   		fixed= y ~ ns(x, df=splineDF)
>   		, random= ~ 1 | idvar
>   		, data= junk)
>   	thislme
> }
> fitfunction()
>
> KLUDGEfit<-function(splineDF=2) {
>   	thislm<-lm( y ~ ns(x, df=splineDF), data= junk)
>   	thislm
>   	cat("finished thislm\n")
>   	mystring<-paste(
>   	"thislme<-lme( fixed= y ~ ns(x, df="
>   	, splineDF
>   	, "), random= ~ 1 | idvar , data= junk)"
>   	, sep="")
>   	eval(parse(text=mystring))
>   	thislme
> }
> KLUDGEfit()
>
> Thanks for any insight
>
> Jacob A. Wegelin
> Assistant Professor
> Department of Biostatistics
> Virginia Commonwealth University
> 830 E. Main St., Seventh Floor
> P. O. Box 980032
> Richmond VA 23298-0032
> U.S.A.
>
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