This is what I ended up using:
Data.subset<-data.frame(PlotFinal,YearFinal,BioFinal,ritFinal) ###Subset
of main dataset
Data.length<-sapply(split(Data.subset,PlotFinal),nrow) ### The number
of data points in each plot
Data.sub<-split(Data.subset,PlotFinal) ##Split Data.subset by
PlotFinal
Good.levels<-Data.sub[Data.length>10] ##Take only those Plots that
have >10 years of observation
lmfun<-function(Good.levels)
{lm(ritFinal~BioFinal+YearFinal,data=Good.levels)} ##Set up the
###regression function
lmList<-lapply(Good.levels,lmfun) ###Apply above
function to all "good levels"
coef.List<-lapply(lmList,coef) ###List of the coefficients of EACH
above regression
This seems to have worked beautifully.
So, my current problem is extracting the coefficients from "coef.List" (and
I think this is getting to the roof of my general confusion). coef.List
seems to be a list of a list of lm objects, right? How come them I cannot
call them by their position directly (ie. coef.List[1] gives me the first lm
object. I cannot seem to call the first coefficient in this object (the
intercept).)
My goal is to have a bunch of new lists, each one listing a subset of
coefficients from each regression (ie. one will be a list of all the
intercepts, another the mean of all the "YearFinal" effects, etc).
Thanks again in advance for help with all these noobie questions!
Ben Bolker wrote:
>
>
>
> AllenL wrote:
>>
>> Much thanks! This helped a lot. Another quick one:
>> In using the lmList function in the nlme package, is it possible to
>> subset my data according to the number of observations in each level?
>> (ie. I obviously want to include only those levels in which the
>> observations are of sufficient size for regression). What is the best way
>> to exclude factors of insufficient size? Can I do it inside the lmList
>> function? I've read the requisite help files etc. and two hours later am
>> still confused.
>> Thanks in advance,
>> Allen
>>
>>
>
> Don't know if you can do it directly in lmList, but:
>
> splitdat <- split(mydata,splitfactor)
> lengths <- sapply(splitdat,nrow) ## NOT sapply(splitdat,length)
> splitdat <- splitdat[lengths>minlength]
> lmfun <- function(d) { lm(y~x,data=d) }
> myLmList <- lapply(splitdat,lmfun)
>
> OR
>
> lengths <- sapply(split(mydata,splitfactor),nrow)
> badlevels <- levels(splitfactor)[lengths<minlength]
> subdata <- subset(mydata,!splitfactor %in% badlevels)
>
> and then proceed with lmList
>
> of course, I didn't test any of this ...
>
> Ben Bolker
>
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