R help - Apr 2014 - Extracting the names of coefficients of random effects

Hi All,
I need to be able to manipulate the names of the coefficients from
*ranef()*.

If there is any missing data when fitting a mixed model using lmer, no
estimate is returned for the associated level for that random effect. Thus
if the data input for regions had levels 
	*Region*
        Bolton
	Bradford
        Cambridge		                                                 
        Durham						 
and there was missing data on Bradford then 
* ranef(model)*					gives 
	                                                     (Intercept)
Bolton:                               	          -0.0981763413
Cambridge		                                   0.0151102347
Durham						   0.1837142259

This becomes a problem if I want to use *predict( )* on new data where there
is no missing data on Bradford. In such an instance

	*predict (model, newdata = newInput) *	gives the following error message 

             ?Error in (function (x, n)  : new levels detected in newdata?

I could get round this by checking the Region field of the new data
?newInput? against the names of the levels of the intercept coefficients
from* ranef().*
However, I?m not sure how to access these since if 
*x<- ranef(model)
x *
This gives the same output above, but

	*x[1,1]* only returns the numeric value  -0.0981763413

x is a dataframe with only one field (column) with numeric values and the
names of the levels are not present or identified. There must be a variable
which defines ?Bolton?, Bradford? etc since if I use the write.table
function
*write.table (x, file="/desktop/Dummy.csv", sep = ",", 
col.names = NA,
qmethod = "double")*
This outputs both the names (?Bolton?, Bradford?..) and their corresponding
numeric values to a spreadsheet.

Does anyone know how to do this without resorting to outputting to a
spreadsheet?
 
Regards,

Brian H Willis
Health and Population Sciences
University of Birmingham




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Brian Willis <b.h.willis <at> bham.ac.uk> writes:
> 
> Hi All,
> I need to be able to manipulate the names of the coefficients from
> *ranef()*.
> 
> If there is any missing data when fitting a mixed model using lmer, no
> estimate is returned for the associated level for that random effect. Thus
> if the data input for regions had levels 
> 	*Region*
>         Bolton
> 	Bradford                    
>         Cambridge
>         Durham						 
> and there was missing data on Bradford then 
> * ranef(model)*					gives 
> 	                                                     (Intercept)
> Bolton:                               	          -0.0981763413
> Cambridge		                                   0.0151102347
> Durham						   0.1837142259

  I think you want to use rownames():

library(lme4)
d <- expand.grid(f=factor(LETTERS[1:10]),rep=1:10)
d$y <- rnorm(100)
m <- lmer(y~(1|f),data=d)

rownames(ranef(m)[[1]])
> This becomes a problem if I want to use *predict( )* on new data where
there
> is no missing data on Bradford. In such an instance
> 
> 	*predict (model, newdata = newInput) *
> gives the following error message 
> 
>              ?Error in (function (x, n)  : new levels detected in newdata?
> 
> I could get round this by checking the Region field of the new data
> ?newInput? against the names of the levels of the intercept coefficients
> from* ranef().*
> However, I?m not sure how to access these since if 
> *x<- ranef(model)
> x *
  You should also check the allow.new.levels argument in
?predict.merMod, and send followups to r-sig-mixed-models at r-project.org.