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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