Possible Duplicate:
dropping factor levels in a subsetted data frame in R
I have a data frame with several variables that I'm running a mixed model on using lme(). One of the variables, ForAgeCat, has five factor levels: 1,2,3,4,5.
str(mvthab.3hr.fc$ForAgeCat)
>Factor w/ 5 levels "1","2","3","4",..: 5 5 5 5 5 5 5 5 5 5 ...
The problem is that factor level 3 actually doesn't exist, that is, in this dataset (which is a subset of a larger dataset) there are no observations from factor level 3, which I think is messing with my modeling in lme(). Can someone help me to remove/eliminate factor level 3 from the list of factor levels?
use the function droplevels, like so:
> DF$factor_var = droplevels(DF$factor_var)
More detail:
> # create a sample dataframe:
> col1 = runif(10)
> col1
[1] 0.6971600 0.1649196 0.5451907 0.9660817 0.8207766 0.9527764
0.9643410 0.2179709 0.9302741 0.4195046
> col2 = gl(n=2, k=5, labels=c("M", "F"))
> col2
[1] M M M M M F F F F F
Levels: M F
> DF = data.frame(Col1=col1, Col2=col2)
> DF
Col1 Col2
1 0.697 M
2 0.165 M
3 0.545 M
4 0.966 M
5 0.821 M
6 0.953 F
7 0.964 F
8 0.218 F
9 0.930 F
10 0.420 F
> # now filter DF so that only *one* factor value remains
> DF1 = DF[DF$Col2=="M",]
> DF1
Col1 Col2
1 0.697 M
2 0.165 M
3 0.545 M
4 0.966 M
5 0.821 M
> str(DF1)
'data.frame': 5 obs. of 2 variables:
$ Col1: num 0.697 0.165 0.545 0.966 0.821
$ Col2: Factor w/ 2 levels "M","F": 1 1 1 1 1
> # but still 2 factor *levels*, even though only one value
> DF1$Col2 = droplevels(DF1$Col2)
> # now Col2 has only a single level:
> str(DF1)
'data.frame': 5 obs. of 2 variables:
$ Col1: num 0.697 0.165 0.545 0.966 0.821
$ Col2: Factor w/ 1 level "M": 1 1 1 1 1