Dear R help list,
I have done a lot of searching but have not been able to find an answer to
my problem. I apologize in advance if this has been asked before.
I am applying a mixed model to my data using lmer. I will use sample data
to illustrate my question:
>library(lme4)
>library(arm)
>data("HR", package = "SASmixed")
> str(HR)
'data.frame': 120 obs. of 5 variables:
$ Patient: Factor w/ 24 levels "201","202","203",..: 1 1 1 1 1 2 2 2 2 2
...
$ Drug : Factor w/ 3 levels "a","b","p": 3 3 3 3 3 2 2 2 2 2 ...
$ baseHR : num 92 92 92 92 92 54 54 54 54 54 ...
$ HR : num 76 84 88 96 84 58 60 60 60 64 ...
$ Time : num 0.0167 0.0833 0.25 0.5 1 ...
> fm1 <- lmer(HR ~ baseHR + Time + Drug + (1 | Patient), HR)
> fixef(fm1) ##Extract estimates of fixed effects
(Intercept) baseHR Time Drugb Drugp
32.6037923 0.5881895 -7.0272873 4.6795262 -1.0027581
> se.fixef(fm1) ##Extract standard error of estimates of fixed effects
(Intercept) baseHR Time Drugb Drugp
9.9034008 0.1184529 1.4181457 3.5651679 3.5843026
##Because the estimate of the fixed effects are displayed as differences
from the intercept (I think?), I can back calculate the actual effect sizes
easily enough. However, how would I do a similar calculation for the
standard error for these effect sizes (since these error estimates are for
the difference in means of effects) if my design isn't balanced (which
confuses things tremendously when working with a data set as large as
mine)? It may help to point out that I'm working with microarray data;
applying the same model for each gene (hundreds of genes total) across
multiple samples (hundreds of samples total), but as an R beginner I like
to start with small data samples and work my way up.
I appreciate the help,
MO
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