Dear R help,

Does no one have an idea of where I might find information that could help
me with this problem?  I apologize for re-posting - I have half a suspicion
that my original message did not make it through.

I hope you all had a good weekend and look forward to your reply,
MO


On Fri, Jul 20, 2012 at 11:56 AM, MO wrote:

> 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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