Dear R-helpers,
I am developing a Mixed-Effects model for a study of immunoassays using
'lme4' library. The study design is as follows: 10 samples were run
using 7 different immunoassays, 3 times each, in duplicates. Data
attached. I have developed the following model:
c.lme <- lmer(Result~SPL + (SPL|Assay/Run) -1, data=data)
This model has excellent predictions - the Rsquared of the predicted vs
measured results is almost 1, with very small RMSE. However, I am not
interested in the estimates of the mean, but in SDs from the model.
I access the SDs using b<-VarCorr(c.lme). There:
- the 'attr(b$Assay, "stddev")' is the assay-to-assay SD component for
each sample (SDaa)
- the 'attr(b$Run, "stddev")' is the run-to-run component (SDrr)
- the 'attr(b, "sc")' i.e. the residual (SDres), would be the
within-run component, but it's a single number for all the samples.
* The problem:
- how to estimate the 'within-run' component (SDres) for each sample
separately, as the two other components?
* Solutions tried:
- subtracting SDaa and SDrr from total SD - sometimes produces
negative results
- adding SDres to SDaa + SDrr is usually greater than total SD
- ...
I have no idea how to do this in a formally acceptable way. Any help
would be appreciated.
Kind regards,
--
Michal J. Figurski, PhD
HUP, Pathology & Laboratory Medicine
Biomarker Research Laboratory
3400 Spruce St. 7 Maloney
Philadelphia, PA 19104
tel. (215) 662-3413
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