getErrorModel: Estimates error model parameters sigma0 (basal error) and sigmaR (relative error) and produces a new data.frame with the signals and error model parameters.
Description
The method is based on a maximum-likelihood estimation. The model prediction is the expected variance given the signal, depending on sigma0 and sigmaR.
Usage
getErrorModel(dataexpression, verbose=FALSE)
Arguments
dataexpression
data.frame, standard output from RPPanalyzer's write.Data.
verbose
logical, if TRUE, the function prints out additional information and produces a PDF file in the working directory with the signal vs. variance plots.
Value
data.framewith columns "slide" (factor, the slide names), "ab" (factor, the antibody/target names), "time" (numeric, the time points), "signal" (numeric, signal values), "sigma0" (numeric, error parameter for the constant error), "sigmaR" (numeric, error parameter for the relative error) and other columns depending on the input data.frame
Details
The empirical variance estimator is $\chi^2$ distributed with $n-2$ degrees of freedom, where $n$ is the number of technical replicates. The estimated error parameters maximize the corresponding log-likelihood function. At the moment, the code assumes $n=3$. For cases $n>3$, the error parameters are slightly overestimated, thus, providing a conservative result. The explicit error model is
$$\sigma^2(S) = \sigma_0^2 + S^2\sigma_R^2$$
where $S$ is the signal strength.