getErrorModel {RPPanalyzer}

Estimates error model parameters var0 (basal variance) and varR (relative variance) 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 var0 and varR.

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.

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 = var0 + varR S^2

where S is the signal strength.

Value

data.frame

with columns "slide" (factor, the slide names), "ab" (factor, the antibody/target names), "time" (numeric, the time points), "signal" (numeric, signal values), "var0" (numeric, error parameter for the constant error, equivalent to sigma0^2), "varR" (numeric, error parameter for the relative error, equivalent to sigmaR^2) and other columns depending on the input data.frame

Author(s)

Daniel Kaschek, Physikalisches Institut, Uni Freiburg. Email: [e-mail]