View source: R/lmms.filter.lines.R
lmms.filter.lines | R Documentation |
This function filters linear models with highly heterogeneous variability within residues. From an "lmms" output, 2 parameters are tested:
lmms.filter.lines( data, lmms.obj, time, homoskedasticity = TRUE, MSE.filter = TRUE, homoskedasticity.cutoff = 0.05 )
data |
a data.frame used in the |
lmms.obj |
a |
time |
a numeric vector containing the sample time point information. |
homoskedasticity |
a logical whether or not to test for homoscedasticity with the Breusch-Pagan test. |
MSE.filter |
whether or not to test for low dispersion with a cutoff on the MSE. |
homoskedasticity.cutoff |
a numeric scalar between 0 and 1, p-value threshold for B-P test. |
* homo-sedasticity of the residues with a Breusch-Pagan test * low dispersion with a cutoff on the MSE (mean squared error)
a list containing the following items
filtering.summary |
a data.frame with the different tests per features (passed = TRUE, failed = FALSE) |
to.keep |
features which passed all the tests |
filtered |
the filtered data.frame |
bptest
# data and lmms output data(timeOmics.simdata) data <- timeOmics.simdata$sim lmms.output <- timeOmics.simdata$lmms.output time <- timeOmics.simdata$time # filter filter.res <- lmms.filter.lines(data = data, lmms.obj = lmms.output, time = time)
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