lmms.filter.lines: Filter Linear Profiles from Linear Mixed Model output
In timeOmics: Time-Course Multi-Omics data integration

Description Usage Arguments Details Value See Also Examples

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::lmmSpline` command
`lmms.obj`	a `lmmspline` object
`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)