lmms.filter.lines {timeOmics} | R Documentation

## Filter Linear Profiles from Linear Mixed Model output

### Description

This function filters linear models with highly heterogeneous variability within residues. From an "lmms" output, 2 parameters are tested:

### Usage

    lmms.filter.lines(
      data,
      lmms.obj,
      time,
      homoskedasticity = TRUE,
      MSE.filter = TRUE,
      homoskedasticity.cutoff = 0.05
    )

### Arguments

`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.

### Details

* homo-sedasticity of the residues with a Breusch-Pagan test * low dispersion with a cutoff on the MSE (mean squared error)

### Value

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

### See Also

`bptest`

### Examples

    # 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)

[Package timeOmics version 1.4.0 Index]