msqrobLm {msqrob2} | R Documentation

## Function to fit msqrob models using lm and rlm

### Description

Low-level function for parameter estimation with msqrob using the ordinary least squares or robust regression base on the MASS::rlm function.

### Usage

    msqrobLm(y, formula, data, robust = TRUE, maxitRob = 5)

### Arguments

`y` | A `matrix` with the quantified feature intensities. The features are along the rows and samples along the columns.
`formula` | Model formula. The model is built based on the covariates in the data object.
`data` | A `DataFrame` with information on the design. It has the same number of rows as the number of columns (samples) of `y`.
`robust` | `boolean(1)` to indicate if robust regression is performed to account for outliers. Default is `TRUE`. If `FALSE` an OLS fit is performed.
`maxitRob` | `numeric(1)` indicating the maximum iterations in the IRWLS algorithm used in the M-estimation step of the robust regression.

### Value

A list of objects of the `StatModel` class.

### Author(s)

Lieven Clement, Oliver M. Crook

### Examples

    # Load example data
    # The data are a Feature object with containing
    # a SummarizedExperiment named "peptide" with MaxQuant peptide intensities
    # The data are a subset of spike-in the human-ecoli study
    # The variable condition in the colData of the Feature object
    # contains information on the spike in condition a-e (from low to high)
    data(pe)

    # Aggregate peptide intensities in protein expression values
    pe <- aggregateFeatures(pe, i = "peptide", fcol = "Proteins", name = "protein")
    pe

    # Fit MSqrob model using robust regression with the MASS rlm function
    models <- msqrobLm(assay(pe[["protein"]]), ~condition, colData(pe))
    #' getCoef(models[[1]])

[Package msqrob2 version 1.0.0 Index]