fitLinearModel {scuttle} | R Documentation

## Fit a linear model

### Description

No-frills fitting of a linear model to the rows of any matrix-like object with numeric values.

### Usage

    fitLinearModel(
      x,
      design,
      get.coefs = TRUE,
      subset.row = NULL,
      BPPARAM = SerialParam(),
      rank.error = TRUE
    )

### Arguments

`x` | A numeric matrix-like object where columns are samples (e.g., cells) and rows are usually features (e.g., genes).
`design` | A numeric design matrix with number of rows equal to `ncol(x)`. This should be of full column rank.
`get.coefs` | A logical scalar indicating whether the coefficients should be returned.
`subset.row` | An integer, character or logical vector indicating the rows of `x` to use for model fitting.
`BPPARAM` | A BiocParallelParam object specifying the parallelization backend to use.
`rank.error` | Logical scalar indicating whether to throw an error when `design` is not of full rank.

### Details

This function is basically a stripped-down version of `lm.fit`, made to operate on any matrix representation (ordinary, sparse, whatever). It is generally intended for use inside other functions that require robust and efficient linear model fitting.

If `design` is not of full rank and `rank.error=TRUE`, an error is raised. If `rank.error=FALSE`, `NA` values are returned for all entries of the output list.

### Value

If `get.coefs=TRUE`, a list is returned containing:

- `coefficents`, a numeric matrix of coefficient estimates, with one row per row of `x` (or a subset thereof specified by `subset.row`) and one column per column of `design`.

- `mean`, a numeric vector of row means of `x`. Computed as a courtesy to avoid iterating over the matrix twice.

- `variance`, a numeric vector of residual variances per row of `x`. Computed by summing the residual effects from the fitted model.

- `residual.df`, an integer scalar containing the residual degrees of freedom for `design`.

Otherwise, if `get.coefs=FALSE`, the same list is returned without `coefficients`.

### Author(s)

Aaron Lun

### Examples

    y <- Matrix::rsparsematrix(1000, 1000, 0.1)
    design <- model.matrix(~runif(1000))

    output <- fitLinearModel(y, design)
    head(output$coefficients)
    head(output$variance)

[Package scuttle version 1.4.0 Index]