vows (version 0.3.1)

lm.mp: Massively parallel linear regression models

Description

Efficiently fits $V$ linear models with a common design matrix, where $V$ may be very large, e.g., the number of voxels in a brain imaging application.

Usage

lm.mp(Y, formula, store.fitted = FALSE)

Arguments

Y

$n \times V$ outcome matrix.

formula

a formula object such as "~ x1 + x2".

store.fitted

logical: Should the fitted values be stored? For large $V$, setting this to TRUE may cause memory problems.

Value

• coef
  $p \times V$ matrix of coefficient estimates.
• sigma2
  $V$-dimensional vector of error variance estimates.
• se.coef
  $p \times V$ matrix of coefficient standard error estimates.
• X
  $n \times p$ common design matrix.
• fitted
  $n \times V$ matrix of fitted values.

See Also

lm4d, summary.lm.mp

Examples

# Please see example for lm4d