lm.mp: Massively parallel linear regression models

In vows: Voxelwise Semiparametrics

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.

Author(s)

Philip Reiss [e-mail], Lei Huang [e-mail], and Yin-Hsiu Chen [e-mail]

See Also

lm4d, summary.lm.mp

Examples

# Please see example for lm4d

vows documentation built on May 2, 2019, 9:26 a.m.