plr {MESS}

Fast computation of several simple linear regressions

Description

Fast computation of several simple linear regression, where the outcome is analyzed with several marginal analyses, or where several outcome are analyzed separately, or a combination of both.

Usage

plr(y, x, addintercept = TRUE)

## S3 method for class 'numeric'
plr(y, x, addintercept = TRUE)

## S3 method for class 'matrix'
plr(y, x, addintercept = TRUE)

Arguments

y

either a vector (of length N) or a matrix (with N rows)

x

a matrix with N rows

addintercept

boolean. Should the intercept be included in the model by default (TRUE)

Value

a data frame (if Y is a vector) or list of data frames (if Y is a matrix)

Author(s)

Claus Ekstrom [e-mail]

See Also

mfastLmCpp

Examples

N <- 1000  # Number of observations
Nx <- 20   # Number of independent variables
Ny <- 80   # Number of dependent variables

# Simulate outcomes that are all standard Gaussians
Y <- matrix(rnorm(N*Ny), ncol=Ny)  
X <- matrix(rnorm(N*Nx), ncol=Nx)

plr(Y, X)