R: Fast computation of several simple linear regressions

plr {MESS} | R Documentation

## 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 ekstrom@sund.ku.dk

### 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)

[Package MESS version 0.5.9 Index]