| plr {MESS} | R Documentation |
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.
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)
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) |
a data frame (if Y is a vector) or list of data frames (if Y is a matrix)
Claus Ekstrom ekstrom@sund.ku.dk
mfastLmCpp
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)