mfastLmCpp {MESS}

Fast marginal simple regresion analyses

Description

Fast computation of simple regression slopes for each predictor represented by a column in a matrix

Usage

mfastLmCpp(y, x, addintercept = TRUE)

Arguments

y

A vector of outcomes.

x

A matrix of regressor variables. Must have the same number of rows as the length of y.

addintercept

A logical that determines if the intercept should be included in all analyses (TRUE) or not (FALSE)

Details

No error checking is done

Value

A data frame with three variables: coefficients, stderr, and tstat that gives the slope estimate, the corresponding standard error, and their ratio for each column in x.

Author(s)

Claus Ekstrom <[e-mail]>

Examples

## Not run: 
  // Generate 100000 predictors and 100 observations
  x <- matrix(rnorm(100*100000), nrow=100)
  y <- rnorm(100, mean=x[,1])
  mfastLmCpp(y, x)


## End(Not run)