MESS (version 0.5.7)

mfastLmCpp: 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)

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.

Details

No error checking is done

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)

# }