R: Fast marginal simple regresion analyses

mfastLmCpp {MESS} | R Documentation

## 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 <claus@rprimer.dk>

### 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)

[Package MESS version 0.5.6 Index]