| mfastLmCpp {MESS} | R Documentation |
Fast computation of simple regression slopes for each predictor represented by a column in a matrix
mfastLmCpp(y, x, addintercept = TRUE)
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) |
No error checking is done
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.
Claus Ekstrom <claus@rprimer.dk>
## 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)