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) |
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.
Missing values (NA, Inf, NaN) are completely disregarded and pairwise complete cases are used for the analysis.
# NOT RUN {
// Generate 100000 predictors and 100 observations
x <- matrix(rnorm(100*100000), nrow=100)
y <- rnorm(100, mean=x[,1])
mfastLmCpp(y, x)
# }