mvbetas: Many multivariate simple linear regressions coefficients

In Rfast: A Collection of Efficient and Extremely Fast R Functions

Many multivariate simple linear regressions coefficients

Description

Many multivariate simple linear regressions coefficients.

Usage

mvbetas(y, x, pvalue = FALSE)

Arguments

y	A matrix with the data, where rows denotes the observations and the columns contain the dependent variables.
x	A numerical vector with one continuous independent variable only.
pvalue	If you want a hypothesis test that each slope (beta coefficient) is equal to zero set this equal to TRUE. It will also produce all the correlations between y and x.

Details

It is a function somehow opposite to the allbetas. Instead of having one y and many xs we have many ys and one x.

Value

A matrix with the constant (alpha) and the slope (beta) for each simple linear regression. If the p-value is set to TRUE, the correlation of each y with the x is calculated along with the relevant p-value.

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <[e-mail]> and Manos Papadakis <[e-mail]>.

See Also

allbetas, correls, univglms

Examples

y <- matrnorm(100, 100)
x <- rnorm(100)
a <- mvbetas(y, x, pvalue = FALSE)
b <- matrix(nrow = 100, ncol = 2)
z <- cbind(1, x)

a <- mvbetas(y, x)
b[2, ] <- coef( lm.fit( z, y[, 1] ) )
b[2, ] <- coef( lm.fit( z, y[, 2] ) )
x <- NULL

Rfast documentation built on Nov. 9, 2023, 5:06 p.m.