allbetas: Simple linear regressions coefficients

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

Simple linear regressions coefficients

Description

Simple linear regressions coefficients.

Usage

allbetas(y, x, pvalue = FALSE, logged = FALSE)

Arguments

y	A numerical vector with the response variable.
x	A matrix with the data, where rows denotes the observations and the columns contain the independent variables.
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.
logged	A boolean variable; it will return the logarithm of the pvalue if set to TRUE.

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 test statistic and its associated p-value.

Author(s)

Michail Tsagris

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

See Also

mvbetas, correls, univglms, colsums, colVars

Examples

x <- matrix( rnorm(100 * 50), ncol = 50 )
y <- rnorm(100)
r <- cor(y, x)  ## correlation of y with each of the xs
a <- allbetas(y, x)  ## the coefficients of each simple linear regression of y with x
x <- NULL

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