allbetas: Simple linear regressions coefficients

Description Usage Arguments Value Author(s) See Also Examples

View source: R/many_regression_models_correlations.R

Description

Simple linear regressions coefficients.

Usage

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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 <mtsagris@uoc.gr> and Manos Papadakis <papadakm95@gmail.com>.

See Also

mvbetas, correls, univglms, colsums, colVars

Examples

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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

Example output

Loading required package: Rcpp
Loading required package: RcppZiggurat

Rfast documentation built on May 18, 2021, 1:07 a.m.