# allbetas: Simple linear regressions coefficients In Rfast: A Collection of Efficient and Extremely Fast R Functions

 Many simple linear regressions coefficients R Documentation

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

``` 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.