R: Simple linear regressions coefficients

Many simple linear regressions coefficients {Rfast} | 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@yahoo.gr> and Manos Papadakis <papadakm95@gmail.com>.

### 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

[Package Rfast version 2.0.0 Index]