Compositional: multivreg – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

multivreg

Multivariate linear regression

Multivariate linear regression.

multivreg(y, x, plot = TRUE, xnew = NULL)

`y`	A matrix with the Eucldidean (continuous) data.
`x`	A matrix with the predictor variable(s), they have to be continuous.
`plot`	Should a plot appear or not?
`xnew`	If you have new data use it, otherwise leave it NULL.

The classical multivariate linear regression model is obtained.

A list including:

`suma`	A summary as produced by `lm`, which includes the coefficients, their standard error, t-values, p-values.
`r.squared`	The value of the R^2 for each univariate regression.
`resid.out`	A vector with number indicating which vectors are potential residual outliers.
`x.leverage`	A vector with number indicating which vectors are potential outliers in the predictor variables space.
`out`	A vector with number indicating which vectors are potential outliers in the residuals and in the predictor variables space.
`est`	The predicted values if xnew is not NULL.

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Giorgos Athineou <gioathineou@gmail.com>

K.V. Mardia, J.T. Kent and J.M. Bibby (1979). Multivariate Analysis. Academic Press.

library(MASS)
x <- as.matrix(iris[, 1:2])
y <- as.matrix(iris[, 3:4])
multivreg(y, x, plot = TRUE)

Compositional Data Analysis

v4.6

GPL (>= 2)

Authors

Michail Tsagris [aut, cre], Giorgos Athineou [aut], Abdulaziz Alenazi [ctb]

Initial release

2021-04-27