multivreg: Multivariate linear regression

In Compositional: Compositional Data Analysis

Multivariate linear regression

Description

Multivariate linear regression.

Usage

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

Arguments

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.

Details

The classical multivariate linear regression model is obtained.

Value

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.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail] and Giorgos Athineou <[e-mail]>.

References

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

See Also

diri.reg, js.compreg, kl.compreg, ols.compreg, comp.reg

Examples

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

Compositional documentation built on Oct. 23, 2023, 5:09 p.m.