Compositional (version 5.5)

Multivariate linear regression: 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.

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.

Details

The classical multivariate linear regression model is obtained.

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

# NOT RUN {
library(MASS)
x <- as.matrix(iris[, 1:2])
y <- as.matrix(iris[, 3:4])
multivreg(y, x, plot = TRUE)
# }