Multivariate linear regression {Compositional} | R Documentation |
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 |
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.
diri.reg, js.compreg, kl.compreg, ols.compreg, comp.reg
library(MASS) x <- as.matrix(iris[, 1:2]) y <- as.matrix(iris[, 3:4]) multivreg(y, x, plot = TRUE)