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Multivariate linear regression.
multivreg(y, x, plot = TRUE, xnew = NULL)
A matrix with the Eucldidean (continuous) data.
A matrix with the predictor variable(s), they have to be continuous.
Should a plot appear or not?
If you have new data use it, otherwise leave it NULL.
A list including:
A summary as produced by lm, which includes the coefficients, their standard error, t-values, p-values.
lm
The value of the \(R^2\) for each univariate regression.
A vector with number indicating which vectors are potential residual outliers.
A vector with number indicating which vectors are potential outliers in the predictor variables space.
A vector with number indicating which vectors are potential outliers in the residuals and in the predictor variables space.
The predicted values if xnew is not NULL.
The classical multivariate linear regression model is obtained.
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
# NOT RUN { library(MASS) x <- as.matrix(iris[, 1:2]) y <- as.matrix(iris[, 3:4]) multivreg(y, x, plot = TRUE) # }
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