predict.prm: Predict method for models of class prm
In sprm: Sparse and Non-Sparse Partial Robust M Regression and Classification

Description Usage Arguments Details Value Author(s) References See Also Examples

Predictions from a partial robust M regression model.

1 2	## S3 method for class 'prm' predict(object, newdata, ...)

`object`	object of class prm.
`newdata`	optional data frame with new observations.
`...`	further arguments. Currently not used.

If newdata is specified the prm model is used to predict the fitted values for this data set, otherwise the fitted values of the model are returned.

predict.prm returns a vector of the predicted response.

Sven Serneels, BASF Corp and Irene Hoffmann

Hoffmann, I., Serneels, S., Filzmoser, P., Croux, C. (2015). Sparse partial robust M regression. Chemometrics and Intelligent Laboratory Systems, 149, 50-59.

Serneels, S., Croux, C., Filzmoser, P., Van Espen, P.J. (2005). Partial Robust M-Regression. Chemometrics and Intelligent Laboratory Systems, 79, 55-64.

prms, prmsCV

set.seed(5023)
U <- c(rep(2,20), rep(5,30))
X <- replicate(6, U+rnorm(50))
beta <- c(rep(1, 3), rep(-1,3))
e <- c(rnorm(45,0,1.5),rnorm(5,-20,1))
y <- X%*%beta + e
d <- as.data.frame(X)
d$y <- y
mod <- prms(y~., data=d, a=2, fun="Hampel")

dnew <- as.data.frame(replicate(6, U+rnorm(10)))
ynewp <- predict(mod, newdata=dnew)