predict {integrOmics} | R Documentation |
Predicted values based on PLS regression or sparse PLS models. New responses and variates are predicted using a fitted model and a new matrix of observations.
## S3 method for class 'pls': predict(object, newdata, ...) ## S3 method for class 'spls': predict(object, newdata, ...)
object |
object of class inheriting from "pls" or "spls" . |
newdata |
data matrix in which to look for for explanatory variables to be used for prediction. |
... |
not used currently. |
predict
produces predicted values, obtained by evaluating the PLS
model returned by pls
or spls
in the frame newdata
.
Variates for newdata
are also returned.
predict
produces a list with the following components:
predict |
a three dimensional array of predicted response values. The dimensions correspond to the observations, the response variables and the model dimension, respectively. |
variates |
matrix of predicted variates. |
B.hat |
matrix of regression coefficients (without the intercept). |
Sébastien Déjean, Ignacio González and Kim-Anh Lê Cao
Tenenhaus, M. (1998). La régression PLS: théorie et pratique. Paris: Editions Technic.
data(linnerud) X <- linnerud$exercise Y <- linnerud$physiological linn.pls <- pls(X, Y, ncomp = 2, mode = "classic") indiv1 <- c(200, 40, 60) indiv2 <- c(190, 45, 45) newdata <- rbind(indiv1, indiv2) colnames(newdata) <- colnames(X) newdata pred <- predict(linn.pls, newdata) plotIndiv(linn.pls, 1, 2, rep.space = "X-variate") points(pred$variates[, 1], pred$variates[, 2], pch = 19, cex = 1.2) text(pred$variates[, 1], pred$variates[, 2], c("new ind.1", "new ind.2"), pos = 3)