Predict Method for MBPLS

Prediction for the mbpls (MBPLS) model. New responses or scores are predicted using a fitted model and a data.frame or list containing matrices of observations.

Usage

# S3 method for class 'mbpls'
predict(
  object,
  newdata,
  ncomp = 1:object$ncomp,
  comps,
  type = c("response", "scores"),
  na.action = na.pass,
  ...
)

Arguments

object: an mvr object. The fitted model
newdata: a data frame. The new data. If missing, the training data is used.
ncomp, comps: vector of positive integers. The components to use in the prediction. See below.
type: character. Whether to predict scores or response values
na.action: function determining what should be done with missing values in newdata. The default is to predict NA. See na.omit for alternatives.
...: further arguments. Currently not used

Value

When type is "response", a three dimensional array of predicted response values is returned. The dimensions correspond to the observations, the response variables and the model sizes, respectively.

When type is "scores", a score matrix is returned.

Details

When type is "response" (default), predicted response values are returned. If comps is missing (or is NULL), predictions for length(ncomp) models with ncomp[1] components, ncomp[2] components, etc., are returned. Otherwise, predictions for a single model with the exact components in comps are returned. (Note that in both cases, the intercept is always included in the predictions. It can be removed by subtracting the Ymeans component of the fitted model.)

When type is "scores", predicted score values are returned for the components given in comps. If comps is missing or NULL, ncomps is used instead.

Note

A warning message like 'newdata' had 10 rows but variable(s) found have 106 rows means that not all variables were found in the newdata data frame. This (usually) happens if the formula contains terms like yarn$NIR. Do not use such terms; use the data argument instead. See mvr for details.

Author

Kristian Hovde Liland

Examples

data(potato)
mb <- mbpls(Sensory ~ Chemical+Compression, data=potato, ncomp = 5, subset = 1:26 <= 18)
testdata <- subset(potato, 1:26 > 18)

# Predict response
yhat <- predict(mb, newdata = testdata)

# Predict scores and plot
scores <- predict(mb, newdata = testdata, type = "scores")
scoreplot(mb)
points(scores[,1], scores[,2], col="red")
legend("topright", legend = c("training", "test"), col=1:2, pch = 1)