dbstats (version 2.0.2)

predict.dbplsr: Predicted values for a dbpls object

Description

predict.dbplsr returns the predicted values, obtained by evaluating the Distance-based partial least squares function in the new data (newdata). newdata can be the values of the explanatory variables of these new cases, the squared distances between these new individuals and the originals ones, or rows of new doubly weighted and centered inner products matrix G.

Usage

# S3 method for dbplsr
predict(object,newdata,type.var="Z",...)

Value

predict.dbplsr produces a vector of predictions for the k new individuals.

Arguments

object

an object of class dbplsr. Result of dbplsr.

newdata

data.frame or matrix which contains the values of Z (if type.var="Z". The squared distances between k new individuals and the original n individuals (only if type.var="D2"). Finally, the G inner products matrix (if type.var="G").

type.var

set de type of newdata. Can be "Z" if newdata contains the values of the explanatory variables, "D2" if contains the squared distances matrix or "G" if contains the inner products matrix.

...

arguments passed to or from other methods to the low level.

Author

Boj, Eva <[e-mail]>, Caballe, Adria <[e-mail]>, Delicado, Pedro <[e-mail]> and Fortiana, Josep <[e-mail]>

References

Boj E, Delicado P, Fortiana J (2010). Distance-based local linear regression for functional predictors. Computational Statistics and Data Analysis 54, 429-437.

Boj E, Grane A, Fortiana J, Claramunt MM (2007). Implementing PLS for distance-based regression: computational issues. Computational Statistics 22, 237-248.

Boj E, Grane A, Fortiana J, Claramunt MM (2007). Selection of predictors in distance-based regression. Communications in Statistics B - Simulation and Computation 36, 87-98.

Cuadras CM, Arenas C, Fortiana J (1996). Some computational aspects of a distance-based model for prediction. Communications in Statistics B - Simulation and Computation 25, 593-609.

Cuadras C, Arenas C (1990). A distance-based regression model for prediction with mixed data. Communications in Statistics A - Theory and Methods 19, 2261-2279.

Cuadras CM (1989). Distance analysis in discrimination and classification using both continuous and categorical variables. In: Y. Dodge (ed.), Statistical Data Analysis and Inference. Amsterdam, The Netherlands: North-Holland Publishing Co., pp. 459-473.

See Also

dbplsr for distance-based partial least squares.

Examples

#require(pls)
# prediction of new observations newdata
library(pls)
data(yarn)
## Default methods:
yarn.dbplsr <- dbplsr(density[1:27] ~ NIR[1:27,], data = yarn, ncomp=6, method="GCV")
pr_yarn_28 <- predict(yarn.dbplsr,newdata=t(as.matrix(yarn$NIR[28,])))
print(pr_yarn_28)
print(yarn$density[28])