plot.dbplsr: Plots for a dbplsr object

Description

Four plots (selected by which) are available: plot of scores, response vs scores, R2 contribution in each component and the value of "OCV", "GCV", "AIC" or "BIC" vs the number of component chosen.

Usage

# S3 method for dbplsr
plot(x,which=c(1L:4L),main="",scores.comps=1:2,
        component=1,method=c("OCV","GCV","AIC","BIC"),...)

Arguments

x: an object of class dbplsr.
which: if a subset of the plots is required, specify a subset of the numbers 1:4.
main: an overall title for the plot. Only if one of the four plots is selected.
scores.comps: array containing the component scores crossed in the first plot (default the first two).
component: numeric value. Component vs response in the second plot (Default the first component).
method: choosen method "OCV", "GCV", "AIC" or "BIC" in the last plot.
...: other parameters to be passed through to plotting functions.

Author

Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>

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.

Belsley, D. A., Kuh, E. and Welsch, R. E. (1980). Regression Diagnostics. New York: Wiley.

Examples

Run this code

#require(pls)
library(pls)
data(yarn)
## Default methods:
yarn.dbplsr <- dbplsr(density ~ NIR, data = yarn, ncomp=6, method="GCV")
plot(yarn.dbplsr,scores.comps=1:3)