summary.dbplsr {dbstats}

Summarizing distance-based partial least squares fits

Description

summary method for class "dbplsr"

Usage

## S3 method for class 'dbplsr'
summary(object,...)

Arguments

object

an object of class dbplsr. Result of dbplsr.

...

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

Value

A list of class summary.dbplsr containing the following components:

ncomp

the number of components of the model.

r.squared

the coefficient of determination R2.

adj.r.squared

adjusted R-squared.

call

the matched call.

residuals

a list containing the residuals for each iteration (response minus fitted values).

sigma

the residual standard error.

gvar

total weighted geometric variability.

gvec

the diagonal entries in G0.

gvar.iter

geometric variability for each iteration.

method

the using method to set ncomp.

crit.value

value of criterion defined in method.

ncomp.opt

optimum number of components according to the selected method.

Author(s)

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)
library(pls)
data(yarn)
## Default methods:
yarn.dbplsr <- dbplsr(density ~ NIR, data = yarn, ncomp=6, method="GCV")
summary(yarn.dbplsr)