summary method for class "dbplsr"
# S3 method for dbplsr
summary(object,...)A list of class summary.dbplsr containing the following components:
the number of components of the model.
the coefficient of determination R2.
adjusted R-squared.
the matched call.
a list containing the residuals for each iteration (response minus fitted values).
the residual standard error.
total weighted geometric variability.
the diagonal entries in G0.
geometric variability for each iteration.
the using method to set ncomp.
value of criterion defined in method.
optimum number of components according to the selected method.
an object of class dbplsr. Result of dbplsr.
arguments passed to or from other methods to the low level.
Boj, Eva <evaboj@ub.edu>, Caballe, Adria <adria.caballe@upc.edu>, Delicado, Pedro <pedro.delicado@upc.edu> and Fortiana, Josep <fortiana@ub.edu>
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.
dbplsr for distance-based partial least squares.
# require(pls)
library(pls)
data(yarn)
## Default methods:
yarn.dbplsr <- dbplsr(density ~ NIR, data = yarn, ncomp=6, method="GCV")
summary(yarn.dbplsr)
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