R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("pls.pcr-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('pls.pcr')

This package has been superseded by the package 
'pls', authored by Bjørn-Helge Mevik and Ron Wehrens. 
Package 'pls.pcr' will not be actively supported anymore, 
and will eventually be deprecated.

> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "kernelpls"
> 
> ### * kernelpls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: kernelpls
> ### Title: Kernel PLS (Dayal and MacGregor)
> ### Aliases: kernelpls
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(NIR)
> attach(NIR)
> NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validation="CV", method="kernelPLS")
> 
> 
> 
> cleanEx(); ..nameEx <- "mvr"
> 
> ### * mvr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mvr
> ### Title: Multivariate Regression
> ### Aliases: mvr pcr pls
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(NIR)
> attach(NIR)
> NIR.pcr <- mvr(Xtrain, Ytrain, 1:6, validation="CV")
> NIR.simpls <- mvr(Xtrain, Ytrain, 1:6, validation="CV", method="SIMPLS")
> NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validation="CV", method="kernelPLS")
> 
> data(sensory)
> Pn <- scale(sensory$Panel)
> Ql <- scale(sensory$Quality)
> sens.pcr <- mvr(Ql, Pn, 1:5)
> sens.simpls <- mvr(Ql, Pn, 1:5, method="SIMPLS")
> sens.kernelpls <- mvr(Ql, Pn, 1:5, method="kernelPLS")
> 
> 
> 
> cleanEx(); ..nameEx <- "plot.mvr"
> 
> ### * plot.mvr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.mvr
> ### Title: Plot a MVR object
> ### Aliases: plot.mvr
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(NIR)
> attach(NIR)
> nir.pcr <- pcr(Xtrain,Ytrain,1:9, validat="CV")
> ## Not run: plot(nir.pcr)
> plot(nir.pcr, "coefficients", nlv=3:6, type="l")
> ## Not run: plot(nir.pcr, "prediction", nlv=3:6)
> plot(nir.pcr, "prediction", nlv=3:6, which=2)
> 
> data(sensory)
> Pn <- scale(sensory$Panel)
> Ql <- scale(sensory$Quality)
> sens.simpls <- mvr(Ql, Pn, 1:5, method="SIMPLS")
> plot(sens.simpls, "loadings")
> plot(sens.simpls, "scores")
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.mvr"
> 
> ### * predict.mvr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.mvr
> ### Title: MVR predictions
> ### Aliases: predict.mvr
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(NIR)
> attach(NIR)
> nir.mvr <- mvr(Xtrain, Ytrain, 2)
> nir.predictions <- predict(nir.mvr, Xtest)
> 
> 
> 
> cleanEx(); ..nameEx <- "simpls"
> 
> ### * simpls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: simpls
> ### Title: De Jong's SIMPLS
> ### Aliases: simpls
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(NIR)
> attach(NIR)
> NIR.simpls <- mvr(Xtrain, Ytrain, 1:6, validation="CV", method="SIMPLS")
> 
> 
> 
> cleanEx(); ..nameEx <- "summary.mvr"
> 
> ### * summary.mvr
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: summary.mvr
> ### Title: Summary of a MVR object
> ### Aliases: summary.mvr
> ### Keywords: regression
> 
> ### ** Examples
> 
> data(NIR)
> attach(NIR)
> nir.mvr <- mvr(Xtrain, Ytrain, 2:8, valid="CV", grps=1)
> summary(nir.mvr)
Data: 	X dimension: 21 268 
	Y dimension: 21 1
Method: PCR
Number of latent variables considered: 2-8
Suggested number of latent variables: 8 (cross validation)


VALIDATION:
Y 
            RMS   sd(RMS)      Q^2
2 LV's   4.8361   1.08069   0.9737
3 LV's   2.7033   0.60446   0.9918
4 LV's   2.5618   0.57156   0.9928
5 LV's   1.7195   0.38243   0.9968
6 LV's   0.5782   0.12917   0.9996
7 LV's   0.6199   0.13840   0.9996
8 LV's   0.3394   0.07535   0.9999



TRAINING: % variance explained
              X        Y
2 LV's    98.78    98.21
3 LV's    99.51    99.47
4 LV's    99.74    99.77
5 LV's    99.89    99.95
6 LV's    99.98    99.99
7 LV's    99.99    99.99
8 LV's    99.99   100.00
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  1.64 0.18 1.85 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')