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("gpls-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('gpls')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "glpls1a"
> 
> ### * glpls1a
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glpls1a
> ### Title: Fit IRWPLS and IRWPLSF model
> ### Aliases: glpls1a
> ### Keywords: regression
> 
> ### ** Examples
> 
>  x <- matrix(rnorm(20),ncol=2)
>  y <- sample(0:1,10,TRUE)
>  ## no bias reduction
>  glpls1a(x,y,br=FALSE)

Call:
NULL

Coefficients:
Intercept        X:1        X:2  
    2.973     -2.878     -1.259  
>   
>  ## no bias reduction and 1 PLS component
>  glpls1a(x,y,K.prov=1,br=FALSE)

Call:
NULL

Coefficients:
Intercept        X:1        X:2  
    2.809     -2.486     -1.363  
> 
>  ## bias reduction
>  glpls1a(x,y,br=TRUE)

Call:
NULL

Coefficients:
Intercept        X:1        X:2  
   1.4564    -1.1928    -0.4512  
> 
> 
> 
> cleanEx(); ..nameEx <- "glpls1a.cv.error"
> 
> ### * glpls1a.cv.error
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glpls1a.cv.error
> ### Title: Leave-one-out cross-validation error using IRWPLS and IRWPLSF
> ###   model
> ### Aliases: glpls1a.cv.error
> ### Keywords: regression
> 
> ### ** Examples
> 
>  x <- matrix(rnorm(20),ncol=2)
>  y <- sample(0:1,10,TRUE)
> 
>  ## no bias reduction
>  glpls1a.cv.error(x,y,br=FALSE)
$error
[1] 0.4

$error.obs
[1] 4 7 8 9

>  ## bias reduction and 1 PLS component
>  glpls1a.cv.error(x,y,K.prov=1, br=TRUE)
$error
[1] 0.3

$error.obs
[1] 4 7 8

> 
> 
> 
> cleanEx(); ..nameEx <- "glpls1a.logit.all"
> 
> ### * glpls1a.logit.all
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glpls1a.logit.all
> ### Title: Fit MIRWPLS and MIRWPLSF model separately for logits
> ### Aliases: glpls1a.logit.all
> ### Keywords: regression
> 
> ### ** Examples
> 
>  x <- matrix(rnorm(20),ncol=2)
>  y <- sample(1:3,10,TRUE)
>  ## no bias reduction
>  glpls1a.logit.all(x,y,br=FALSE)
$coefficients
          [,1]       [,2]
[1,] -1.599558   3.144428
[2,]  5.063456 -22.248319
[3,]  2.087350  21.477995

>  ## bias reduction
>  glpls1a.logit.all(x,y,br=TRUE)
$coefficients
           [,1]       [,2]
[1,] -0.2469014  0.3882321
[2,]  1.5438948 -2.2676992
[3,]  0.6578717  2.0499759

> 
> 
> 
> cleanEx(); ..nameEx <- "glpls1a.mlogit"
> 
> ### * glpls1a.mlogit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glpls1a.mlogit
> ### Title: Fit MIRWPLS and MIRWPLSF model
> ### Aliases: glpls1a.mlogit
> ### Keywords: regression
> 
> ### ** Examples
> 
>  x <- matrix(rnorm(20),ncol=2)
>  y <- sample(1:3,10,TRUE)
>  ## no bias reduction and 1 PLS component
>  glpls1a.mlogit(cbind(rep(1,10),x),y,K.prov=1,br=FALSE)
$coefficients
          [,1]       [,2]
[1,] 0.9260784  0.8217932
[2,] 1.5412616 -1.5312377
[3,] 0.2589442  0.3241784

$convergence
[1] FALSE

$niter
[1] 100

$bias.reduction
[1] FALSE

>  ## bias reduction
>  glpls1a.mlogit(cbind(rep(1,10),x),y,br=TRUE)
$coefficients
          [,1]       [,2]
[1,] 0.2940383  0.9193367
[2,] 0.9557165 -0.9675655
[3,] 0.4721678  0.1683686

$convergence
[1] TRUE

$niter
[1] 12

$bias.reduction
[1] TRUE

> 
> 
> 
> cleanEx(); ..nameEx <- "glpls1a.mlogit.cv.error"
> 
> ### * glpls1a.mlogit.cv.error
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glpls1a.mlogit.cv.error
> ### Title: Leave-one-out cross-validation error using MIRWPLS and MIRWPLSF
> ###   model
> ### Aliases: glpls1a.mlogit.cv.error
> ### Keywords: regression
> 
> ### ** Examples
> 
>  x <- matrix(rnorm(20),ncol=2)
>  y <- sample(1:3,10,TRUE)
> 
>  ## no bias reduction
>  glpls1a.mlogit.cv.error(x,y,br=FALSE)
$error
[1] 0.5

$error.obs
[1] 2 5 7 8 9

>  glpls1a.mlogit.cv.error(x,y,mlogit=FALSE,br=FALSE)
$error
[1] 0.5

$error.obs
[1] 2 4 5 7 9

>  ## bias reduction
>  glpls1a.mlogit.cv.error(x,y,br=TRUE)
$error
[1] 0.5

$error.obs
[1] 2 5 7 8 9

>  glpls1a.mlogit.cv.error(x,y,mlogit=FALSE,br=TRUE)
$error
[1] 0.6

$error.obs
[1] 2 3 4 5 7 9

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "glpls1a.train.test.error"
> 
> ### * glpls1a.train.test.error
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glpls1a.train.test.error
> ### Title: out-of-sample test set error using IRWPLS and IRWPLSF model
> ### Aliases: glpls1a.train.test.error
> ### Keywords: regression
> 
> ### ** Examples
> 
>  x <- matrix(rnorm(20),ncol=2)
>  y <- sample(0:1,10,TRUE)
>  x1 <- matrix(rnorm(10),ncol=2)
>  y1 <- sample(0:1,5,TRUE)
> 
>  ## no bias reduction
>  glpls1a.train.test.error(x,y,x1,y1,br=FALSE)
$error
[1] 0.8

$error.obs
[1] 1 3 4 5

$predict.test
          [,1]
[1,] 0.8058771
[2,] 0.9720929
[3,] 0.9987910
[4,] 0.9880673
[5,] 0.9708083

>  ## bias reduction
>  glpls1a.train.test.error(x,y,x1,y1,br=TRUE)
$error
[1] 0.8

$error.obs
[1] 1 3 4 5

$predict.test
          [,1]
[1,] 0.7130766
[2,] 0.8440395
[3,] 0.9541642
[4,] 0.8860480
[5,] 0.8290839

> 
> 
> 
> cleanEx(); ..nameEx <- "gpls"
> 
> ### * gpls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gpls
> ### Title: A function to fit Generalized partial least squares models.
> ### Aliases: gpls print.gpls gpls.formula gpls.default
> ### Keywords: classif
> 
> ### ** Examples
> 
> library(MASS)
> m1 = gpls(type~., data=Pima.tr, K=3)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.gpls"
> 
> ### * predict.gpls
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.gpls
> ### Title: A prediction method for gpls.
> ### Aliases: predict.gpls
> ### Keywords: classif
> 
> ### ** Examples
> 
>   example(gpls)

gpls> library(MASS)

gpls> m1 = gpls(type ~ ., data = Pima.tr, K = 3)
>   p1 = predict(m1)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  33.75 0.4 35.39 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')