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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("GenKern-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('GenKern')
Loading required package: KernSmooth
KernSmooth 2.22 installed
Copyright M. P. Wand 1997

Loading GenKern version 1.1
Copyright Lucy and Aykroyd 2000
last update October 2004
requires KernSmooth


Package GenKern installed

> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "KernSec"
> 
> ### * KernSec
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: KernSec
> ### Title: Univariate kernel density estimate
> ### Aliases: KernSec
> ### Keywords: distribution smooth
> 
> ### ** Examples
> 
> x <- c(2,4,6,8,10) 
> 
> z <- KernSec(x)                         # simplest invocation
> plot(z$xords, z$yden, type="l")
> 
> z <- KernSec(x, xbandwidth=2, range.x=c(0,8))
> plot(z$xords, z$yden, type="l")
> 
> # local bandwidths
> ords <- seq(from=0, to=14, length=100)
> bands <- x/15
> z <- KernSec(x, xbandwidth=bands, range.x=ords)
> plot(z$xords, z$yden, type="l")         # should plot a wriggly line
> 
> bands <- seq(from=1, to=4, length=100)  # improvise a pilot estimate
> z <- KernSec(x, xbandwidth=bands, range.x=ords)
> plot(z$xords, z$yden, type="l") 
> 
> 
> 
> cleanEx(); ..nameEx <- "KernSur"
> 
> ### * KernSur
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: KernSur
> ### Title: Bivariate kernel density estimation
> ### Aliases: KernSur
> ### Keywords: distribution smooth
> 
> ### ** Examples
> 
> x <- c(2,4,6,8,10)                         # make up some x-y data
> y <- x 
> 
> # calculate and plot a surface with zero correlation based on above data
> op <- KernSur(x,y, xgridsize=50, ygridsize=50, correlation=0, 
+         xbandwidth=1, ybandwidth=1, range.x=c(0,13), range.y=c(0,13))
> image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE)
> contour(op$xords, op$yords, op$zden, add=TRUE)
> box()
> 
> # re-calculate and re-plot the above using a 0.8 correlation
> op <- KernSur(x,y, xgridsize=50, ygridsize=50, correlation=0.8,
+          xbandwidth=1, ybandwidth=1, range.x=c(0,13), range.y=c(0,13))
> image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE)
> contour(op$xords, op$yords, op$zden, add=TRUE)
> box()
> 
> # calculate and plot a surface of the above data with an ascending
> # correlation and bandwidths and a vector of equally spaced ordinates
> bands <- c(1,1.1,1.2,1.3,1.0)
> cors <- c(0,-0.2,-0.4,-0.6, -0.7)
> rnge.x <- seq(from=0, to=13, length=100)
> 
> op <- KernSur(x,y, xgridsize=50, ygridsize=50, correlation=cors, 
+         xbandwidth=bands, ybandwidth=bands, range.x=rnge.x, range.y=c(0,13))
> image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE)
> contour(op$xords, op$yords, op$zden, add=TRUE)
> box()
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "nearest"
> 
> ### * nearest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: nearest
> ### Title: Index of a vector nearest in value to a supplied value
> ### Aliases: nearest
> ### Keywords: arith
> 
> ### ** Examples
> 
> # make up a vector
> x <- c(1,2,2,2,2,2,3,4,5,6,7,8,9,10)
> # conventional useage - xval within range should return 9
> nearest(x, 4.7)
[1] 9
> # xval - outside the range of x should return 14
> nearest(x, 12.7, outside=TRUE)
[1] 14
> # many 'nearest' values in x - should return - 2 3 4 5 6 
> nearest(x, 1.7)
[1] 2 3 4 5 6
> # make x[3] an NA
> x[3] <- NA
> # returns - 2 4 5 6 - by enabling na.rm
> nearest(x, 1.7, na.rm=TRUE)
[1] 2 4 5 6
> 
> 
> 
> cleanEx(); ..nameEx <- "per"
> 
> ### * per
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: per
> ### Title: Locate value for ith percentage point in a binned distribution
> ### Aliases: per
> ### Keywords: nonparametric
> 
> ### ** Examples
> 
> # make up some x-y data
> x <- seq(1,100)
> y <- dnorm(x, mean=40, sd=10)
> plot(x,y)                   
> # mark the median, 0.1 and 0.9 positions with vertical lines
> abline(v=per(y,x,0.5))
> abline(v=per(y,x,0.9))
> abline(v=per(y,x,0.1)) 
> # for a bimodal distribution which doesn't sum to one
> x <- c(1:5) 
> y <- c(2,3,4,3,4)
> per(y,x,0.5) # should return 3.25
[1] 3.25
> # change the previous example to bin extremes
> x <- c(1:6)
> per(y,x,0.5) # should return 3.75
[1] 3.75
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.39 0.03 0.43 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')