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("shapes-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('shapes')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "bookstein2d"
> 
> ### * bookstein2d
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: bookstein2d
> ### Title: Bookstein's baseline registration for 2D data
> ### Aliases: bookstein2d
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(gorf.dat)
> data(gorm.dat)
> 
> bookf<-bookstein2d(gorf.dat)
> bookm<-bookstein2d(gorm.dat)
> 
> plotshapes(bookf$mshape,bookm$mshape,joinline=c(1,6,7,8,2,3,4,5,1))
> 
> 
> 
> cleanEx(); ..nameEx <- "digit3.dat"
> 
> ### * digit3.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: digit3.dat
> ### Title: Digit 3 data
> ### Aliases: digit3.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(digit3.dat)
> k<-dim(digit3.dat)[1]
> n<-dim(digit3.dat)[3]
> digit3.dat[,2,]<- -digit3.dat[,2,]+50
> plotshapes(digit3.dat,joinline=c(1:13))
> 
> 
> 
> cleanEx(); ..nameEx <- "gorf.dat"
> 
> ### * gorf.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gorf.dat
> ### Title: Female gorilla data
> ### Aliases: gorf.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(gorf.dat)
> plotshapes(gorf.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "gorm.dat"
> 
> ### * gorm.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gorm.dat
> ### Title: Male gorilla data
> ### Aliases: gorm.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(gorm.dat)
> plotshapes(gorm.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "macf.dat"
> 
> ### * macf.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: macf.dat
> ### Title: Female macaque data
> ### Aliases: macf.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(macf.dat)
> plotshapes(macf.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "macm.dat"
> 
> ### * macm.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: macm.dat
> ### Title: Male macaque data
> ### Aliases: macm.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(macm.dat)
> plotshapes(macm.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "plotshapes"
> 
> ### * plotshapes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plotshapes
> ### Title: Plot configurations
> ### Aliases: plotshapes
> ### Keywords: hplot multivariate
> 
> ### ** Examples
> 
> data(gorf.dat)
> data(gorm.dat)
> plotshapes(gorf.dat,gorm.dat,joinline=c(1,6,7,8,2,3,4,5,1))
> 
> data(macm.dat)
> data(macf.dat)
> plotshapes(macm.dat,macf.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "procGPA"
> 
> ### * procGPA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: procGPA
> ### Title: Generalised Procrustes analysis
> ### Aliases: procGPA
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> #2D example : female and male Gorillas (cf. Dryden and Mardia, 1998)
> 
> data(gorf.dat)
> data(gorm.dat)
> 
> plotshapes(gorf.dat,gorm.dat)
> n1<-dim(gorf.dat)[3]
> n2<-dim(gorm.dat)[3]
> k<-dim(gorf.dat)[1]
> m<-dim(gorf.dat)[2]
> gor.dat<-array(0,c(k,2,n1+n2))
> gor.dat[,,1:n1]<-gorf.dat
> gor.dat[,,(n1+1):(n1+n2)]<-gorm.dat
> 
> gor<-procGPA(gor.dat)
> shapepca(gor,type="r",mag=3)
> shapepca(gor,type="v",mag=3)
> 
> gor.gp<-c(rep("f",times=30),rep("m",times=29))
> x<-cbind(gor$size,gor$rho,gor$scores[,1:3])
> pairs(x,panel=function(x,y) text(x,y,gor.gp),
+    label=c("s","rho","score 1","score 2","score 3"))
> 
> ##########################################################
> #3D example
> 
> data(macm.dat)
> out<-procGPA(macm.dat,scale=FALSE)
> 
> par(mfrow=c(2,2))
> plot(out$rawscores[,1],out$rawscores[,2],xlab="PC1",ylab="PC2")
> title("PC scores")
> plot(out$rawscores[,2],out$rawscores[,3],xlab="PC2",ylab="PC3")
> plot(out$rawscores[,1],out$rawscores[,3],xlab="PC1",ylab="PC3")
> plot(out$size,out$rho,xlab="size",ylab="rho")
> title("Size versus shape distance")
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "procOPA"
> 
> ### * procOPA
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: procOPA
> ### Title: Ordinary Procrustes analysis
> ### Aliases: procOPA
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(digit3.dat)
> 
> A<-digit3.dat[,,1]
> B<-digit3.dat[,,2]
> ans<-procOPA(A,B) 
> plotshapes(A,B,joinline=1:13)
> plotshapes(ans$Ahat,ans$Bhat,joinline=1:13)
> 
> #Sooty Mangabey data
> data(sooty.dat)
> A<-sooty.dat[,,1]   #juvenile
> B<-sooty.dat[,,2]   #adult
> par(mfrow=c(1,3))
> par(pty="s")
> plot(A,xlim=c(-2000,3000),ylim=c(-2000,3000),xlab=" ",ylab=" ")
> lines(A[c(1:12,1),])
> points(B)
> lines(B[c(1:12,1),],lty=2)
> title("Juvenile (-------) Adult (- - - -)")
> #match B onto A
> out<-procOPA(A,B)
> #rotation angle
> print(atan2(out$R[1,2],out$R[1,1])*180/pi)
[1] -45.52717
> #scale
> print(out$s)
[1] 0.8745017
> plot(A,xlim=c(-2000,3000),ylim=c(-2000,3000),xlab=" ",ylab=" ")
> lines(A[c(1:12,1),])
> points(out$Bhat)
> lines(out$Bhat[c(1:12,1),],lty=2)
> title("Match adult onto juvenile")
> #match A onto B
> out<-procOPA(B,A)
> #rotation angle
> print(atan2(out$R[1,2],out$R[1,1])*180/pi)
[1] 45.52717
> #scale
> print(out$s)
[1] 1.130936
> plot(B,xlim=c(-2000,3000),ylim=c(-2000,3000),xlab=" ",ylab=" ")
> lines(B[c(1:12,1),],lty=2)
> points(out$Bhat)
> lines(out$Bhat[c(1:12,1),])
> title("Match juvenile onto adult")
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "qcet2.dat"
> 
> ### * qcet2.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qcet2.dat
> ### Title: Control T2 mouse vertabrae data
> ### Aliases: qcet2.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(qcet2.dat)
> plotshapes(qcet2.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "qlet2.dat"
> 
> ### * qlet2.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qlet2.dat
> ### Title: Large T2 mouse vertabrae data
> ### Aliases: qlet2.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(qlet2.dat)
> plotshapes(qlet2.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "qset2.dat"
> 
> ### * qset2.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: qset2.dat
> ### Title: Small T2 mouse vertabrae data
> ### Aliases: qset2.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(qset2.dat)
> plotshapes(qset2.dat)
> 
> 
> 
> cleanEx(); ..nameEx <- "riemdist"
> 
> ### * riemdist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: riemdist
> ### Title: Riemannian shape distance
> ### Aliases: riemdist
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(gorf.dat)
> data(gorm.dat)
> gorf<-procGPA(gorf.dat)
> gorm<-procGPA(gorm.dat)
> rho<-riemdist(gorf$mshape,gorm$mshape)
> cat("Riemannian distance between mean shapes is ",rho," \n")
Riemannian distance between mean shapes is  0.05866407  
> 
> 
> 
> cleanEx(); ..nameEx <- "shapepca"
> 
> ### * shapepca
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: shapepca
> ### Title: Principal components for shape
> ### Aliases: shapepca
> ### Keywords: hplot multivariate
> 
> ### ** Examples
> 
> #2d example
> data(gorf.dat)
> data(gorm.dat)
> 
> gorf<-procGPA(gorf.dat)
> gorm<-procGPA(gorm.dat)
> shapepca(gorf,type="r",mag=3)
> shapepca(gorf,type="v",mag=3)
> shapepca(gorm,type="r",mag=3)
> shapepca(gorm,type="v",mag=3)
> 
> #3D example
> #data(macm.dat)
> #out<-procGPA(macm.dat)
> #movie
> #shapepca(out,pcno=1)
> 
> 
> 
> cleanEx(); ..nameEx <- "sooty.dat"
> 
> ### * sooty.dat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sooty.dat
> ### Title: Sooty mangabey data
> ### Aliases: sooty.dat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(sooty.dat)
> plotshapes(sooty.dat,joinline=c(1:12,1))
> 
> 
> 
> cleanEx(); ..nameEx <- "testmeanshapes"
> 
> ### * testmeanshapes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: testmeanshapes
> ### Title: Tests for mean shape difference
> ### Aliases: testmeanshapes
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> 
> #2D example : female and male Gorillas (cf. Dryden and Mardia, 1998)
> 
> data(gorf.dat)
> data(gorm.dat)
> 
> #Hotelling's Tsq test
> test1<-testmeanshapes(gorf.dat,gorm.dat)
Hotelling's T^2 test:  Test statistic =  26.47 
 p-value =  0 Degrees of freedom =  12 46 
> 
> #Goodall's isotropic test
> test2<-testmeanshapes(gorf.dat,gorm.dat,Hotelling=FALSE)
Goodall's F test:  Test statistic =  22.29 
 p-value =  0 Degrees of freedom =  12 684 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "tpsgrid"
> 
> ### * tpsgrid
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: tpsgrid
> ### Title: Thin-plate spline transformation grids
> ### Aliases: tpsgrid
> ### Keywords: multivariate hplot
> 
> ### ** Examples
> 
> data(gorf.dat)
> data(gorm.dat)
> 
> #TPS grid with shape change exaggerated (2x)
> gorf<-procGPA(gorf.dat)
> gorm<-procGPA(gorm.dat)
> mag<-2
> TT<-gorf$mshape
> YY<-gorm$mshape
> par(mfrow=c(1,2))
> YY<-TT+(YY-TT)*mag
> tpsgrid(TT,YY,-0.6,-0.6,1.2,2,0.1,22) 
> title("TPS grid: Female mean (left) to Male mean (right)")   
> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  3.76 0.05 3.83 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')