R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(467037,460070,447988,442867,436087,431328,484015,509673,512927,502831,470984,471067,476049,474605,470439,461251,454724,455626,516847,525192,522975,518585,509239,512238,519164,517009,509933,509127,500857,506971,569323,579714,577992,565464,547344,554788,562325,560854,555332,543599,536662,542722,593530,610763,612613,611324,594167,595454,590865,589379,584428,573100,567456,569028,620735,628884,628232,612117,595404,597141,593408,590072,579799,574205,572775,572942,619567,625809,619916,587625,565742,557274,560576,548854,531673,525919,511038,498662,555362,564591,541657,527070,509846,514258)
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2007), Tukey lambda PPCC Plot (v1.0.1) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_tukeylambda.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description
> gp <- function(lambda, p)
+ {
+ (p^lambda-(1-p)^lambda)/lambda
+ }
> sortx <- sort(x)
> c <- array(NA,dim=c(201))
> for (i in 1:201)
+ {
+ if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1ne4w1229696170.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda')
> grid()
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Distribution (lambda)',1,TRUE)
> a<-table.element(a,'Correlation',1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE)
> a<-table.element(a,c[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE)
> a<-table.element(a,(c[100]+c[102])/2)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE)
> a<-table.element(a,c[115])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE)
> a<-table.element(a,c[151])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE)
> a<-table.element(a,c[201])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/216gu1229696170.tab") 
> 
> system("convert tmp/1ne4w1229696170.ps tmp/1ne4w1229696170.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.447   0.196   0.512