R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(100.17,101.13,99.25,99.69,101.04,99.79,100.35,101.45,100.4,100.52,102.52,101.23,102.14,101.06,100.31,101.18,101.28,101.99,101.34,100.5,103.74,104.19,102.23,103.32,104.67,103.22,102.64,105.26,103.63,102.71,104.34,102.92,105.92,107.39,105.68,105.86,107.05,106.77,105.88,106.23,107.53,105.51,107.37,105.61,108.38,109.6,106.62,105.69,107.06,105.67,106.24,107.9,105.91,106.44,107.69,105.9,108.59,111.36,109.36,109.21,111.3,109.21,110.95,110.89,111.04,108.96,110.5,109.02,112.87,112.73,113.28,113.53,112.99,112.68,114.26,114.28,114.28,114.2,113.64,114.2,116.68,116.73,118.71,117.8)
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., (2012), Percentiles (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_percentiles.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> x <-sort(x[!is.na(x)])
> q1 <- function(data,n,p,i,f) {
+ np <- n*p;
+ i <<- floor(np)
+ f <<- np - i
+ qvalue <- (1-f)*data[i] + f*data[i+1]
+ }
> q2 <- function(data,n,p,i,f) {
+ np <- (n+1)*p
+ i <<- floor(np)
+ f <<- np - i
+ qvalue <- (1-f)*data[i] + f*data[i+1]
+ }
> q3 <- function(data,n,p,i,f) {
+ np <- n*p
+ i <<- floor(np)
+ f <<- np - i
+ if (f==0) {
+ qvalue <- data[i]
+ } else {
+ qvalue <- data[i+1]
+ }
+ }
> q4 <- function(data,n,p,i,f) {
+ np <- n*p
+ i <<- floor(np)
+ f <<- np - i
+ if (f==0) {
+ qvalue <- (data[i]+data[i+1])/2
+ } else {
+ qvalue <- data[i+1]
+ }
+ }
> q5 <- function(data,n,p,i,f) {
+ np <- (n-1)*p
+ i <<- floor(np)
+ f <<- np - i
+ if (f==0) {
+ qvalue <- data[i+1]
+ } else {
+ qvalue <- data[i+1] + f*(data[i+2]-data[i+1])
+ }
+ }
> q6 <- function(data,n,p,i,f) {
+ np <- n*p+0.5
+ i <<- floor(np)
+ f <<- np - i
+ qvalue <- data[i]
+ }
> q7 <- function(data,n,p,i,f) {
+ np <- (n+1)*p
+ i <<- floor(np)
+ f <<- np - i
+ if (f==0) {
+ qvalue <- data[i]
+ } else {
+ qvalue <- f*data[i] + (1-f)*data[i+1]
+ }
+ }
> q8 <- function(data,n,p,i,f) {
+ np <- (n+1)*p
+ i <<- floor(np)
+ f <<- np - i
+ if (f==0) {
+ qvalue <- data[i]
+ } else {
+ if (f == 0.5) {
+ qvalue <- (data[i]+data[i+1])/2
+ } else {
+ if (f < 0.5) {
+ qvalue <- data[i]
+ } else {
+ qvalue <- data[i+1]
+ }
+ }
+ }
+ }
> lx <- length(x)
> qval <- array(NA,dim=c(99,8))
> mystep <- 25
> mystart <- 25
> if (lx>10){
+ mystep=10
+ mystart=10
+ }
> if (lx>20){
+ mystep=5
+ mystart=5
+ }
> if (lx>50){
+ mystep=2
+ mystart=2
+ }
> if (lx>=100){
+ mystep=1
+ mystart=1
+ }
> for (perc in seq(mystart,99,mystep)) {
+ qval[perc,1] <- q1(x,lx,perc/100,i,f)
+ qval[perc,2] <- q2(x,lx,perc/100,i,f)
+ qval[perc,3] <- q3(x,lx,perc/100,i,f)
+ qval[perc,4] <- q4(x,lx,perc/100,i,f)
+ qval[perc,5] <- q5(x,lx,perc/100,i,f)
+ qval[perc,6] <- q6(x,lx,perc/100,i,f)
+ qval[perc,7] <- q7(x,lx,perc/100,i,f)
+ qval[perc,8] <- q8(x,lx,perc/100,i,f)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/1fs3y1381261799.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> myqqnorm <- qqnorm(x,col=2)
> qqline(x)
> grid()
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Percentiles - Ungrouped Data',9,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p',1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_1.htm', 'Weighted Average at Xnp',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),1,TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),1,TRUE)
> a<-table.row.end(a)
> for (perc in seq(mystart,99,mystep)) {
+ a<-table.row.start(a)
+ a<-table.element(a,round(perc/100,2),1,TRUE)
+ for (j in 1:8) {
+ a<-table.element(a,round(qval[perc,j],6))
+ }
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/2ciq01381261799.tab") 
> 
> try(system("convert tmp/1fs3y1381261799.ps tmp/1fs3y1381261799.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.717   0.297   1.981