R version 2.7.1 (2008-06-23)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(5
+ ,5
+ ,6
+ ,6
+ ,6
+ ,6
+ ,6
+ ,6
+ ,6
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,7
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,8
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,9
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,11
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,12
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,13
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,14
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,15
+ ,16
+ ,16
+ ,16
+ ,16
+ ,17
+ ,17
+ ,18
+ ,18)
> ylimmax = ''
> ylimmin = ''
> main = 'Robustness of Central Tendency'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> geomean <- function(x) {
+ return(exp(mean(log(x))))
+ }
> harmean <- function(x) {
+ return(1/mean(1/x))
+ }
> quamean <- function(x) {
+ return(sqrt(mean(x*x)))
+ }
> winmean <- function(x) {
+ x <-sort(x[!is.na(x)])
+ n<-length(x)
+ denom <- 3
+ nodenom <- n/denom
+ if (nodenom>40) denom <- n/40
+ sqrtn = sqrt(n)
+ roundnodenom = floor(nodenom)
+ win <- array(NA,dim=c(roundnodenom,2))
+ for (j in 1:roundnodenom) {
+ win[j,1] <- (j*x[j+1]+sum(x[(j+1):(n-j)])+j*x[n-j])/n
+ win[j,2] <- sd(c(rep(x[j+1],j),x[(j+1):(n-j)],rep(x[n-j],j)))/sqrtn
+ }
+ return(win)
+ }
> trimean <- function(x) {
+ x <-sort(x[!is.na(x)])
+ n<-length(x)
+ denom <- 3
+ nodenom <- n/denom
+ if (nodenom>40) denom <- n/40
+ sqrtn = sqrt(n)
+ roundnodenom = floor(nodenom)
+ tri <- array(NA,dim=c(roundnodenom,2))
+ for (j in 1:roundnodenom) {
+ tri[j,1] <- mean(x,trim=j/n)
+ tri[j,2] <- sd(x[(j+1):(n-j)]) / sqrt(n-j*2)
+ }
+ return(tri)
+ }
> midrange <- function(x) {
+ return((max(x)+min(x))/2)
+ }
> 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]
+ }
+ }
+ }
+ }
> midmean <- function(x,def) {
+ x <-sort(x[!is.na(x)])
+ n<-length(x)
+ if (def==1) {
+ qvalue1 <- q1(x,n,0.25,i,f)
+ qvalue3 <- q1(x,n,0.75,i,f)
+ }
+ if (def==2) {
+ qvalue1 <- q2(x,n,0.25,i,f)
+ qvalue3 <- q2(x,n,0.75,i,f)
+ }
+ if (def==3) {
+ qvalue1 <- q3(x,n,0.25,i,f)
+ qvalue3 <- q3(x,n,0.75,i,f)
+ }
+ if (def==4) {
+ qvalue1 <- q4(x,n,0.25,i,f)
+ qvalue3 <- q4(x,n,0.75,i,f)
+ }
+ if (def==5) {
+ qvalue1 <- q5(x,n,0.25,i,f)
+ qvalue3 <- q5(x,n,0.75,i,f)
+ }
+ if (def==6) {
+ qvalue1 <- q6(x,n,0.25,i,f)
+ qvalue3 <- q6(x,n,0.75,i,f)
+ }
+ if (def==7) {
+ qvalue1 <- q7(x,n,0.25,i,f)
+ qvalue3 <- q7(x,n,0.75,i,f)
+ }
+ if (def==8) {
+ qvalue1 <- q8(x,n,0.25,i,f)
+ qvalue3 <- q8(x,n,0.75,i,f)
+ }
+ midm <- 0
+ myn <- 0
+ roundno4 <- round(n/4)
+ round3no4 <- round(3*n/4)
+ for (i in 1:n) {
+ if ((x[i]>=qvalue1) & (x[i]<=qvalue3)){
+ midm = midm + x[i]
+ myn = myn + 1
+ }
+ }
+ midm = midm / myn
+ return(midm)
+ }
> (arm <- mean(x))
[1] 10.74580
> sqrtn <- sqrt(length(x))
> (armse <- sd(x) / sqrtn)
[1] 0.06848215
> (armose <- arm / armse)
[1] 156.9139
> (geo <- geomean(x))
[1] 10.55826
> (har <- harmean(x))
[1] 10.36266
> (qua <- quamean(x))
[1] 10.92607
> (win <- winmean(x))
           [,1]       [,2]
  [1,] 10.74580 0.06848215
  [2,] 10.74580 0.06797582
  [3,] 10.74580 0.06797582
  [4,] 10.74101 0.06748646
  [5,] 10.74101 0.06748646
  [6,] 10.74101 0.06748646
  [7,] 10.74101 0.06748646
  [8,] 10.73141 0.06666865
  [9,] 10.74221 0.06584022
 [10,] 10.74221 0.06584022
 [11,] 10.74221 0.06584022
 [12,] 10.74221 0.06584022
 [13,] 10.74221 0.06584022
 [14,] 10.74221 0.06584022
 [15,] 10.74221 0.06584022
 [16,] 10.74221 0.06584022
 [17,] 10.74221 0.06584022
 [18,] 10.74221 0.06584022
 [19,] 10.74221 0.06584022
 [20,] 10.74221 0.06584022
 [21,] 10.74221 0.06584022
 [22,] 10.74221 0.06584022
 [23,] 10.74221 0.06584022
 [24,] 10.74221 0.06584022
 [25,] 10.74221 0.06584022
 [26,] 10.74221 0.06584022
 [27,] 10.74221 0.06584022
 [28,] 10.70863 0.06348759
 [29,] 10.70863 0.06348759
 [30,] 10.70863 0.06348759
 [31,] 10.70863 0.06348759
 [32,] 10.74700 0.06110088
 [33,] 10.74700 0.06110088
 [34,] 10.74700 0.06110088
 [35,] 10.74700 0.06110088
 [36,] 10.74700 0.06110088
 [37,] 10.74700 0.06110088
 [38,] 10.74700 0.06110088
 [39,] 10.74700 0.06110088
 [40,] 10.74700 0.06110088
 [41,] 10.74700 0.06110088
 [42,] 10.74700 0.06110088
 [43,] 10.74700 0.06110088
 [44,] 10.74700 0.06110088
 [45,] 10.74700 0.06110088
 [46,] 10.74700 0.06110088
 [47,] 10.74700 0.06110088
 [48,] 10.74700 0.06110088
 [49,] 10.74700 0.06110088
 [50,] 10.74700 0.06110088
 [51,] 10.74700 0.06110088
 [52,] 10.74700 0.06110088
 [53,] 10.74700 0.06110088
 [54,] 10.74700 0.06110088
 [55,] 10.74700 0.06110088
 [56,] 10.74700 0.06110088
 [57,] 10.74700 0.06110088
 [58,] 10.74700 0.06110088
 [59,] 10.74700 0.06110088
 [60,] 10.74700 0.06110088
 [61,] 10.74700 0.06110088
 [62,] 10.74700 0.06110088
 [63,] 10.74700 0.06110088
 [64,] 10.74700 0.06110088
 [65,] 10.74700 0.06110088
 [66,] 10.66787 0.05659256
 [67,] 10.66787 0.05659256
 [68,] 10.66787 0.05659256
 [69,] 10.66787 0.05659256
 [70,] 10.66787 0.05659256
 [71,] 10.66787 0.05659256
 [72,] 10.66787 0.05659256
 [73,] 10.66787 0.05659256
 [74,] 10.66787 0.05659256
 [75,] 10.66787 0.05659256
 [76,] 10.66787 0.05659256
 [77,] 10.66787 0.05659256
 [78,] 10.66787 0.05659256
 [79,] 10.66787 0.05659256
 [80,] 10.66787 0.05659256
 [81,] 10.66787 0.05659256
 [82,] 10.66787 0.05659256
 [83,] 10.66787 0.05659256
 [84,] 10.66787 0.05659256
 [85,] 10.66787 0.05659256
 [86,] 10.66787 0.05659256
 [87,] 10.66787 0.05659256
 [88,] 10.66787 0.05659256
 [89,] 10.66787 0.05659256
 [90,] 10.66787 0.05659256
 [91,] 10.66787 0.05659256
 [92,] 10.55755 0.05198905
 [93,] 10.55755 0.05198905
 [94,] 10.55755 0.05198905
 [95,] 10.55755 0.05198905
 [96,] 10.55755 0.05198905
 [97,] 10.55755 0.05198905
 [98,] 10.55755 0.05198905
 [99,] 10.55755 0.05198905
[100,] 10.55755 0.05198905
[101,] 10.55755 0.05198905
[102,] 10.55755 0.05198905
[103,] 10.55755 0.05198905
[104,] 10.55755 0.05198905
[105,] 10.55755 0.05198905
[106,] 10.55755 0.05198905
[107,] 10.55755 0.05198905
[108,] 10.55755 0.05198905
[109,] 10.55755 0.05198905
[110,] 10.55755 0.05198905
[111,] 10.55755 0.05198905
[112,] 10.55755 0.05198905
[113,] 10.55755 0.05198905
[114,] 10.55755 0.05198905
[115,] 10.55755 0.05198905
[116,] 10.55755 0.05198905
[117,] 10.55755 0.05198905
[118,] 10.55755 0.05198905
[119,] 10.55755 0.05198905
[120,] 10.55755 0.05198905
[121,] 10.55755 0.05198905
[122,] 10.55755 0.05198905
[123,] 10.55755 0.05198905
[124,] 10.55755 0.05198905
[125,] 10.55755 0.05198905
[126,] 10.55755 0.05198905
[127,] 10.55755 0.05198905
[128,] 10.55755 0.05198905
[129,] 10.55755 0.05198905
[130,] 10.71343 0.04363087
[131,] 10.71343 0.04363087
[132,] 10.71343 0.04363087
[133,] 10.71343 0.04363087
[134,] 10.71343 0.04363087
[135,] 10.71343 0.04363087
[136,] 10.71343 0.04363087
[137,] 10.71343 0.04363087
[138,] 10.71343 0.04363087
[139,] 10.71343 0.04363087
[140,] 10.71343 0.04363087
[141,] 10.71343 0.04363087
[142,] 10.71343 0.04363087
[143,] 10.71343 0.04363087
[144,] 10.71343 0.04363087
[145,] 10.71343 0.04363087
[146,] 10.71343 0.04363087
[147,] 10.71343 0.04363087
[148,] 10.71343 0.04363087
[149,] 10.71343 0.04363087
[150,] 10.71343 0.04363087
[151,] 10.71343 0.04363087
[152,] 10.71343 0.04363087
[153,] 10.71343 0.04363087
[154,] 10.71343 0.04363087
[155,] 10.71343 0.04363087
[156,] 10.71343 0.04363087
[157,] 10.71343 0.04363087
[158,] 10.71343 0.04363087
[159,] 10.71343 0.04363087
[160,] 10.71343 0.04363087
[161,] 10.71343 0.04363087
[162,] 10.71343 0.04363087
[163,] 10.71343 0.04363087
[164,] 10.71343 0.04363087
[165,] 10.71343 0.04363087
[166,] 10.71343 0.04363087
[167,] 10.71343 0.04363087
[168,] 10.71343 0.04363087
[169,] 10.71343 0.04363087
[170,] 10.71343 0.04363087
[171,] 10.71343 0.04363087
[172,] 10.71343 0.04363087
[173,] 10.71343 0.04363087
[174,] 10.71343 0.04363087
[175,] 10.71343 0.04363087
[176,] 10.71343 0.04363087
[177,] 10.71343 0.04363087
[178,] 10.71343 0.04363087
[179,] 10.71343 0.04363087
[180,] 10.71343 0.04363087
[181,] 10.71343 0.04363087
[182,] 10.71343 0.04363087
[183,] 10.71343 0.04363087
[184,] 10.71343 0.04363087
[185,] 10.71343 0.04363087
[186,] 10.71343 0.04363087
[187,] 10.71343 0.04363087
[188,] 10.71343 0.04363087
[189,] 10.71343 0.04363087
[190,] 10.71343 0.04363087
[191,] 10.71343 0.04363087
[192,] 10.71343 0.04363087
[193,] 10.71343 0.04363087
[194,] 10.71343 0.04363087
[195,] 10.71343 0.04363087
[196,] 10.71343 0.04363087
[197,] 10.71343 0.04363087
[198,] 10.95084 0.03382780
[199,] 10.95084 0.03382780
[200,] 10.95084 0.03382780
[201,] 10.95084 0.03382780
[202,] 10.95084 0.03382780
[203,] 10.95084 0.03382780
[204,] 10.95084 0.03382780
[205,] 10.95084 0.03382780
[206,] 10.95084 0.03382780
[207,] 10.95084 0.03382780
[208,] 10.95084 0.03382780
[209,] 10.95084 0.03382780
[210,] 10.95084 0.03382780
[211,] 10.95084 0.03382780
[212,] 10.95084 0.03382780
[213,] 10.95084 0.03382780
[214,] 10.95084 0.03382780
[215,] 10.95084 0.03382780
[216,] 10.95084 0.03382780
[217,] 10.95084 0.03382780
[218,] 10.95084 0.03382780
[219,] 10.95084 0.03382780
[220,] 10.95084 0.03382780
[221,] 10.95084 0.03382780
[222,] 10.95084 0.03382780
[223,] 10.95084 0.03382780
[224,] 10.95084 0.03382780
[225,] 10.95084 0.03382780
[226,] 10.95084 0.03382780
[227,] 10.95084 0.03382780
[228,] 10.95084 0.03382780
[229,] 10.95084 0.03382780
[230,] 10.95084 0.03382780
[231,] 10.95084 0.03382780
[232,] 10.95084 0.03382780
[233,] 10.95084 0.03382780
[234,] 10.95084 0.03382780
[235,] 10.95084 0.03382780
[236,] 10.95084 0.03382780
[237,] 10.95084 0.03382780
[238,] 10.95084 0.03382780
[239,] 10.95084 0.03382780
[240,] 10.95084 0.03382780
[241,] 10.95084 0.03382780
[242,] 10.95084 0.03382780
[243,] 10.95084 0.03382780
[244,] 10.95084 0.03382780
[245,] 10.95084 0.03382780
[246,] 10.95084 0.03382780
[247,] 10.95084 0.03382780
[248,] 10.95084 0.03382780
[249,] 10.95084 0.03382780
[250,] 10.95084 0.03382780
[251,] 10.95084 0.03382780
[252,] 10.95084 0.03382780
[253,] 10.95084 0.03382780
[254,] 10.95084 0.03382780
[255,] 10.95084 0.03382780
[256,] 10.95084 0.03382780
[257,] 10.95084 0.03382780
[258,] 10.95084 0.03382780
[259,] 10.95084 0.03382780
[260,] 10.95084 0.03382780
[261,] 10.95084 0.03382780
[262,] 10.95084 0.03382780
[263,] 10.95084 0.03382780
[264,] 10.95084 0.03382780
[265,] 10.95084 0.03382780
[266,] 10.95084 0.03382780
[267,] 10.95084 0.03382780
[268,] 10.95084 0.03382780
[269,] 10.95084 0.03382780
[270,] 10.95084 0.03382780
[271,] 10.95084 0.03382780
[272,] 10.95084 0.03382780
[273,] 10.95084 0.03382780
[274,] 10.95084 0.03382780
[275,] 10.95084 0.03382780
[276,] 10.95084 0.03382780
[277,] 10.95084 0.03382780
[278,] 10.95084 0.03382780
> (tri <- trimean(x))
           [,1]       [,2]
  [1,] 10.74580 0.06773866
  [2,] 10.74399 0.06697915
  [3,] 10.74034 0.06646717
  [4,] 10.74034 0.06594572
  [5,] 10.73786 0.06554430
  [6,] 10.73723 0.06513606
  [7,] 10.73659 0.06472080
  [8,] 10.73659 0.06429836
  [9,] 10.73652 0.06398059
 [10,] 10.73587 0.06375817
 [11,] 10.73522 0.06353236
 [12,] 10.73457 0.06330310
 [13,] 10.73391 0.06307032
 [14,] 10.73325 0.06283395
 [15,] 10.73259 0.06259391
 [16,] 10.73259 0.06235013
 [17,] 10.73125 0.06210254
 [18,] 10.73058 0.06185105
 [19,] 10.72990 0.06159557
 [20,] 10.72922 0.06133604
 [21,] 10.72854 0.06107235
 [22,] 10.72785 0.06080442
 [23,] 10.72716 0.06053215
 [24,] 10.72646 0.06025544
 [25,] 10.72577 0.05997421
 [26,] 10.72506 0.05968833
 [27,] 10.72436 0.05939772
 [28,] 10.72365 0.05910224
 [29,] 10.72423 0.05890849
 [30,] 10.72481 0.05871168
 [31,] 10.72539 0.05851175
 [32,] 10.72539 0.05830863
 [33,] 10.72526 0.05819644
 [34,] 10.72454 0.05808245
 [35,] 10.72382 0.05796662
 [36,] 10.72310 0.05784892
 [37,] 10.72237 0.05772932
 [38,] 10.72164 0.05760778
 [39,] 10.72090 0.05748428
 [40,] 10.72016 0.05735878
 [41,] 10.71941 0.05723123
 [42,] 10.71867 0.05710161
 [43,] 10.71791 0.05696988
 [44,] 10.71716 0.05683599
 [45,] 10.71640 0.05669991
 [46,] 10.71563 0.05656160
 [47,] 10.71486 0.05642102
 [48,] 10.71409 0.05627812
 [49,] 10.71332 0.05613286
 [50,] 10.71253 0.05598519
 [51,] 10.71175 0.05583507
 [52,] 10.71096 0.05568246
 [53,] 10.71096 0.05552730
 [54,] 10.70937 0.05536954
 [55,] 10.70856 0.05520913
 [56,] 10.70776 0.05504602
 [57,] 10.70694 0.05488016
 [58,] 10.70613 0.05471149
 [59,] 10.70531 0.05453995
 [60,] 10.70448 0.05436548
 [61,] 10.70365 0.05418803
 [62,] 10.70282 0.05400752
 [63,] 10.70198 0.05382391
 [64,] 10.70198 0.05363711
 [65,] 10.70028 0.05344706
 [66,] 10.69943 0.05325370
 [67,] 10.70000 0.05316471
 [68,] 10.70057 0.05307411
 [69,] 10.70115 0.05298185
 [70,] 10.70173 0.05288791
 [71,] 10.70231 0.05279226
 [72,] 10.70290 0.05269487
 [73,] 10.70349 0.05259569
 [74,] 10.70408 0.05249471
 [75,] 10.70468 0.05239189
 [76,] 10.70528 0.05228718
 [77,] 10.70588 0.05218057
 [78,] 10.70649 0.05207200
 [79,] 10.70710 0.05196144
 [80,] 10.70772 0.05184885
 [81,] 10.70833 0.05173420
 [82,] 10.70896 0.05161744
 [83,] 10.70958 0.05149854
 [84,] 10.71021 0.05137744
 [85,] 10.71084 0.05125410
 [86,] 10.71148 0.05112849
 [87,] 10.71212 0.05100054
 [88,] 10.71277 0.05087023
 [89,] 10.71341 0.05073748
 [90,] 10.71407 0.05060227
 [91,] 10.71472 0.05046453
 [92,] 10.71538 0.05032421
 [93,] 10.71759 0.05026594
 [94,] 10.71981 0.05020625
 [95,] 10.72205 0.05014509
 [96,] 10.72430 0.05008245
 [97,] 10.72656 0.05001830
 [98,] 10.72884 0.04995259
 [99,] 10.73113 0.04988531
[100,] 10.73344 0.04981642
[101,] 10.73576 0.04974588
[102,] 10.73810 0.04967367
[103,] 10.74045 0.04959974
[104,] 10.74281 0.04952406
[105,] 10.74519 0.04944660
[106,] 10.74519 0.04936731
[107,] 10.75000 0.04928617
[108,] 10.75243 0.04920311
[109,] 10.75487 0.04911812
[110,] 10.75733 0.04903114
[111,] 10.75980 0.04894213
[112,] 10.76230 0.04885104
[113,] 10.76230 0.04875784
[114,] 10.76733 0.04866247
[115,] 10.76987 0.04856489
[116,] 10.77243 0.04846504
[117,] 10.77243 0.04836288
[118,] 10.77759 0.04825834
[119,] 10.78020 0.04815139
[120,] 10.78283 0.04804195
[121,] 10.78283 0.04792998
[122,] 10.78814 0.04781542
[123,] 10.79082 0.04769819
[124,] 10.79352 0.04757824
[125,] 10.79623 0.04745551
[126,] 10.79897 0.04732993
[127,] 10.80172 0.04720142
[128,] 10.80172 0.04706992
[129,] 10.80729 0.04693535
[130,] 10.81010 0.04679763
[131,] 10.81119 0.04680817
[132,] 10.81228 0.04681823
[133,] 10.81338 0.04682780
[134,] 10.81449 0.04683688
[135,] 10.81560 0.04684545
[136,] 10.81673 0.04685349
[137,] 10.81786 0.04686101
[138,] 10.81900 0.04686797
[139,] 10.82014 0.04687438
[140,] 10.82130 0.04688022
[141,] 10.82246 0.04688547
[142,] 10.82364 0.04689013
[143,] 10.82482 0.04689417
[144,] 10.82601 0.04689759
[145,] 10.82721 0.04690037
[146,] 10.82841 0.04690249
[147,] 10.82963 0.04690394
[148,] 10.83086 0.04690470
[149,] 10.83209 0.04690475
[150,] 10.83333 0.04690409
[151,] 10.83459 0.04690269
[152,] 10.83585 0.04690054
[153,] 10.83712 0.04689761
[154,] 10.83840 0.04689390
[155,] 10.83969 0.04688937
[156,] 10.84100 0.04688402
[157,] 10.84231 0.04687781
[158,] 10.84363 0.04687074
[159,] 10.84496 0.04686278
[160,] 10.84630 0.04685390
[161,] 10.84766 0.04684409
[162,] 10.84902 0.04683333
[163,] 10.85039 0.04682158
[164,] 10.85178 0.04680884
[165,] 10.85317 0.04679506
[166,] 10.85458 0.04678023
[167,] 10.85600 0.04676432
[168,] 10.85743 0.04674731
[169,] 10.85887 0.04672916
[170,] 10.86032 0.04670985
[171,] 10.86179 0.04668935
[172,] 10.86327 0.04666763
[173,] 10.86475 0.04664465
[174,] 10.86626 0.04662040
[175,] 10.86777 0.04659483
[176,] 10.86929 0.04656791
[177,] 10.87083 0.04653960
[178,] 10.87238 0.04650988
[179,] 10.87395 0.04647870
[180,] 10.87553 0.04644603
[181,] 10.87712 0.04641183
[182,] 10.87872 0.04637605
[183,] 10.88034 0.04633866
[184,] 10.88197 0.04629962
[185,] 10.88362 0.04625888
[186,] 10.88528 0.04621639
[187,] 10.88696 0.04617211
[188,] 10.88865 0.04612599
[189,] 10.89035 0.04607799
[190,] 10.89207 0.04602804
[191,] 10.89381 0.04597610
[192,] 10.89556 0.04592211
[193,] 10.89732 0.04586601
[194,] 10.89910 0.04580776
[195,] 10.90090 0.04574728
[196,] 10.90271 0.04568451
[197,] 10.90455 0.04561939
[198,] 10.90639 0.04555186
[199,] 10.90596 0.04564467
[200,] 10.90553 0.04573803
[201,] 10.90509 0.04583194
[202,] 10.90465 0.04592642
[203,] 10.90421 0.04602145
[204,] 10.90376 0.04611705
[205,] 10.90330 0.04621323
[206,] 10.90284 0.04630999
[207,] 10.90238 0.04640733
[208,] 10.90191 0.04650526
[209,] 10.90144 0.04660379
[210,] 10.90097 0.04670292
[211,] 10.90097 0.04680266
[212,] 10.90049 0.04690301
[213,] 10.89951 0.04700398
[214,] 10.89901 0.04710558
[215,] 10.89851 0.04720781
[216,] 10.89801 0.04731067
[217,] 10.89750 0.04741418
[218,] 10.89698 0.04751834
[219,] 10.89698 0.04762315
[220,] 10.89594 0.04772863
[221,] 10.89541 0.04783478
[222,] 10.89487 0.04794160
[223,] 10.89433 0.04804910
[224,] 10.89378 0.04815729
[225,] 10.89323 0.04826617
[226,] 10.89323 0.04837576
[227,] 10.89267 0.04848606
[228,] 10.89153 0.04859707
[229,] 10.89096 0.04870881
[230,] 10.89037 0.04882127
[231,] 10.88978 0.04893448
[232,] 10.88919 0.04904842
[233,] 10.88859 0.04916312
[234,] 10.88859 0.04927858
[235,] 10.88736 0.04939481
[236,] 10.88674 0.04951181
[237,] 10.88611 0.04962959
[238,] 10.88547 0.04974816
[239,] 10.88483 0.04986753
[240,] 10.88418 0.04998771
[241,] 10.88418 0.05010870
[242,] 10.88352 0.05023051
[243,] 10.88218 0.05035315
[244,] 10.88150 0.05047663
[245,] 10.88081 0.05060095
[246,] 10.88012 0.05072613
[247,] 10.87941 0.05085218
[248,] 10.87870 0.05097909
[249,] 10.87870 0.05110689
[250,] 10.87725 0.05123557
[251,] 10.87651 0.05136515
[252,] 10.87576 0.05149564
[253,] 10.87500 0.05162705
[254,] 10.87423 0.05175938
[255,] 10.87346 0.05189264
[256,] 10.87346 0.05202685
[257,] 10.87187 0.05216200
[258,] 10.87107 0.05229812
[259,] 10.87025 0.05243521
[260,] 10.86943 0.05257328
[261,] 10.86859 0.05271234
[262,] 10.86774 0.05285239
[263,] 10.86688 0.05299345
[264,] 10.86601 0.05313554
[265,] 10.86513 0.05327864
[266,] 10.86424 0.05342279
[267,] 10.86333 0.05356798
[268,] 10.86242 0.05371422
[269,] 10.86149 0.05386153
[270,] 10.86054 0.05400991
[271,] 10.85959 0.05415938
[272,] 10.85862 0.05430994
[273,] 10.85764 0.05446161
[274,] 10.85664 0.05461438
[275,] 10.85563 0.05476828
[276,] 10.85461 0.05492331
[277,] 10.85357 0.05507947
[278,] 10.85252 0.05523679
> (midr <- midrange(x))
[1] 11.5
> midm <- array(NA,dim=8)
> for (j in 1:8) midm[j] <- midmean(x,j)
> midm
[1] 11.11949 11.11949 11.11949 11.11949 11.11949 11.11949 11.11949 11.11949
> postscript(file="/var/www/html/rcomp/tmp/167xi1218458258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> lb <- win[,1] - 2*win[,2]
> ub <- win[,1] + 2*win[,2]
> if ((ylimmin == '') | (ylimmax == '')) plot(win[,1],type='b',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(win[,1],type='l',main=main, xlab='j', pch=19, ylab='Winsorized Mean(j/n)', ylim=c(ylimmin,ylimmax))
> lines(ub,lty=3)
> lines(lb,lty=3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2asls1218458258.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> lb <- tri[,1] - 2*tri[,2]
> ub <- tri[,1] + 2*tri[,2]
> if ((ylimmin == '') | (ylimmax == '')) plot(tri[,1],type='b',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(min(lb),max(ub))) else plot(tri[,1],type='l',main=main, xlab='j', pch=19, ylab='Trimmed Mean(j/n)', ylim=c(ylimmin,ylimmax))
> lines(ub,lty=3)
> lines(lb,lty=3)
> 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,'Central Tendency - Ungrouped Data',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Measure',header=TRUE)
> a<-table.element(a,'Value',header=TRUE)
> a<-table.element(a,'S.E.',header=TRUE)
> a<-table.element(a,'Value/S.E.',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', 'Arithmetic Mean', 'click to view the definition of the Arithmetic Mean'),header=TRUE)
> a<-table.element(a,arm)
> a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean_standard_error.htm', armse, 'click to view the definition of the Standard Error of the Arithmetic Mean'))
> a<-table.element(a,armose)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/geometric_mean.htm', 'Geometric Mean', 'click to view the definition of the Geometric Mean'),header=TRUE)
> a<-table.element(a,geo)
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/harmonic_mean.htm', 'Harmonic Mean', 'click to view the definition of the Harmonic Mean'),header=TRUE)
> a<-table.element(a,har)
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/quadratic_mean.htm', 'Quadratic Mean', 'click to view the definition of the Quadratic Mean'),header=TRUE)
> a<-table.element(a,qua)
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> for (j in 1:length(win[,1])) {
+ a<-table.row.start(a)
+ mylabel <- paste('Winsorized Mean (',j)
+ mylabel <- paste(mylabel,'/')
+ mylabel <- paste(mylabel,length(win[,1]))
+ mylabel <- paste(mylabel,')')
+ a<-table.element(a,hyperlink('http://www.xycoon.com/winsorized_mean.htm', mylabel, 'click to view the definition of the Winsorized Mean'),header=TRUE)
+ a<-table.element(a,win[j,1])
+ a<-table.element(a,win[j,2])
+ a<-table.element(a,win[j,1]/win[j,2])
+ a<-table.row.end(a)
+ }
> for (j in 1:length(tri[,1])) {
+ a<-table.row.start(a)
+ mylabel <- paste('Trimmed Mean (',j)
+ mylabel <- paste(mylabel,'/')
+ mylabel <- paste(mylabel,length(tri[,1]))
+ mylabel <- paste(mylabel,')')
+ a<-table.element(a,hyperlink('http://www.xycoon.com/arithmetic_mean.htm', mylabel, 'click to view the definition of the Trimmed Mean'),header=TRUE)
+ a<-table.element(a,tri[j,1])
+ a<-table.element(a,tri[j,2])
+ a<-table.element(a,tri[j,1]/tri[j,2])
+ a<-table.row.end(a)
+ }
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/median_1.htm', 'Median', 'click to view the definition of the Median'),header=TRUE)
> a<-table.element(a,median(x))
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/midrange.htm', 'Midrange', 'click to view the definition of the Midrange'),header=TRUE)
> a<-table.element(a,midr)
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_1.htm','Weighted Average at Xnp',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[1])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_2.htm','Weighted Average at X(n+1)p',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[2])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_3.htm','Empirical Distribution Function',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[3])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_4.htm','Empirical Distribution Function - Averaging',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[4])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_5.htm','Empirical Distribution Function - Interpolation',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[5])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_6.htm','Closest Observation',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[6])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_7.htm','True Basic - Statistics Graphics Toolkit',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[7])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> mymid <- hyperlink('http://www.xycoon.com/midmean.htm', 'Midmean', 'click to view the definition of the Midmean')
> mylabel <- paste(mymid,hyperlink('http://www.xycoon.com/method_8.htm','MS Excel (old versions)',''),sep=' - ')
> a<-table.element(a,mylabel,header=TRUE)
> a<-table.element(a,midm[8])
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Number of observations',header=TRUE)
> a<-table.element(a,length(x))
> a<-table.element(a,'')
> a<-table.element(a,'')
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/3wx4n1218458259.tab") 
> 
> system("convert tmp/167xi1218458258.ps tmp/167xi1218458258.png")
> system("convert tmp/2asls1218458258.ps tmp/2asls1218458258.png")
> 
> 
> proc.time()
   user  system elapsed 
  9.731   0.440   9.941