R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- c(2.4,2.4,2.5,2.6,2.4,2.6,2.4,2.3,2.4,2.4,2.4,2.4,2.4,2.4,2.4,2.4,2.5,2.1,2.1,2,2,2,1.9,1.9,2,1.8,1.6,1.3,1.4,1.4,1.5,1.7,1.6,1.5,1.6,1.5,1.1,1.1,1.1,1.4,1.3,1.4,1.3,1.1,1,0.9,0.8,0.8,0.8,0.8,1,1.1,1,0.9,1.1,1.2,1.2,1.4,1.5,1.7,1.9,1.9,1.9,1.7,1.7,2.1,2,2,2.5,2.4,2.5,2.5,2,1.9,2.2,2.7,3.1,2.8,2.6,2.3,2.2,2.2,2,2,2.6,2.5,2.5,2.3,2,1.9,2,2.1,2.1,2.3,2.3,2.3,2.1,2.4,2.5,2.1,1.8,1.9,1.9,2.1,2.2,2,2.2,2,1.9,1.6,1.7,2,2.5,2.4,2.3,2.3,2.1,2.4,2.2,2.4,1.9,2.1,2.1,2.1,2,2.1,2.2,2.2,2.6,2.5,2.3,2.2,2.4,2.3,2.2,2.5,2.5,2.5,2.4,2.3,1.7,1.6,1.9,1.9,1.8,1.8,1.9,1.9,1.9,1.9,1.8,1.7,2.1,2.6,3.1,3.1,3.2,3.3,3.6,3.3,3.7,4,4,3.8,3.6,3.2,2.1,1.6,1.1,1.2,0.6,0.6,0,-0.1,-0.6,-0.2,-0.3,-0.1,0.5,0.9)
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = ''
> par2 = ''
> par1 = ''
> main = 'Robustness of Central Tendency'
> 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] 1.962778
> sqrtn <- sqrt(length(x))
> (armse <- sd(x) / sqrtn)
[1] 0.05629261
> (armose <- arm / armse)
[1] 34.86741
> (geo <- geomean(x))
[1] NaN
Warning message:
In log(x) : NaNs produced
> (har <- harmean(x))
[1] 0
> (qua <- quamean(x))
[1] 2.102314
> (win <- winmean(x))
          [,1]       [,2]
 [1,] 1.964444 0.05589197
 [2,] 1.963333 0.05521191
 [3,] 1.963333 0.05455025
 [4,] 1.961111 0.05416478
 [5,] 1.963889 0.05358509
 [6,] 1.970556 0.04884575
 [7,] 1.974444 0.04820890
 [8,] 1.970000 0.04754625
 [9,] 1.980000 0.04602377
[10,] 1.974444 0.04522598
[11,] 1.974444 0.04522598
[12,] 1.974444 0.04522598
[13,] 1.960000 0.04144054
[14,] 1.952222 0.04059960
[15,] 1.943889 0.03978655
[16,] 1.952778 0.03852036
[17,] 1.952778 0.03852036
[18,] 1.952778 0.03852036
[19,] 1.963333 0.03710421
[20,] 1.963333 0.03710421
[21,] 1.951667 0.03604841
[22,] 1.951667 0.03604841
[23,] 1.951667 0.03604841
[24,] 1.951667 0.03604841
[25,] 1.951667 0.03604841
[26,] 1.966111 0.03418978
[27,] 1.966111 0.03418978
[28,] 1.966111 0.03418978
[29,] 1.982222 0.03222704
[30,] 1.982222 0.03222704
[31,] 1.982222 0.03222704
[32,] 2.000000 0.03018667
[33,] 2.000000 0.03018667
[34,] 1.981111 0.02853542
[35,] 1.981111 0.02853542
[36,] 1.981111 0.02853542
[37,] 2.001667 0.02626649
[38,] 2.001667 0.02626649
[39,] 2.001667 0.02626649
[40,] 2.001667 0.02626649
[41,] 2.024444 0.02391821
[42,] 2.024444 0.02391821
[43,] 2.024444 0.02391821
[44,] 2.024444 0.02391821
[45,] 2.024444 0.02391821
[46,] 2.024444 0.02391821
[47,] 2.050556 0.02142498
[48,] 2.050556 0.02142498
[49,] 2.050556 0.02142498
[50,] 2.050556 0.02142498
[51,] 2.050556 0.02142498
[52,] 2.050556 0.02142498
[53,] 2.021111 0.01885929
[54,] 2.051111 0.01611737
[55,] 2.051111 0.01611737
[56,] 2.051111 0.01611737
[57,] 2.051111 0.01611737
[58,] 2.051111 0.01611737
[59,] 2.083889 0.01342066
[60,] 2.083889 0.01342066
> (tri <- trimean(x))
          [,1]       [,2]
 [1,] 1.965730 0.05385564
 [2,] 1.967045 0.05163208
 [3,] 1.968966 0.04959889
 [4,] 1.970930 0.04764787
 [5,] 1.973529 0.04563467
 [6,] 1.975595 0.04358226
 [7,] 1.976506 0.04245066
 [8,] 1.976829 0.04135225
 [9,] 1.977778 0.04028120
[10,] 1.977500 0.03937353
[11,] 1.977848 0.03851245
[12,] 1.978205 0.03757865
[13,] 1.978205 0.03656321
[14,] 1.980263 0.03594853
[15,] 1.982667 0.03537902
[16,] 1.985811 0.03484806
[17,] 1.988356 0.03441268
[18,] 1.990972 0.03393846
[19,] 1.993662 0.03342134
[20,] 1.995714 0.03300427
[21,] 1.997826 0.03254869
[22,] 2.000735 0.03214375
[23,] 2.003731 0.03169910
[24,] 2.006818 0.03121032
[25,] 2.010000 0.03067225
[26,] 2.010000 0.03007886
[27,] 2.015873 0.02960937
[28,] 2.018548 0.02909075
[29,] 2.021311 0.02851682
[30,] 2.023333 0.02807387
[31,] 2.025424 0.02758277
[32,] 2.027586 0.02703727
[33,] 2.028947 0.02662871
[34,] 2.030357 0.02617383
[35,] 2.032727 0.02580187
[36,] 2.035185 0.02538437
[37,] 2.037736 0.02491505
[38,] 2.039423 0.02461118
[39,] 2.041176 0.02426768
[40,] 2.043000 0.02387911
[41,] 2.044898 0.02343895
[42,] 2.045833 0.02317537
[43,] 2.046809 0.02287414
[44,] 2.047826 0.02252982
[45,] 2.048889 0.02213591
[46,] 2.050000 0.02168454
[47,] 2.051163 0.02116602
[48,] 2.051190 0.02084655
[49,] 2.051190 0.02047640
[50,] 2.051250 0.02004692
[51,] 2.051282 0.01954729
[52,] 2.051282 0.01896381
[53,] 2.051351 0.01827869
[54,] 2.052778 0.01779867
[55,] 2.052857 0.01758993
[56,] 2.052941 0.01733705
[57,] 2.053030 0.01703140
[58,] 2.053125 0.01666202
[59,] 2.053226 0.01621474
[60,] 2.051667 0.01603169
> (midr <- midrange(x))
[1] 1.7
> midm <- array(NA,dim=8)
> for (j in 1:8) midm[j] <- midmean(x,j)
> midm
[1] 2.068571 2.068571 2.068571 2.068571 2.068571 2.068571 2.068571 2.068571
> postscript(file="/var/wessaorg/rcomp/tmp/1l2xf1321396516.ps",horizontal=F,onefile=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))
Error: object 'ylimmin' not found
Execution halted