R version 2.8.0 (2008-10-20)
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(6
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+ ,88)
> ylimmax = ''
> ylimmin = ''
> main = 'Robuustheid - gefilterde datareeks'
> #'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] 11.08643
> sqrtn <- sqrt(length(x))
> (armse <- sd(x) / sqrtn)
[1] 0.1544176
> (armose <- arm / armse)
[1] 71.79515
> (geo <- geomean(x))
[1] 10.72675
> (har <- harmean(x))
[1] 10.4916
> (qua <- quamean(x))
[1] 11.94772
> (win <- winmean(x))
           [,1]       [,2]
  [1,] 11.06483 0.14253610
  [2,] 11.03601 0.12788660
  [3,] 11.01080 0.11739188
  [4,] 10.90516 0.08160666
  [5,] 10.85714 0.07173094
  [6,] 10.84274 0.06973591
  [7,] 10.84274 0.06973591
  [8,] 10.83313 0.06862431
  [9,] 10.83313 0.06862431
 [10,] 10.82113 0.06742099
 [11,] 10.82113 0.06742099
 [12,] 10.82113 0.06742099
 [13,] 10.82113 0.06742099
 [14,] 10.80432 0.06600167
 [15,] 10.80432 0.06600167
 [16,] 10.80432 0.06600167
 [17,] 10.80432 0.06600167
 [18,] 10.80432 0.06600167
 [19,] 10.80432 0.06600167
 [20,] 10.80432 0.06600167
 [21,] 10.80432 0.06600167
 [22,] 10.80432 0.06600167
 [23,] 10.80432 0.06600167
 [24,] 10.80432 0.06600167
 [25,] 10.83433 0.06416190
 [26,] 10.83433 0.06416190
 [27,] 10.83433 0.06416190
 [28,] 10.83433 0.06416190
 [29,] 10.83433 0.06416190
 [30,] 10.83433 0.06416190
 [31,] 10.83433 0.06416190
 [32,] 10.83433 0.06416190
 [33,] 10.83433 0.06416190
 [34,] 10.79352 0.06127876
 [35,] 10.79352 0.06127876
 [36,] 10.79352 0.06127876
 [37,] 10.79352 0.06127876
 [38,] 10.79352 0.06127876
 [39,] 10.79352 0.06127876
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 [44,] 10.79352 0.06127876
 [45,] 10.79352 0.06127876
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 [50,] 10.79352 0.06127876
 [51,] 10.79352 0.06127876
 [52,] 10.79352 0.06127876
 [53,] 10.79352 0.06127876
 [54,] 10.79352 0.06127876
 [55,] 10.79352 0.06127876
 [56,] 10.79352 0.06127876
 [57,] 10.79352 0.06127876
 [58,] 10.79352 0.06127876
 [59,] 10.79352 0.06127876
 [60,] 10.79352 0.06127876
 [61,] 10.79352 0.06127876
 [62,] 10.79352 0.06127876
 [63,] 10.79352 0.06127876
 [64,] 10.79352 0.06127876
 [65,] 10.79352 0.06127876
 [66,] 10.79352 0.06127876
 [67,] 10.79352 0.06127876
 [68,] 10.79352 0.06127876
 [69,] 10.79352 0.06127876
 [70,] 10.79352 0.06127876
 [71,] 10.79352 0.06127876
 [72,] 10.70708 0.05642487
 [73,] 10.70708 0.05642487
 [74,] 10.70708 0.05642487
 [75,] 10.70708 0.05642487
 [76,] 10.70708 0.05642487
 [77,] 10.70708 0.05642487
 [78,] 10.70708 0.05642487
 [79,] 10.70708 0.05642487
 [80,] 10.70708 0.05642487
 [81,] 10.70708 0.05642487
 [82,] 10.70708 0.05642487
 [83,] 10.70708 0.05642487
 [84,] 10.70708 0.05642487
 [85,] 10.70708 0.05642487
 [86,] 10.70708 0.05642487
 [87,] 10.70708 0.05642487
 [88,] 10.70708 0.05642487
 [89,] 10.70708 0.05642487
 [90,] 10.70708 0.05642487
 [91,] 10.70708 0.05642487
 [92,] 10.70708 0.05642487
 [93,] 10.70708 0.05642487
 [94,] 10.70708 0.05642487
 [95,] 10.70708 0.05642487
 [96,] 10.70708 0.05642487
 [97,] 10.70708 0.05642487
 [98,] 10.58944 0.05157600
 [99,] 10.58944 0.05157600
[100,] 10.58944 0.05157600
[101,] 10.58944 0.05157600
[102,] 10.58944 0.05157600
[103,] 10.58944 0.05157600
[104,] 10.58944 0.05157600
[105,] 10.58944 0.05157600
[106,] 10.58944 0.05157600
[107,] 10.58944 0.05157600
[108,] 10.58944 0.05157600
[109,] 10.58944 0.05157600
[110,] 10.58944 0.05157600
[111,] 10.58944 0.05157600
[112,] 10.58944 0.05157600
[113,] 10.58944 0.05157600
[114,] 10.58944 0.05157600
[115,] 10.58944 0.05157600
[116,] 10.58944 0.05157600
[117,] 10.58944 0.05157600
[118,] 10.58944 0.05157600
[119,] 10.58944 0.05157600
[120,] 10.58944 0.05157600
[121,] 10.58944 0.05157600
[122,] 10.58944 0.05157600
[123,] 10.73709 0.04349981
[124,] 10.73709 0.04349981
[125,] 10.73709 0.04349981
[126,] 10.73709 0.04349981
[127,] 10.73709 0.04349981
[128,] 10.73709 0.04349981
[129,] 10.73709 0.04349981
[130,] 10.73709 0.04349981
[131,] 10.73709 0.04349981
[132,] 10.73709 0.04349981
[133,] 10.73709 0.04349981
[134,] 10.73709 0.04349981
[135,] 10.73709 0.04349981
[136,] 10.73709 0.04349981
[137,] 10.73709 0.04349981
[138,] 10.73709 0.04349981
[139,] 10.73709 0.04349981
[140,] 10.73709 0.04349981
[141,] 10.73709 0.04349981
[142,] 10.73709 0.04349981
[143,] 10.73709 0.04349981
[144,] 10.73709 0.04349981
[145,] 10.73709 0.04349981
[146,] 10.73709 0.04349981
[147,] 10.73709 0.04349981
[148,] 10.73709 0.04349981
[149,] 10.73709 0.04349981
[150,] 10.73709 0.04349981
[151,] 10.73709 0.04349981
[152,] 10.73709 0.04349981
[153,] 10.73709 0.04349981
[154,] 10.73709 0.04349981
[155,] 10.73709 0.04349981
[156,] 10.73709 0.04349981
[157,] 10.73709 0.04349981
[158,] 10.73709 0.04349981
[159,] 10.73709 0.04349981
[160,] 10.73709 0.04349981
[161,] 10.73709 0.04349981
[162,] 10.73709 0.04349981
[163,] 10.73709 0.04349981
[164,] 10.73709 0.04349981
[165,] 10.73709 0.04349981
[166,] 10.73709 0.04349981
[167,] 10.73709 0.04349981
[168,] 10.73709 0.04349981
[169,] 10.73709 0.04349981
[170,] 10.73709 0.04349981
[171,] 10.73709 0.04349981
[172,] 10.73709 0.04349981
[173,] 10.73709 0.04349981
[174,] 10.73709 0.04349981
[175,] 10.73709 0.04349981
[176,] 10.73709 0.04349981
[177,] 10.73709 0.04349981
[178,] 10.73709 0.04349981
[179,] 10.73709 0.04349981
[180,] 10.73709 0.04349981
[181,] 10.73709 0.04349981
[182,] 10.73709 0.04349981
[183,] 10.73709 0.04349981
[184,] 10.73709 0.04349981
[185,] 10.73709 0.04349981
[186,] 10.73709 0.04349981
[187,] 10.73709 0.04349981
[188,] 10.73709 0.04349981
[189,] 10.73709 0.04349981
[190,] 10.73709 0.04349981
[191,] 10.96639 0.03387002
[192,] 10.96639 0.03387002
[193,] 10.96639 0.03387002
[194,] 10.96639 0.03387002
[195,] 10.96639 0.03387002
[196,] 10.96639 0.03387002
[197,] 10.96639 0.03387002
[198,] 10.96639 0.03387002
[199,] 10.96639 0.03387002
[200,] 10.96639 0.03387002
[201,] 10.96639 0.03387002
[202,] 10.96639 0.03387002
[203,] 10.96639 0.03387002
[204,] 10.96639 0.03387002
[205,] 10.96639 0.03387002
[206,] 10.96639 0.03387002
[207,] 10.96639 0.03387002
[208,] 10.96639 0.03387002
[209,] 10.96639 0.03387002
[210,] 10.96639 0.03387002
[211,] 10.96639 0.03387002
[212,] 10.96639 0.03387002
[213,] 10.96639 0.03387002
[214,] 10.96639 0.03387002
[215,] 10.96639 0.03387002
[216,] 10.96639 0.03387002
[217,] 10.96639 0.03387002
[218,] 10.96639 0.03387002
[219,] 10.96639 0.03387002
[220,] 10.96639 0.03387002
[221,] 10.96639 0.03387002
[222,] 10.96639 0.03387002
[223,] 10.96639 0.03387002
[224,] 10.96639 0.03387002
[225,] 10.96639 0.03387002
[226,] 10.96639 0.03387002
[227,] 10.96639 0.03387002
[228,] 10.96639 0.03387002
[229,] 10.96639 0.03387002
[230,] 10.96639 0.03387002
[231,] 10.96639 0.03387002
[232,] 10.96639 0.03387002
[233,] 10.96639 0.03387002
[234,] 10.96639 0.03387002
[235,] 10.96639 0.03387002
[236,] 10.96639 0.03387002
[237,] 10.96639 0.03387002
[238,] 10.96639 0.03387002
[239,] 10.96639 0.03387002
[240,] 10.96639 0.03387002
[241,] 10.96639 0.03387002
[242,] 10.96639 0.03387002
[243,] 10.96639 0.03387002
[244,] 10.96639 0.03387002
[245,] 10.96639 0.03387002
[246,] 10.96639 0.03387002
[247,] 10.96639 0.03387002
[248,] 10.96639 0.03387002
[249,] 10.96639 0.03387002
[250,] 10.96639 0.03387002
[251,] 10.96639 0.03387002
[252,] 10.96639 0.03387002
[253,] 10.96639 0.03387002
[254,] 10.96639 0.03387002
[255,] 10.96639 0.03387002
[256,] 10.96639 0.03387002
[257,] 10.96639 0.03387002
[258,] 10.96639 0.03387002
[259,] 10.96639 0.03387002
[260,] 10.96639 0.03387002
[261,] 10.96639 0.03387002
[262,] 10.96639 0.03387002
[263,] 10.96639 0.03387002
[264,] 10.96639 0.03387002
[265,] 10.96639 0.03387002
[266,] 10.96639 0.03387002
[267,] 10.96639 0.03387002
[268,] 10.96639 0.03387002
[269,] 10.96639 0.03387002
[270,] 10.96639 0.03387002
[271,] 10.96639 0.03387002
[272,] 10.96639 0.03387002
[273,] 10.96639 0.03387002
[274,] 10.96639 0.03387002
[275,] 10.96639 0.03387002
[276,] 10.96639 0.03387002
[277,] 10.96639 0.03387002
> (tri <- trimean(x))
           [,1]       [,2]
  [1,] 11.08643 0.12384036
  [2,] 11.00000 0.10147771
  [3,] 10.88392 0.08494353
  [4,] 10.88392 0.07053570
  [5,] 10.82503 0.06739561
  [6,] 10.81851 0.06646438
  [7,] 10.81441 0.06588141
  [8,] 10.81441 0.06528672
  [9,] 10.80736 0.06483571
 [10,] 10.80443 0.06437647
 [11,] 10.80271 0.06404435
 [12,] 10.80099 0.06370673
 [13,] 10.79926 0.06336348
 [14,] 10.79752 0.06301445
 [15,] 10.79701 0.06277596
 [16,] 10.79701 0.06253376
 [17,] 10.79599 0.06228778
 [18,] 10.79548 0.06203792
 [19,] 10.79497 0.06178412
 [20,] 10.79445 0.06152628
 [21,] 10.79393 0.06126433
 [22,] 10.79341 0.06099817
 [23,] 10.79288 0.06072770
 [24,] 10.79236 0.06045285
 [25,] 10.79183 0.06017349
 [26,] 10.79001 0.05997967
 [27,] 10.78819 0.05978274
 [28,] 10.78636 0.05958265
 [29,] 10.78452 0.05937933
 [30,] 10.78266 0.05917272
 [31,] 10.78266 0.05896274
 [32,] 10.78080 0.05874933
 [33,] 10.77705 0.05853242
 [34,] 10.77516 0.05831193
 [35,] 10.77457 0.05819801
 [36,] 10.77398 0.05808225
 [37,] 10.77339 0.05796461
 [38,] 10.77279 0.05784508
 [39,] 10.77219 0.05772360
 [40,] 10.77158 0.05760016
 [41,] 10.77097 0.05747470
 [42,] 10.77036 0.05734721
 [43,] 10.76975 0.05721763
 [44,] 10.76913 0.05708594
 [45,] 10.76851 0.05695208
 [46,] 10.76788 0.05681604
 [47,] 10.76725 0.05667775
 [48,] 10.76662 0.05653719
 [49,] 10.76599 0.05639431
 [50,] 10.76535 0.05624906
 [51,] 10.76471 0.05610140
 [52,] 10.76406 0.05595128
 [53,] 10.76341 0.05579866
 [54,] 10.76276 0.05564349
 [55,] 10.76210 0.05548572
 [56,] 10.76144 0.05532529
 [57,] 10.76078 0.05516216
 [58,] 10.76011 0.05499626
 [59,] 10.75944 0.05482755
 [60,] 10.75877 0.05465596
 [61,] 10.75877 0.05448144
 [62,] 10.75809 0.05430393
 [63,] 10.75740 0.05412336
 [64,] 10.75672 0.05393967
 [65,] 10.75533 0.05375279
 [66,] 10.75464 0.05356265
 [67,] 10.75393 0.05336919
 [68,] 10.75323 0.05317232
 [69,] 10.75252 0.05297199
 [70,] 10.75180 0.05276810
 [71,] 10.75109 0.05256057
 [72,] 10.75036 0.05234934
 [73,] 10.75109 0.05224609
 [74,] 10.75182 0.05214096
 [75,] 10.75256 0.05203392
 [76,] 10.75330 0.05192492
 [77,] 10.75405 0.05181393
 [78,] 10.75480 0.05170092
 [79,] 10.75556 0.05158583
 [80,] 10.75632 0.05146864
 [81,] 10.75708 0.05134930
 [82,] 10.75785 0.05122776
 [83,] 10.75862 0.05110399
 [84,] 10.75940 0.05097793
 [85,] 10.76018 0.05084954
 [86,] 10.76097 0.05071878
 [87,] 10.76176 0.05058559
 [88,] 10.76256 0.05044993
 [89,] 10.76336 0.05031174
 [90,] 10.76417 0.05017097
 [91,] 10.76498 0.05002756
 [92,] 10.76579 0.04988146
 [93,] 10.76662 0.04973261
 [94,] 10.76744 0.04958095
 [95,] 10.76827 0.04942641
 [96,] 10.76911 0.04926894
 [97,] 10.76995 0.04910847
 [98,] 10.77080 0.04894493
 [99,] 10.77323 0.04886615
[100,] 10.77567 0.04878555
[101,] 10.77813 0.04870311
[102,] 10.78060 0.04861877
[103,] 10.78309 0.04853251
[104,] 10.78560 0.04844427
[105,] 10.78812 0.04835402
[106,] 10.79066 0.04826171
[107,] 10.79321 0.04816730
[108,] 10.79579 0.04807074
[109,] 10.79837 0.04797199
[110,] 10.80098 0.04787099
[111,] 10.80360 0.04776770
[112,] 10.80624 0.04766205
[113,] 10.80890 0.04755401
[114,] 10.81157 0.04744351
[115,] 10.81426 0.04733050
[116,] 10.81697 0.04721491
[117,] 10.81970 0.04709670
[118,] 10.82245 0.04697579
[119,] 10.82521 0.04685213
[120,] 10.82799 0.04672564
[121,] 10.83080 0.04659625
[122,] 10.83080 0.04646391
[123,] 10.83362 0.04632853
[124,] 10.83646 0.04633799
[125,] 10.83761 0.04634699
[126,] 10.83877 0.04635550
[127,] 10.83993 0.04636353
[128,] 10.84111 0.04637106
[129,] 10.84348 0.04637807
[130,] 10.84468 0.04638457
[131,] 10.84588 0.04639053
[132,] 10.84710 0.04639594
[133,] 10.84832 0.04640080
[134,] 10.84956 0.04640508
[135,] 10.85080 0.04640877
[136,] 10.85205 0.04641187
[137,] 10.85331 0.04641436
[138,] 10.85458 0.04641622
[139,] 10.85586 0.04641745
[140,] 10.85714 0.04641801
[141,] 10.85844 0.04641791
[142,] 10.85974 0.04641712
[143,] 10.86106 0.04641563
[144,] 10.86239 0.04641342
[145,] 10.86372 0.04641048
[146,] 10.86506 0.04640679
[147,] 10.86642 0.04640232
[148,] 10.86778 0.04639708
[149,] 10.86916 0.04639102
[150,] 10.87054 0.04638414
[151,] 10.87194 0.04637642
[152,] 10.87335 0.04636784
[153,] 10.87476 0.04635837
[154,] 10.87619 0.04634799
[155,] 10.87763 0.04633669
[156,] 10.87908 0.04632445
[157,] 10.88054 0.04631123
[158,] 10.88201 0.04629701
[159,] 10.88350 0.04628178
[160,] 10.88499 0.04626551
[161,] 10.88650 0.04624816
[162,] 10.88802 0.04622973
[163,] 10.88955 0.04621017
[164,] 10.89109 0.04618946
[165,] 10.89264 0.04616758
[166,] 10.89421 0.04614449
[167,] 10.89579 0.04612017
[168,] 10.89738 0.04609458
[169,] 10.89899 0.04606769
[170,] 10.90061 0.04603948
[171,] 10.90224 0.04600990
[172,] 10.90389 0.04597892
[173,] 10.90554 0.04594651
[174,] 10.90722 0.04591263
[175,] 10.90890 0.04587724
[176,] 10.91060 0.04584031
[177,] 10.91232 0.04580179
[178,] 10.91405 0.04576164
[179,] 10.91579 0.04571982
[180,] 10.91755 0.04567630
[181,] 10.91932 0.04563101
[182,] 10.92111 0.04558392
[183,] 10.92291 0.04553497
[184,] 10.92473 0.04548413
[185,] 10.92657 0.04543133
[186,] 10.92842 0.04537653
[187,] 10.93028 0.04531966
[188,] 10.93217 0.04526068
[189,] 10.93407 0.04519953
[190,] 10.93598 0.04513614
[191,] 10.93792 0.04507045
[192,] 10.93764 0.04516107
[193,] 10.93736 0.04525221
[194,] 10.93708 0.04534390
[195,] 10.93679 0.04543614
[196,] 10.93651 0.04552893
[197,] 10.93622 0.04562227
[198,] 10.93593 0.04571618
[199,] 10.93563 0.04581065
[200,] 10.93533 0.04590570
[201,] 10.93503 0.04600133
[202,] 10.93473 0.04609754
[203,] 10.93443 0.04619435
[204,] 10.93412 0.04629175
[205,] 10.93381 0.04638975
[206,] 10.93349 0.04648837
[207,] 10.93317 0.04658759
[208,] 10.93285 0.04668744
[209,] 10.93253 0.04678792
[210,] 10.93220 0.04688903
[211,] 10.93187 0.04699077
[212,] 10.93154 0.04709317
[213,] 10.93120 0.04719622
[214,] 10.93086 0.04729992
[215,] 10.93052 0.04740430
[216,] 10.93017 0.04750934
[217,] 10.92982 0.04761507
[218,] 10.92947 0.04772148
[219,] 10.92911 0.04782859
[220,] 10.92875 0.04793640
[221,] 10.92839 0.04804492
[222,] 10.92802 0.04815416
[223,] 10.92765 0.04826412
[224,] 10.92727 0.04837481
[225,] 10.92689 0.04848624
[226,] 10.92651 0.04859841
[227,] 10.92612 0.04871135
[228,] 10.92573 0.04882504
[229,] 10.92533 0.04893951
[230,] 10.92493 0.04905475
[231,] 10.92453 0.04917079
[232,] 10.92412 0.04928762
[233,] 10.92371 0.04940525
[234,] 10.92329 0.04952370
[235,] 10.92287 0.04964297
[236,] 10.92244 0.04976308
[237,] 10.92201 0.04988402
[238,] 10.92157 0.05000582
[239,] 10.92113 0.05012847
[240,] 10.92068 0.05025199
[241,] 10.92023 0.05037639
[242,] 10.91977 0.05050168
[243,] 10.91931 0.05062787
[244,] 10.91931 0.05075496
[245,] 10.91837 0.05088298
[246,] 10.91837 0.05101192
[247,] 10.91740 0.05114180
[248,] 10.91740 0.05127263
[249,] 10.91642 0.05140442
[250,] 10.91642 0.05153718
[251,] 10.91541 0.05167092
[252,] 10.91541 0.05180566
[253,] 10.91437 0.05194140
[254,] 10.91437 0.05207815
[255,] 10.91331 0.05221593
[256,] 10.91331 0.05235476
[257,] 10.91223 0.05249463
[258,] 10.91167 0.05263557
[259,] 10.91111 0.05277758
[260,] 10.91054 0.05292068
[261,] 10.90997 0.05306488
[262,] 10.90939 0.05321019
[263,] 10.90879 0.05335663
[264,] 10.90820 0.05350420
[265,] 10.90759 0.05365293
[266,] 10.90698 0.05380282
[267,] 10.90635 0.05395389
[268,] 10.90572 0.05410615
[269,] 10.90508 0.05425962
[270,] 10.90444 0.05441431
[271,] 10.90378 0.05457023
[272,] 10.90311 0.05472740
[273,] 10.90244 0.05488583
[274,] 10.90175 0.05504554
[275,] 10.90106 0.05520654
[276,] 10.90036 0.05536884
[277,] 10.89964 0.05553247
> (midr <- midrange(x))
[1] 47
> 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/freestat/rcomp/tmp/1tfok1248777407.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/freestat/rcomp/tmp/26s3n1248777407.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/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/3nr4x1248777408.tab") 
> 
> system("convert tmp/1tfok1248777407.ps tmp/1tfok1248777407.png")
> system("convert tmp/26s3n1248777407.ps tmp/26s3n1248777407.png")
> 
> 
> proc.time()
   user  system elapsed 
  8.272   0.628   8.454