R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
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> x <- c(25
+ ,25
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> ylimmax = ''
> ylimmin = ''
> main = 'Robuustheid maximumprijs 2006'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa, P., (2012), Central Tendency (v1.0.4) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_centraltendency.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> 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] 29.90212
> sqrtn <- sqrt(length(x))
> (armse <- sd(x) / sqrtn)
[1] 0.2005545
> (armose <- arm / armse)
[1] 149.0972
> (geo <- geomean(x))
[1] 29.19555
> (har <- harmean(x))
[1] 27.85231
> (qua <- quamean(x))
[1] 30.5607
> (win <- winmean(x))
           [,1]       [,2]
  [1,] 29.90111 0.19814835
  [2,] 29.90111 0.19814835
  [3,] 29.90716 0.19756403
  [4,] 29.91120 0.19720443
  [5,] 29.92129 0.19638006
  [6,] 29.92129 0.19638006
  [7,] 29.92129 0.19638006
  [8,] 29.92129 0.19638006
  [9,] 29.92129 0.19638006
 [10,] 29.92129 0.19638006
 [11,] 29.92129 0.19638006
 [12,] 29.92129 0.19638006
 [13,] 29.92129 0.19638006
 [14,] 29.92129 0.19638006
 [15,] 29.92129 0.19638006
 [16,] 29.92129 0.19638006
 [17,] 29.92129 0.19638006
 [18,] 29.92129 0.19638006
 [19,] 29.92129 0.19638006
 [20,] 29.98184 0.19214533
 [21,] 29.98184 0.19214533
 [22,] 29.98184 0.19214533
 [23,] 29.98184 0.19214533
 [24,] 30.03027 0.18932216
 [25,] 30.03027 0.18932216
 [26,] 30.03027 0.18932216
 [27,] 30.03027 0.18932216
 [28,] 30.03027 0.18932216
 [29,] 30.03027 0.18932216
 [30,] 30.03027 0.18932216
 [31,] 30.03027 0.18932216
 [32,] 30.03027 0.18932216
 [33,] 29.93037 0.17917954
 [34,] 29.86176 0.17283795
 [35,] 29.86176 0.17283795
 [36,] 29.86176 0.17283795
 [37,] 29.86176 0.17283795
 [38,] 29.86176 0.17283795
 [39,] 29.86176 0.17283795
 [40,] 29.86176 0.17283795
 [41,] 29.73764 0.16259681
 [42,] 29.65288 0.15653089
 [43,] 29.65288 0.15653089
 [44,] 29.65288 0.15653089
 [45,] 29.65288 0.15653089
 [46,] 29.65288 0.15653089
 [47,] 29.65288 0.15653089
 [48,] 29.65288 0.15653089
 [49,] 29.65288 0.15653089
 [50,] 29.65288 0.15653089
 [51,] 29.65288 0.15653089
 [52,] 29.65288 0.15653089
 [53,] 29.65288 0.15653089
 [54,] 29.65288 0.15653089
 [55,] 29.65288 0.15653089
 [56,] 29.65288 0.15653089
 [57,] 29.65288 0.15653089
 [58,] 29.65288 0.15653089
 [59,] 29.65288 0.15653089
 [60,] 29.65288 0.15653089
 [61,] 29.65288 0.15653089
 [62,] 29.65288 0.15653089
 [63,] 29.65288 0.15653089
 [64,] 29.65288 0.15653089
 [65,] 29.65288 0.15653089
 [66,] 29.65288 0.15653089
 [67,] 29.65288 0.15653089
 [68,] 29.65288 0.15653089
 [69,] 29.65288 0.15653089
 [70,] 29.65288 0.15653089
 [71,] 29.65288 0.15653089
 [72,] 29.65288 0.15653089
 [73,] 29.65288 0.15653089
 [74,] 29.65288 0.15653089
 [75,] 29.65288 0.15653089
 [76,] 29.65288 0.15653089
 [77,] 29.73058 0.15185227
 [78,] 29.73058 0.15185227
 [79,] 29.81029 0.14740148
 [80,] 29.81029 0.14740148
 [81,] 29.89203 0.14322484
 [82,] 29.89203 0.14322484
 [83,] 29.89203 0.14322484
 [84,] 29.89203 0.14322484
 [85,] 29.89203 0.14322484
 [86,] 29.89203 0.14322484
 [87,] 29.89203 0.14322484
 [88,] 29.89203 0.14322484
 [89,] 29.89203 0.14322484
 [90,] 29.98285 0.13904058
 [91,] 29.98285 0.13904058
 [92,] 30.07568 0.13526007
 [93,] 30.07568 0.13526007
 [94,] 30.07568 0.13526007
 [95,] 29.78809 0.11540074
 [96,] 29.78809 0.11540074
 [97,] 29.59233 0.10402858
 [98,] 29.59233 0.10402858
 [99,] 29.59233 0.10402858
[100,] 29.59233 0.10402858
[101,] 29.59233 0.10402858
[102,] 29.59233 0.10402858
[103,] 29.59233 0.10402858
[104,] 29.59233 0.10402858
[105,] 29.59233 0.10402858
[106,] 29.59233 0.10402858
[107,] 29.59233 0.10402858
[108,] 29.59233 0.10402858
[109,] 29.59233 0.10402858
[110,] 29.59233 0.10402858
[111,] 29.59233 0.10402858
[112,] 29.59233 0.10402858
[113,] 29.59233 0.10402858
[114,] 29.59233 0.10402858
[115,] 29.59233 0.10402858
[116,] 29.59233 0.10402858
[117,] 29.59233 0.10402858
[118,] 29.59233 0.10402858
[119,] 29.59233 0.10402858
[120,] 29.59233 0.10402858
[121,] 29.59233 0.10402858
[122,] 29.59233 0.10402858
[123,] 29.59233 0.10402858
[124,] 29.59233 0.10402858
[125,] 29.59233 0.10402858
[126,] 29.59233 0.10402858
[127,] 29.59233 0.10402858
[128,] 29.59233 0.10402858
[129,] 29.59233 0.10402858
[130,] 29.59233 0.10402858
[131,] 29.59233 0.10402858
[132,] 29.59233 0.10402858
[133,] 29.59233 0.10402858
[134,] 29.59233 0.10402858
[135,] 29.59233 0.10402858
[136,] 29.59233 0.10402858
[137,] 29.59233 0.10402858
[138,] 29.59233 0.10402858
[139,] 29.59233 0.10402858
[140,] 29.59233 0.10402858
[141,] 29.59233 0.10402858
[142,] 29.59233 0.10402858
[143,] 29.59233 0.10402858
[144,] 29.59233 0.10402858
[145,] 29.59233 0.10402858
[146,] 29.59233 0.10402858
[147,] 29.59233 0.10402858
[148,] 29.59233 0.10402858
[149,] 29.59233 0.10402858
[150,] 29.59233 0.10402858
[151,] 29.59233 0.10402858
[152,] 29.59233 0.10402858
[153,] 29.59233 0.10402858
[154,] 29.59233 0.10402858
[155,] 29.59233 0.10402858
[156,] 29.59233 0.10402858
[157,] 29.59233 0.10402858
[158,] 29.59233 0.10402858
[159,] 29.59233 0.10402858
[160,] 29.59233 0.10402858
[161,] 29.59233 0.10402858
[162,] 29.59233 0.10402858
[163,] 29.59233 0.10402858
[164,] 29.59233 0.10402858
[165,] 29.59233 0.10402858
[166,] 29.59233 0.10402858
[167,] 29.59233 0.10402858
[168,] 29.59233 0.10402858
[169,] 29.59233 0.10402858
[170,] 29.59233 0.10402858
[171,] 29.59233 0.10402858
[172,] 29.59233 0.10402858
[173,] 29.59233 0.10402858
[174,] 29.59233 0.10402858
[175,] 29.59233 0.10402858
[176,] 29.59233 0.10402858
[177,] 29.59233 0.10402858
[178,] 29.59233 0.10402858
[179,] 29.59233 0.10402858
[180,] 29.41070 0.09480404
[181,] 29.22805 0.08628586
[182,] 29.22805 0.08628586
[183,] 29.22805 0.08628586
[184,] 29.04238 0.07863297
[185,] 29.04238 0.07863297
[186,] 29.04238 0.07863297
[187,] 29.04238 0.07863297
[188,] 29.04238 0.07863297
[189,] 29.04238 0.07863297
[190,] 28.85066 0.07207023
[191,] 28.65792 0.06718930
[192,] 28.65792 0.06718930
[193,] 28.65792 0.06718930
[194,] 28.65792 0.06718930
[195,] 28.65792 0.06718930
[196,] 28.65792 0.06718930
[197,] 28.65792 0.06718930
[198,] 28.65792 0.06718930
[199,] 28.65792 0.06718930
[200,] 28.65792 0.06718930
[201,] 28.65792 0.06718930
[202,] 28.65792 0.06718930
[203,] 28.65792 0.06718930
[204,] 28.65792 0.06718930
[205,] 28.65792 0.06718930
[206,] 28.65792 0.06718930
[207,] 28.65792 0.06718930
[208,] 28.65792 0.06718930
[209,] 28.65792 0.06718930
[210,] 28.65792 0.06718930
[211,] 28.65792 0.06718930
[212,] 28.65792 0.06718930
[213,] 28.65792 0.06718930
[214,] 28.65792 0.06718930
[215,] 28.65792 0.06718930
[216,] 28.65792 0.06718930
[217,] 28.65792 0.06718930
[218,] 28.65792 0.06718930
[219,] 28.65792 0.06718930
[220,] 28.65792 0.06718930
[221,] 28.65792 0.06718930
[222,] 28.65792 0.06718930
[223,] 28.65792 0.06718930
[224,] 28.65792 0.06718930
[225,] 28.65792 0.06718930
[226,] 28.65792 0.06718930
[227,] 28.65792 0.06718930
[228,] 28.65792 0.06718930
[229,] 28.65792 0.06718930
[230,] 28.65792 0.06718930
[231,] 28.89102 0.05452008
[232,] 28.89102 0.05452008
[233,] 28.89102 0.05452008
[234,] 28.89102 0.05452008
[235,] 28.89102 0.05452008
[236,] 28.89102 0.05452008
[237,] 28.89102 0.05452008
[238,] 28.89102 0.05452008
[239,] 28.89102 0.05452008
[240,] 28.89102 0.05452008
[241,] 28.89102 0.05452008
[242,] 28.89102 0.05452008
[243,] 28.89102 0.05452008
[244,] 28.89102 0.05452008
[245,] 28.89102 0.05452008
[246,] 28.89102 0.05452008
[247,] 28.89102 0.05452008
[248,] 28.89102 0.05452008
[249,] 29.14228 0.04117027
[250,] 29.14228 0.04117027
[251,] 29.14228 0.04117027
[252,] 29.14228 0.04117027
[253,] 29.14228 0.04117027
[254,] 29.14228 0.04117027
[255,] 29.14228 0.04117027
[256,] 29.40061 0.02775843
[257,] 29.40061 0.02775843
[258,] 29.40061 0.02775843
[259,] 29.66196 0.01503430
[260,] 29.66196 0.01503430
[261,] 29.66196 0.01503430
[262,] 29.66196 0.01503430
[263,] 29.66196 0.01503430
[264,] 29.66196 0.01503430
[265,] 29.66196 0.01503430
[266,] 29.66196 0.01503430
[267,] 29.66196 0.01503430
[268,] 29.66196 0.01503430
[269,] 29.66196 0.01503430
[270,] 29.66196 0.01503430
[271,] 29.66196 0.01503430
[272,] 29.66196 0.01503430
[273,] 29.66196 0.01503430
[274,] 29.66196 0.01503430
[275,] 29.66196 0.01503430
[276,] 29.66196 0.01503430
[277,] 29.66196 0.01503430
[278,] 29.66196 0.01503430
[279,] 29.66196 0.01503430
[280,] 29.66196 0.01503430
[281,] 29.66196 0.01503430
[282,] 29.66196 0.01503430
[283,] 29.66196 0.01503430
[284,] 29.66196 0.01503430
[285,] 29.66196 0.01503430
[286,] 29.66196 0.01503430
[287,] 29.66196 0.01503430
[288,] 29.66196 0.01503430
[289,] 29.66196 0.01503430
[290,] 29.66196 0.01503430
[291,] 29.66196 0.01503430
[292,] 29.66196 0.01503430
[293,] 29.66196 0.01503430
[294,] 29.66196 0.01503430
[295,] 29.66196 0.01503430
[296,] 29.66196 0.01503430
[297,] 29.66196 0.01503430
[298,] 29.66196 0.01503430
[299,] 29.66196 0.01503430
[300,] 29.66196 0.01503430
[301,] 29.66196 0.01503430
[302,] 29.66196 0.01503430
[303,] 29.66196 0.01503430
[304,] 29.66196 0.01503430
[305,] 29.66196 0.01503430
[306,] 29.66196 0.01503430
[307,] 29.66196 0.01503430
[308,] 29.66196 0.01503430
[309,] 29.66196 0.01503430
[310,] 29.66196 0.01503430
[311,] 29.66196 0.01503430
[312,] 29.66196 0.01503430
[313,] 29.66196 0.01503430
[314,] 29.66196 0.01503430
[315,] 29.66196 0.01503430
[316,] 29.66196 0.01503430
[317,] 29.66196 0.01503430
[318,] 29.66196 0.01503430
[319,] 29.66196 0.01503430
[320,] 29.66196 0.01503430
[321,] 29.66196 0.01503430
[322,] 29.66196 0.01503430
[323,] 29.66196 0.01503430
[324,] 29.66196 0.01503430
[325,] 29.66196 0.01503430
[326,] 29.66196 0.01503430
[327,] 29.66196 0.01503430
[328,] 29.66196 0.01503430
[329,] 29.66196 0.01503430
[330,] 29.66196 0.01503430
> (tri <- trimean(x))
           [,1]        [,2]
  [1,] 29.90091 0.196476625
  [2,] 29.90071 0.194775980
  [3,] 29.90051 0.193045511
  [4,] 29.89827 0.191488867
  [5,] 29.89501 0.190001006
  [6,] 29.88968 0.188664230
  [7,] 29.88434 0.187305868
  [8,] 29.87897 0.185925333
  [9,] 29.87359 0.184522007
 [10,] 29.86818 0.183095246
 [11,] 29.86275 0.181644376
 [12,] 29.85729 0.180168687
 [13,] 29.85181 0.178667438
 [14,] 29.84631 0.177139848
 [15,] 29.84079 0.175585096
 [16,] 29.83525 0.174002319
 [17,] 29.82968 0.172390606
 [18,] 29.82408 0.170748996
 [19,] 29.81847 0.169076474
 [20,] 29.81283 0.167371965
 [21,] 29.80400 0.165901989
 [22,] 29.79514 0.164405000
 [23,] 29.78624 0.162880113
 [24,] 29.77731 0.161326394
 [25,] 29.76621 0.159895184
 [26,] 29.75506 0.158437088
 [27,] 29.74386 0.156951208
 [28,] 29.73262 0.155436600
 [29,] 29.72133 0.153892262
 [30,] 29.70999 0.152317134
 [31,] 29.69860 0.150710091
 [32,] 29.68716 0.149069937
 [33,] 29.67568 0.147395400
 [34,] 29.66739 0.146140156
 [35,] 29.66124 0.145127743
 [36,] 29.65506 0.144098306
 [37,] 29.64885 0.143051374
 [38,] 29.64262 0.141986454
 [39,] 29.63636 0.140903032
 [40,] 29.63008 0.139800566
 [41,] 29.62376 0.138678492
 [42,] 29.62073 0.137904181
 [43,] 29.61989 0.137320584
 [44,] 29.61905 0.136728595
 [45,] 29.61820 0.136128043
 [46,] 29.61735 0.135518752
 [47,] 29.61650 0.134900539
 [48,] 29.61564 0.134273216
 [49,] 29.61478 0.133636589
 [50,] 29.61392 0.132990458
 [51,] 29.61305 0.132334615
 [52,] 29.61218 0.131668845
 [53,] 29.61130 0.130992927
 [54,] 29.61042 0.130306631
 [55,] 29.60953 0.129609718
 [56,] 29.60865 0.128901943
 [57,] 29.60775 0.128183049
 [58,] 29.60686 0.127452772
 [59,] 29.60596 0.126710835
 [60,] 29.60505 0.125956953
 [61,] 29.60414 0.125190829
 [62,] 29.60323 0.124412152
 [63,] 29.60231 0.123620602
 [64,] 29.60139 0.122815844
 [65,] 29.60046 0.121997527
 [66,] 29.59953 0.121165288
 [67,] 29.59860 0.120318745
 [68,] 29.59766 0.119457503
 [69,] 29.59672 0.118581145
 [70,] 29.59577 0.117689237
 [71,] 29.59482 0.116781324
 [72,] 29.59386 0.115856927
 [73,] 29.59290 0.114915545
 [74,] 29.59193 0.113956654
 [75,] 29.59096 0.112979698
 [76,] 29.58999 0.111984095
 [77,] 29.58901 0.110969230
 [78,] 29.58683 0.110053093
 [79,] 29.58463 0.109119634
 [80,] 29.58123 0.108276569
 [81,] 29.57780 0.107417863
 [82,] 29.57316 0.106640185
 [83,] 29.56848 0.105848264
 [84,] 29.56379 0.105041679
 [85,] 29.55907 0.104219987
 [86,] 29.55433 0.103382726
 [87,] 29.54957 0.102529408
 [88,] 29.54479 0.101659519
 [89,] 29.53998 0.100772521
 [90,] 29.53514 0.099867843
 [91,] 29.52905 0.099038069
 [92,] 29.52292 0.098191840
 [93,] 29.51553 0.097408210
 [94,] 29.50809 0.096608975
 [95,] 29.50062 0.095793630
 [96,] 29.49687 0.095406776
 [97,] 29.49310 0.095013636
 [98,] 29.49182 0.094831809
 [99,] 29.49054 0.094647161
[100,] 29.48925 0.094459643
[101,] 29.48796 0.094269209
[102,] 29.48666 0.094075807
[103,] 29.48535 0.093879387
[104,] 29.48404 0.093679898
[105,] 29.48271 0.093477286
[106,] 29.48139 0.093271497
[107,] 29.48005 0.093062476
[108,] 29.47871 0.092850165
[109,] 29.47736 0.092634506
[110,] 29.47601 0.092415440
[111,] 29.47464 0.092192906
[112,] 29.47327 0.091966840
[113,] 29.47190 0.091737178
[114,] 29.47051 0.091503855
[115,] 29.46912 0.091266802
[116,] 29.46772 0.091025951
[117,] 29.46631 0.090781230
[118,] 29.46490 0.090532567
[119,] 29.46348 0.090279886
[120,] 29.46205 0.090023111
[121,] 29.46061 0.089762163
[122,] 29.45917 0.089496960
[123,] 29.45772 0.089227419
[124,] 29.45626 0.088953455
[125,] 29.45479 0.088674978
[126,] 29.45332 0.088391900
[127,] 29.45183 0.088104126
[128,] 29.45034 0.087811561
[129,] 29.44884 0.087514106
[130,] 29.44733 0.087211659
[131,] 29.44582 0.086904116
[132,] 29.44429 0.086591369
[133,] 29.44276 0.086273306
[134,] 29.44122 0.085949814
[135,] 29.43967 0.085620773
[136,] 29.43811 0.085286062
[137,] 29.43654 0.084945555
[138,] 29.43497 0.084599122
[139,] 29.43338 0.084246628
[140,] 29.43179 0.083887934
[141,] 29.43018 0.083522898
[142,] 29.42857 0.083151370
[143,] 29.42695 0.082773197
[144,] 29.42532 0.082388220
[145,] 29.42368 0.081996273
[146,] 29.42203 0.081597186
[147,] 29.42037 0.081190782
[148,] 29.41871 0.080776876
[149,] 29.41703 0.080355278
[150,] 29.41534 0.079925790
[151,] 29.41364 0.079488204
[152,] 29.41194 0.079042307
[153,] 29.41022 0.078587876
[154,] 29.40849 0.078124678
[155,] 29.40675 0.077652470
[156,] 29.40501 0.077171001
[157,] 29.40325 0.076680005
[158,] 29.40148 0.076179209
[159,] 29.39970 0.075668323
[160,] 29.39791 0.075147046
[161,] 29.39611 0.074615062
[162,] 29.39430 0.074072042
[163,] 29.39248 0.073517636
[164,] 29.39065 0.072951481
[165,] 29.38880 0.072373194
[166,] 29.38695 0.071782369
[167,] 29.38508 0.071178583
[168,] 29.38321 0.070561385
[169,] 29.38132 0.069930302
[170,] 29.37942 0.069284832
[171,] 29.37750 0.068624444
[172,] 29.37558 0.067948573
[173,] 29.37364 0.067256621
[174,] 29.37170 0.066547949
[175,] 29.36973 0.065821878
[176,] 29.36776 0.065077681
[177,] 29.36578 0.064314581
[178,] 29.36378 0.063531743
[179,] 29.36177 0.062728272
[180,] 29.35975 0.061903203
[181,] 29.35930 0.061268305
[182,] 29.36045 0.060791629
[183,] 29.36160 0.060304322
[184,] 29.36276 0.059806022
[185,] 29.36554 0.059433514
[186,] 29.36834 0.059052938
[187,] 29.37115 0.058664046
[188,] 29.37398 0.058266578
[189,] 29.37684 0.057860264
[190,] 29.37971 0.057444817
[191,] 29.38424 0.057120101
[192,] 29.39044 0.056841099
[193,] 29.39669 0.056555223
[194,] 29.40299 0.056262265
[195,] 29.40932 0.055962010
[196,] 29.41569 0.055654230
[197,] 29.42211 0.055338688
[198,] 29.42857 0.055015136
[199,] 29.43508 0.054683314
[200,] 29.44162 0.054342950
[201,] 29.44822 0.053993757
[202,] 29.45486 0.053635436
[203,] 29.46154 0.053267671
[204,] 29.46827 0.052890128
[205,] 29.47504 0.052502459
[206,] 29.48187 0.052104293
[207,] 29.48873 0.051695240
[208,] 29.49565 0.051274888
[209,] 29.50262 0.050842800
[210,] 29.50963 0.050398511
[211,] 29.51670 0.049941530
[212,] 29.52381 0.049471332
[213,] 29.53097 0.048987359
[214,] 29.53819 0.048489016
[215,] 29.54545 0.047975665
[216,] 29.55277 0.047446623
[217,] 29.56014 0.046901156
[218,] 29.56757 0.046338473
[219,] 29.57505 0.045757723
[220,] 29.58258 0.045157984
[221,] 29.59016 0.044538254
[222,] 29.59781 0.043897444
[223,] 29.60550 0.043234365
[224,] 29.61326 0.042547715
[225,] 29.62107 0.041836061
[226,] 29.62894 0.041097819
[227,] 29.63687 0.040331233
[228,] 29.64486 0.039534347
[229,] 29.65291 0.038704966
[230,] 29.66102 0.037840616
[231,] 29.66919 0.036938486
[232,] 29.67552 0.036411528
[233,] 29.68190 0.035865884
[234,] 29.68834 0.035300457
[235,] 29.69482 0.034714035
[236,] 29.70135 0.034105284
[237,] 29.70793 0.033472720
[238,] 29.71456 0.032814691
[239,] 29.72125 0.032129344
[240,] 29.72798 0.031414587
[241,] 29.73477 0.030668047
[242,] 29.74162 0.029887010
[243,] 29.74851 0.029068342
[244,] 29.75547 0.028208396
[245,] 29.76248 0.027302880
[246,] 29.76954 0.026346675
[247,] 29.77666 0.025333600
[248,] 29.78384 0.024256052
[249,] 29.79108 0.023104499
[250,] 29.79633 0.022484728
[251,] 29.80164 0.021833731
[252,] 29.80698 0.021148310
[253,] 29.81237 0.020424666
[254,] 29.81781 0.019658222
[255,] 29.82328 0.018843376
[256,] 29.82881 0.017973136
[257,] 29.83229 0.017631400
[258,] 29.83579 0.017275135
[259,] 29.83932 0.016903238
[260,] 29.84076 0.016877511
[261,] 29.84222 0.016850727
[262,] 29.84368 0.016822855
[263,] 29.84516 0.016793862
[264,] 29.84665 0.016763713
[265,] 29.84816 0.016732374
[266,] 29.84967 0.016699806
[267,] 29.85120 0.016665972
[268,] 29.85275 0.016630831
[269,] 29.85430 0.016594342
[270,] 29.85588 0.016556462
[271,] 29.85746 0.016517146
[272,] 29.85906 0.016476347
[273,] 29.86067 0.016434017
[274,] 29.86230 0.016390105
[275,] 29.86395 0.016344558
[276,] 29.86560 0.016297321
[277,] 29.86728 0.016248337
[278,] 29.86897 0.016197545
[279,] 29.87067 0.016144884
[280,] 29.87239 0.016090286
[281,] 29.87413 0.016033685
[282,] 29.87588 0.015975007
[283,] 29.87765 0.015914179
[284,] 29.87943 0.015851120
[285,] 29.88124 0.015785750
[286,] 29.88305 0.015717980
[287,] 29.88489 0.015647721
[288,] 29.88675 0.015574875
[289,] 29.88862 0.015499343
[290,] 29.89051 0.015421018
[291,] 29.89242 0.015339789
[292,] 29.89435 0.015255537
[293,] 29.89630 0.015168137
[294,] 29.89826 0.015077458
[295,] 29.90025 0.014983360
[296,] 29.90226 0.014885695
[297,] 29.90428 0.014784305
[298,] 29.90633 0.014679024
[299,] 29.90840 0.014569672
[300,] 29.91049 0.014456061
[301,] 29.91260 0.014337987
[302,] 29.91473 0.014215233
[303,] 29.91688 0.014087567
[304,] 29.91906 0.013954739
[305,] 29.92126 0.013816480
[306,] 29.92348 0.013672499
[307,] 29.92573 0.013522483
[308,] 29.92800 0.013366092
[309,] 29.93029 0.013202954
[310,] 29.93261 0.013032667
[311,] 29.93496 0.012854787
[312,] 29.93733 0.012668831
[313,] 29.93973 0.012474262
[314,] 29.94215 0.012270487
[315,] 29.94460 0.012056847
[316,] 29.94708 0.011832603
[317,] 29.94958 0.011596926
[318,] 29.95211 0.011348875
[319,] 29.95467 0.011087379
[320,] 29.95726 0.010811206
[321,] 29.95989 0.010518925
[322,] 29.96254 0.010208863
[323,] 29.96522 0.009879033
[324,] 29.96793 0.009527045
[325,] 29.97067 0.009149980
[326,] 29.97345 0.008744202
[327,] 29.97626 0.008305072
[328,] 29.97910 0.007826506
[329,] 29.98198 0.007300225
[330,] 29.98489 0.006714425
> (midr <- midrange(x))
[1] 30.5
> midm <- array(NA,dim=8)
> for (j in 1:8) midm[j] <- midmean(x,j)
> midm
[1] 29.69244 29.69244 29.69244 29.69244 29.69244 29.69244 29.69244 29.69244
> postscript(file="/var/wessaorg/rcomp/tmp/1lkcg1457465964.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))
> lines(ub,lty=3)
> lines(lb,lty=3)
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/2smxe1457465964.ps",horizontal=F,onefile=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/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,'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/wessaorg/rcomp/tmp/359yy1457465964.tab") 
> 
> try(system("convert tmp/1lkcg1457465964.ps tmp/1lkcg1457465964.png",intern=TRUE))
character(0)
> try(system("convert tmp/2smxe1457465964.ps tmp/2smxe1457465964.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 11.175   0.216  11.396