R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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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(25.7300
+ ,26.2400
+ ,25.5150
+ ,26.4400
+ ,27.0800
+ ,26.8000
+ ,26.2850
+ ,25.4600
+ ,24.6500
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+ ,24.0550
+ ,23.9150
+ ,23.9650
+ ,24.7750
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+ ,23.9950
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+ ,24.5650
+ ,24.5900
+ ,24.6800
+ ,24.7600
+ ,24.6950
+ ,24.6400
+ ,24.8350
+ ,24.9200
+ ,24.8000
+ ,24.7500
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+ ,24.6950
+ ,24.6700
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+ ,24.5200
+ ,24.2300
+ ,24.0600
+ ,24.2350
+ ,24.3500
+ ,24.3050
+ ,24.6800
+ ,24.7100
+ ,24.9550
+ ,24.5300
+ ,24.3700
+ ,24.5900
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+ ,26.0800
+ ,26.2250
+ ,26.3350
+ ,26.7100
+ ,26.8800
+ ,26.5000
+ ,26.4250
+ ,26.2900
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+ ,26.4850
+ ,26.5650
+ ,26.6550
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+ ,26.5450
+ ,26.3250
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+ ,27.9550
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+ ,26.8000
+ ,26.6750
+ ,26.8250
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+ ,26.9750
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+ ,27.1300
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> ylimmax = ''
> ylimmin = ''
> main = 'Robustness of Central Tendency'
> #'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] 33.03119
> sqrtn <- sqrt(length(x))
> (armse <- sd(x) / sqrtn)
[1] 0.1638966
> (armose <- arm / armse)
[1] 201.5367
> (geo <- geomean(x))
[1] 32.36969
> (har <- harmean(x))
[1] 31.66681
> (qua <- quamean(x))
[1] 33.63958
> (win <- winmean(x))
           [,1]       [,2]
  [1,] 33.03141 0.16387670
  [2,] 33.03139 0.16387114
  [3,] 33.03126 0.16384075
  [4,] 33.03129 0.16383435
  [5,] 33.03197 0.16379775
  [6,] 33.03205 0.16377882
  [7,] 33.03163 0.16375415
  [8,] 33.03412 0.16362395
  [9,] 33.03445 0.16359114
 [10,] 33.03408 0.16357425
 [11,] 33.03445 0.16353629
 [12,] 33.03445 0.16351516
 [13,] 33.03428 0.16350864
 [14,] 33.03497 0.16347356
 [15,] 33.03512 0.16345732
 [16,] 33.03501 0.16345332
 [17,] 33.03591 0.16339837
 [18,] 33.03609 0.16337903
 [19,] 33.03666 0.16333992
 [20,] 33.03639 0.16330690
 [21,] 33.03945 0.16315676
 [22,] 33.03982 0.16311399
 [23,] 33.03982 0.16310091
 [24,] 33.03997 0.16307958
 [25,] 33.03981 0.16307339
 [26,] 33.04007 0.16306091
 [27,] 33.04016 0.16305659
 [28,] 33.04025 0.16303624
 [29,] 33.04063 0.16300131
 [30,] 33.04073 0.16297957
 [31,] 33.04104 0.16292980
 [32,] 33.04242 0.16286390
 [33,] 33.04220 0.16283725
 [34,] 33.04197 0.16280983
 [35,] 33.04244 0.16278775
 [36,] 33.04232 0.16277326
 [37,] 33.04207 0.16274350
 [38,] 33.04220 0.16273752
 [39,] 33.04207 0.16270007
 [40,] 33.04260 0.16267495
 [41,] 33.04273 0.16266852
 [42,] 33.04273 0.16262171
 [43,] 33.04245 0.16256341
 [44,] 33.04245 0.16251455
 [45,] 33.04215 0.16250365
 [46,] 33.04078 0.16244092
 [47,] 33.04063 0.16242226
 [48,] 33.04206 0.16235493
 [49,] 33.04206 0.16232790
 [50,] 33.04222 0.16232013
 [51,] 33.04188 0.16230787
 [52,] 33.04240 0.16228363
 [53,] 33.04258 0.16224623
 [54,] 33.04150 0.16217773
 [55,] 33.04132 0.16212589
 [56,] 33.04280 0.16199534
 [57,] 33.04262 0.16197298
 [58,] 33.04070 0.16190411
 [59,] 33.04109 0.16188593
 [60,] 33.04089 0.16186248
 [61,] 33.04149 0.16183431
 [62,] 33.04416 0.16171060
 [63,] 33.04333 0.16157837
 [64,] 33.04290 0.16156330
 [65,] 33.04333 0.16143810
 [66,] 33.04333 0.16126038
 [67,] 33.04333 0.16126038
 [68,] 33.04513 0.16114139
 [69,] 33.04445 0.16109876
 [70,] 33.04422 0.16107186
 [71,] 33.04422 0.16107186
 [72,] 33.04255 0.16099405
 [73,] 33.04110 0.16082659
 [74,] 33.03939 0.16074732
 [75,] 33.03790 0.16065594
 [76,] 33.03790 0.16061564
 [77,] 33.03790 0.16061564
 [78,] 33.03867 0.16058037
 [79,] 33.03867 0.16053854
 [80,] 33.03602 0.16044746
 [81,] 33.03495 0.16032526
 [82,] 33.03332 0.16018342
 [83,] 33.03387 0.16011490
 [84,] 33.03220 0.15992632
 [85,] 33.03192 0.15989459
 [86,] 33.03192 0.15989459
 [87,] 33.02876 0.15976533
 [88,] 33.03138 0.15960110
 [89,] 33.03020 0.15956166
 [90,] 33.03199 0.15948116
 [91,] 33.03169 0.15944747
 [92,] 33.03138 0.15941343
 [93,] 33.03200 0.15938576
 [94,] 33.02982 0.15928884
 [95,] 33.03139 0.15921826
 [96,] 33.03393 0.15910440
 [97,] 33.03297 0.15904735
 [98,] 33.03362 0.15901835
 [99,] 33.03362 0.15896732
[100,] 33.03428 0.15888627
[101,] 33.03294 0.15884198
[102,] 33.03396 0.15874441
[103,] 33.03396 0.15869152
[104,] 33.03258 0.15864609
[105,] 33.03258 0.15859226
[106,] 33.03188 0.15856916
[107,] 33.03471 0.15844326
[108,] 33.03542 0.15841155
[109,] 33.03434 0.15834810
[110,] 33.03580 0.15828357
[111,] 33.03690 0.15823479
[112,] 33.03616 0.15821044
[113,] 33.03728 0.15816083
[114,] 33.03690 0.15811939
[115,] 33.03690 0.15811939
[116,] 33.03690 0.15806029
[117,] 33.03690 0.15806029
[118,] 33.03690 0.15806029
[119,] 33.03808 0.15800814
[120,] 33.03650 0.15795612
[121,] 33.03690 0.15793845
[122,] 33.03851 0.15786726
[123,] 33.03892 0.15778685
[124,] 33.03810 0.15776003
[125,] 33.04058 0.15758750
[126,] 33.04266 0.15749602
[127,] 33.04308 0.15747759
[128,] 33.04181 0.15740374
[129,] 33.04267 0.15736634
[130,] 33.04439 0.15729102
[131,] 33.04569 0.15723416
[132,] 33.04656 0.15712937
[133,] 33.04568 0.15710069
[134,] 33.04834 0.15698466
[135,] 33.04790 0.15693611
[136,] 33.04835 0.15684804
[137,] 33.06013 0.15619988
[138,] 33.06561 0.15596405
[139,] 33.06469 0.15593413
[140,] 33.06840 0.15577513
[141,] 33.06747 0.15574479
[142,] 33.06982 0.15564419
[143,] 33.06982 0.15564419
[144,] 33.07077 0.15553173
[145,] 33.07317 0.15535719
[146,] 33.07752 0.15509960
[147,] 33.07898 0.15503761
[148,] 33.08779 0.15466444
[149,] 33.08582 0.15460039
[150,] 33.09426 0.15424511
[151,] 33.09476 0.15422414
[152,] 33.09677 0.15413975
[153,] 33.10537 0.15370481
[154,] 33.10588 0.15360802
[155,] 33.10588 0.15322854
[156,] 33.11569 0.15274660
[157,] 33.11829 0.15263958
[158,] 33.12038 0.15255354
[159,] 33.12617 0.15231604
[160,] 33.12988 0.15208682
[161,] 33.14586 0.15143730
[162,] 33.14639 0.15133750
[163,] 33.14532 0.15122401
[164,] 33.14966 0.15104894
[165,] 33.15075 0.15084643
[166,] 33.15075 0.15084643
[167,] 33.15572 0.15052677
[168,] 33.15461 0.15045063
[169,] 33.15517 0.15042825
[170,] 33.16417 0.15006903
[171,] 33.16191 0.14991438
[172,] 33.16760 0.14968831
[173,] 33.17046 0.14957491
[174,] 33.16989 0.14951497
[175,] 33.17104 0.14938602
[176,] 33.17104 0.14930248
[177,] 33.17397 0.14918689
[178,] 33.17456 0.14907930
[179,] 33.17575 0.14899026
[180,] 33.17872 0.14883056
[181,] 33.17932 0.14880703
[182,] 33.17992 0.14869736
[183,] 33.18174 0.14862612
[184,] 33.18296 0.14849156
[185,] 33.18724 0.14832400
[186,] 33.18540 0.14822086
[187,] 33.19220 0.14795554
[188,] 33.19345 0.14790714
[189,] 33.19470 0.14785853
[190,] 33.19533 0.14783411
[191,] 33.19343 0.14772838
[192,] 33.19343 0.14772838
[193,] 33.19407 0.14761303
[194,] 33.19279 0.14757189
[195,] 33.19408 0.14743046
[196,] 33.19602 0.14735508
[197,] 33.19276 0.14720478
[198,] 33.19211 0.14713759
[199,] 33.19079 0.14709559
[200,] 33.18947 0.14705343
[201,] 33.19479 0.14675389
[202,] 33.19278 0.14664304
[203,] 33.19211 0.14657443
[204,] 33.19481 0.14647029
[205,] 33.19617 0.14641801
[206,] 33.19481 0.14637472
[207,] 33.19481 0.14627849
[208,] 33.19412 0.14620836
[209,] 33.19412 0.14620836
[210,] 33.19620 0.14612814
[211,] 33.19620 0.14603020
[212,] 33.19620 0.14603020
[213,] 33.19479 0.14598558
[214,] 33.19904 0.14582239
[215,] 33.19904 0.14572277
[216,] 33.20119 0.14564057
[217,] 33.20190 0.14551264
[218,] 33.20190 0.14541181
[219,] 33.20480 0.14519979
[220,] 33.20553 0.14517199
[221,] 33.21065 0.14487481
[222,] 33.21359 0.14476298
[223,] 33.21506 0.14460417
[224,] 33.21729 0.14431368
[225,] 33.21878 0.14415388
[226,] 33.22476 0.14392758
[227,] 33.22626 0.14387085
[228,] 33.22702 0.14373785
[229,] 33.22853 0.14357581
[230,] 33.23766 0.14312725
[231,] 33.24225 0.14295536
[232,] 33.24148 0.14287821
[233,] 33.24303 0.14282051
[234,] 33.24457 0.14254928
[235,] 33.24380 0.14241782
[236,] 33.24926 0.14205305
[237,] 33.24534 0.14166121
[238,] 33.24849 0.14154412
[239,] 33.24612 0.14141579
[240,] 33.25883 0.14094506
[241,] 33.25643 0.14081584
[242,] 33.26364 0.14038639
[243,] 33.27168 0.14003623
[244,] 33.27087 0.14001100
[245,] 33.27412 0.13983708
[246,] 33.28633 0.13939094
[247,] 33.28796 0.13922074
[248,] 33.28796 0.13910971
[249,] 33.28961 0.13882709
[250,] 33.29375 0.13867696
[251,] 33.29873 0.13849642
[252,] 33.29373 0.13834038
[253,] 33.29122 0.13820585
[254,] 33.29122 0.13797972
[255,] 33.29122 0.13797972
[256,] 33.28867 0.13773021
[257,] 33.28867 0.13761607
[258,] 33.28953 0.13747074
[259,] 33.29124 0.13717927
[260,] 33.30243 0.13666205
[261,] 33.32834 0.13539300
[262,] 33.32487 0.13517089
[263,] 33.33009 0.13498599
[264,] 33.33271 0.13489333
[265,] 33.33359 0.13474629
[266,] 33.33447 0.13471522
[267,] 33.33358 0.13451276
[268,] 33.33358 0.13439561
[269,] 33.33269 0.13430946
[270,] 33.33537 0.13421505
[271,] 33.33179 0.13410480
[272,] 33.32909 0.13396264
[273,] 33.32999 0.13393083
[274,] 33.33271 0.13383511
[275,] 33.33271 0.13383511
[276,] 33.32906 0.13372318
[277,] 33.32814 0.13363485
[278,] 33.32446 0.13340153
[279,] 33.32262 0.13334522
[280,] 33.32910 0.13299583
[281,] 33.32538 0.13282166
[282,] 33.32725 0.13245096
[283,] 33.32912 0.13238541
[284,] 33.32818 0.13235688
[285,] 33.33007 0.13210615
[286,] 33.32439 0.13193417
[287,] 33.32724 0.13183462
[288,] 33.33010 0.13148661
[289,] 33.32914 0.13127114
[290,] 33.32914 0.13127114
[291,] 33.32914 0.13114599
[292,] 33.32817 0.13105411
[293,] 33.33690 0.13075082
[294,] 33.33787 0.13059093
[295,] 33.34471 0.13022787
[296,] 33.34373 0.13013500
[297,] 33.34176 0.12994879
[298,] 33.33979 0.12988954
[299,] 33.33880 0.12985983
[300,] 33.33781 0.12983004
[301,] 33.33681 0.12973586
[302,] 33.33581 0.12938367
[303,] 33.33080 0.12891132
[304,] 33.32979 0.12881670
[305,] 33.32576 0.12869664
[306,] 33.32778 0.12856178
[307,] 33.32981 0.12842658
[308,] 33.33083 0.12839148
[309,] 33.33083 0.12839148
[310,] 33.33083 0.12819417
[311,] 33.32671 0.12800623
[312,] 33.32362 0.12784853
[313,] 33.32672 0.12760922
[314,] 33.32776 0.12757352
[315,] 33.32255 0.12735315
[316,] 33.32464 0.12728131
[317,] 33.32569 0.12724530
[318,] 33.32254 0.12695105
[319,] 33.32042 0.12688899
[320,] 33.31831 0.12669198
[321,] 33.32362 0.12623983
[322,] 33.32149 0.12617740
[323,] 33.32042 0.12607826
[324,] 33.32042 0.12580618
[325,] 33.31934 0.12570660
[326,] 33.32042 0.12546474
[327,] 33.31934 0.12543319
[328,] 33.31934 0.12529582
[329,] 33.31825 0.12519526
[330,] 33.31934 0.12515799
[331,] 33.32153 0.12508325
[332,] 33.31714 0.12495544
[333,] 33.31714 0.12495544
[334,] 33.31824 0.12477815
[335,] 33.31714 0.12467600
[336,] 33.31936 0.12460023
[337,] 33.32159 0.12452427
[338,] 33.32383 0.12437762
[339,] 33.32383 0.12430691
[340,] 33.32945 0.12411573
[341,] 33.32945 0.12397362
[342,] 33.32832 0.12372718
[343,] 33.33059 0.12357884
[344,] 33.33059 0.12350729
[345,] 33.33287 0.12342993
[346,] 33.33631 0.12331364
[347,] 33.33401 0.12324715
[348,] 33.33516 0.12320818
[349,] 33.33632 0.12316911
[350,] 33.33863 0.12287280
[351,] 33.33863 0.12279998
[352,] 33.33747 0.12269330
[353,] 33.33747 0.12254694
[354,] 33.33278 0.12241178
[355,] 33.33161 0.12230442
[356,] 33.33279 0.12226464
[357,] 33.33515 0.12218490
[358,] 33.33515 0.12203675
[359,] 33.33634 0.12199669
[360,] 33.33753 0.12195653
[361,] 33.33753 0.12195653
[362,] 33.33873 0.12176647
[363,] 33.34113 0.12168556
[364,] 33.34233 0.12164502
[365,] 33.34354 0.12145358
[366,] 33.34596 0.12137213
[367,] 33.34718 0.12133131
[368,] 33.34718 0.12117941
[369,] 33.35450 0.12093349
[370,] 33.35083 0.12075159
[371,] 33.34838 0.12068107
[372,] 33.35084 0.12059850
[373,] 33.35331 0.12051575
[374,] 33.35454 0.12032030
[375,] 33.36075 0.12011265
[376,] 33.35826 0.12004121
[377,] 33.35951 0.11984448
[378,] 33.36326 0.11971909
[379,] 33.36075 0.11964715
[380,] 33.36452 0.11936515
[381,] 33.36831 0.11908267
[382,] 33.36831 0.11908267
[383,] 33.36957 0.11904043
[384,] 33.37084 0.11899809
[385,] 33.37212 0.11887673
[386,] 33.37084 0.11884014
[387,] 33.37212 0.11879750
[388,] 33.36955 0.11872398
[389,] 33.36312 0.11846035
[390,] 33.36441 0.11841737
[391,] 33.36570 0.11837429
[392,] 33.36570 0.11829407
[393,] 33.36440 0.11809619
[394,] 33.36571 0.11805281
[395,] 33.36309 0.11797828
[396,] 33.36440 0.11793468
[397,] 33.36966 0.11775995
[398,] 33.37097 0.11763488
[399,] 33.36569 0.11740298
[400,] 33.36569 0.11723973
[401,] 33.36835 0.11715160
[402,] 33.36968 0.11710744
[403,] 33.36834 0.11698733
[404,] 33.36701 0.11686698
[405,] 33.36299 0.11650536
[406,] 33.36299 0.11650536
[407,] 33.37107 0.11623740
[408,] 33.37647 0.11605853
[409,] 33.37782 0.11601374
[410,] 33.37511 0.11593675
[411,] 33.38871 0.11532007
[412,] 33.39143 0.11523007
[413,] 33.39143 0.11523007
[414,] 33.39143 0.11523007
[415,] 33.39143 0.11506153
[416,] 33.39143 0.11497708
[417,] 33.39695 0.11471050
[418,] 33.39834 0.11466493
[419,] 33.41081 0.11425433
[420,] 33.41359 0.11416298
[421,] 33.41359 0.11373650
[422,] 33.42058 0.11350740
[423,] 33.42758 0.11319244
[424,] 33.42898 0.11297498
[425,] 33.42476 0.11259767
[426,] 33.42617 0.11255154
[427,] 33.43747 0.11218201
[428,] 33.43889 0.11196298
[429,] 33.44173 0.11169724
[430,] 33.43641 0.11152405
[431,] 33.43461 0.11123527
[432,] 33.43604 0.11101464
[433,] 33.45037 0.11054798
[434,] 33.46186 0.11017458
[435,] 33.46330 0.11012784
[436,] 33.46618 0.10994655
[437,] 33.46907 0.10976492
[438,] 33.47777 0.10930723
[439,] 33.48068 0.10921320
[440,] 33.48359 0.10894240
[441,] 33.48651 0.10884806
[442,] 33.48651 0.10867074
[443,] 33.48504 0.10854044
[444,] 33.48945 0.10839809
[445,] 33.49092 0.10835056
[446,] 33.49682 0.10816010
[447,] 33.50718 0.10764744
[448,] 33.51162 0.10750433
[449,] 33.53540 0.10674107
[450,] 33.53391 0.10660885
[451,] 33.54883 0.10595097
[452,] 33.54434 0.10573346
[453,] 33.54434 0.10573346
[454,] 33.54284 0.10532860
[455,] 33.54736 0.10491221
[456,] 33.54736 0.10491221
[457,] 33.54131 0.10455915
[458,] 33.54586 0.10441403
[459,] 33.54434 0.10427977
[460,] 33.54890 0.10376813
[461,] 33.55196 0.10367092
[462,] 33.55348 0.10325498
[463,] 33.55348 0.10325498
[464,] 33.55041 0.10298414
[465,] 33.56118 0.10264153
[466,] 33.55810 0.10255463
[467,] 33.55810 0.10236935
[468,] 33.55810 0.10218374
[469,] 33.56896 0.10183879
[470,] 33.57519 0.10164150
[471,] 33.57830 0.10154271
[472,] 33.57205 0.10136674
[473,] 33.57519 0.10108025
[474,] 33.57675 0.10103057
[475,] 33.57990 0.10093103
[476,] 33.58462 0.10059313
[477,] 33.58304 0.10054871
[478,] 33.58304 0.10026516
[479,] 33.58304 0.09988650
[480,] 33.57828 0.09927891
[481,] 33.57828 0.09927891
[482,] 33.58785 0.09897663
[483,] 33.59904 0.09843325
[484,] 33.59904 0.09843325
[485,] 33.60546 0.09823103
[486,] 33.60224 0.09814076
[487,] 33.61030 0.09750347
[488,] 33.60868 0.09736212
[489,] 33.61839 0.09705697
[490,] 33.62326 0.09671145
[491,] 33.62488 0.09646736
[492,] 33.62488 0.09646736
[493,] 33.63467 0.09596669
[494,] 33.63467 0.09557853
[495,] 33.63303 0.09543543
[496,] 33.63795 0.09508658
[497,] 33.65933 0.09441845
[498,] 33.67581 0.09390472
[499,] 33.67581 0.09390472
[500,] 33.67912 0.09340978
[501,] 33.68244 0.09330663
[502,] 33.67912 0.09301675
[503,] 33.70075 0.09224616
> (tri <- trimean(x))
           [,1]       [,2]
  [1,] 33.03387 0.16371153
  [2,] 33.03634 0.16354500
  [3,] 33.03882 0.16337991
  [4,] 33.04136 0.16322373
  [5,] 33.04389 0.16306788
  [6,] 33.04630 0.16291822
  [7,] 33.04869 0.16277053
  [8,] 33.05115 0.16262523
  [9,] 33.05331 0.16249552
 [10,] 33.05543 0.16236846
 [11,] 33.05760 0.16224207
 [12,] 33.05974 0.16211818
 [13,] 33.06188 0.16199505
 [14,] 33.06405 0.16187137
 [15,] 33.06617 0.16174925
 [16,] 33.06828 0.16162721
 [17,] 33.07041 0.16150436
 [18,] 33.07249 0.16138387
 [19,] 33.07456 0.16126348
 [20,] 33.07661 0.16114422
 [21,] 33.07868 0.16102568
 [22,] 33.08060 0.16091380
 [23,] 33.08251 0.16080302
 [24,] 33.08443 0.16069186
 [25,] 33.08635 0.16058066
 [26,] 33.08827 0.16046872
 [27,] 33.08827 0.16035629
 [28,] 33.09212 0.16024302
 [29,] 33.09405 0.16012953
 [30,] 33.09597 0.16001634
 [31,] 33.09789 0.15990293
 [32,] 33.09980 0.15979028
 [33,] 33.10168 0.15967891
 [34,] 33.10356 0.15956742
 [35,] 33.10546 0.15945581
 [36,] 33.10735 0.15934389
 [37,] 33.10925 0.15923140
 [38,] 33.11116 0.15911878
 [39,] 33.11308 0.15900531
 [40,] 33.11500 0.15889189
 [41,] 33.11691 0.15877814
 [42,] 33.11883 0.15866353
 [43,] 33.12075 0.15854915
 [44,] 33.12269 0.15843527
 [45,] 33.12462 0.15832163
 [46,] 33.12658 0.15820721
 [47,] 33.12856 0.15809331
 [48,] 33.13056 0.15797881
 [49,] 33.13253 0.15786491
 [50,] 33.13451 0.15775059
 [51,] 33.13649 0.15763539
 [52,] 33.13848 0.15751939
 [53,] 33.14047 0.15740287
 [54,] 33.14047 0.15728610
 [55,] 33.14448 0.15716975
 [56,] 33.14650 0.15705345
 [57,] 33.14850 0.15693887
 [58,] 33.15052 0.15682369
 [59,] 33.15257 0.15670884
 [60,] 33.15462 0.15659328
 [61,] 33.15462 0.15647710
 [62,] 33.15874 0.15636039
 [63,] 33.16076 0.15624505
 [64,] 33.16280 0.15613120
 [65,] 33.16485 0.15601654
 [66,] 33.16689 0.15590319
 [67,] 33.16895 0.15579210
 [68,] 33.17101 0.15567994
 [69,] 33.17305 0.15556890
 [70,] 33.17510 0.15545755
 [71,] 33.17716 0.15534560
 [72,] 33.17923 0.15523256
 [73,] 33.18133 0.15511978
 [74,] 33.18346 0.15500880
 [75,] 33.18563 0.15489809
 [76,] 33.18782 0.15478784
 [77,] 33.19001 0.15467718
 [78,] 33.19221 0.15456542
 [79,] 33.19441 0.15445316
 [80,] 33.19662 0.15434047
 [81,] 33.19887 0.15422812
 [82,] 33.20114 0.15411662
 [83,] 33.20344 0.15400627
 [84,] 33.20573 0.15389590
 [85,] 33.20806 0.15378737
 [86,] 33.21040 0.15367823
 [87,] 33.21275 0.15356799
 [88,] 33.21514 0.15345858
 [89,] 33.21751 0.15335059
 [90,] 33.21990 0.15324208
 [91,] 33.22227 0.15313369
 [92,] 33.22465 0.15302469
 [93,] 33.22705 0.15291506
 [94,] 33.22945 0.15280473
 [95,] 33.23187 0.15269465
 [96,] 33.23429 0.15258448
 [97,] 33.23669 0.15247483
 [98,] 33.23910 0.15236486
 [99,] 33.24151 0.15225417
[100,] 33.24393 0.15214305
[101,] 33.24393 0.15203192
[102,] 33.24880 0.15192023
[103,] 33.25123 0.15180875
[104,] 33.25368 0.15169683
[105,] 33.25615 0.15158434
[106,] 33.25863 0.15147142
[107,] 33.26112 0.15135762
[108,] 33.26112 0.15124435
[109,] 33.26606 0.15113033
[110,] 33.26854 0.15101594
[111,] 33.27103 0.15090122
[112,] 33.27350 0.15078595
[113,] 33.27599 0.15066978
[114,] 33.27848 0.15055306
[115,] 33.27848 0.15043563
[116,] 33.28349 0.15031698
[117,] 33.28600 0.15019784
[118,] 33.28853 0.15007744
[119,] 33.29106 0.14995579
[120,] 33.29358 0.14983354
[121,] 33.29614 0.14971063
[122,] 33.29614 0.14958666
[123,] 33.30124 0.14946229
[124,] 33.30379 0.14933761
[125,] 33.30636 0.14921195
[126,] 33.30891 0.14908709
[127,] 33.31145 0.14896207
[128,] 33.31399 0.14883595
[129,] 33.31656 0.14870937
[130,] 33.31912 0.14858192
[131,] 33.32168 0.14845405
[132,] 33.32423 0.14832551
[133,] 33.32678 0.14819688
[134,] 33.32935 0.14806720
[135,] 33.33191 0.14793755
[136,] 33.33447 0.14780709
[137,] 33.33704 0.14767627
[138,] 33.33952 0.14755175
[139,] 33.34195 0.14742867
[140,] 33.34440 0.14730460
[141,] 33.34682 0.14718106
[142,] 33.34926 0.14705652
[143,] 33.35169 0.14693180
[144,] 33.35412 0.14680574
[145,] 33.35656 0.14667959
[146,] 33.35898 0.14655406
[147,] 33.36137 0.14643012
[148,] 33.36376 0.14630553
[149,] 33.36609 0.14618384
[150,] 33.36843 0.14606150
[151,] 33.37072 0.14594184
[152,] 33.37300 0.14582109
[153,] 33.37528 0.14569995
[154,] 33.37750 0.14558233
[155,] 33.37972 0.14546445
[156,] 33.38194 0.14534935
[157,] 33.38410 0.14523827
[158,] 33.38624 0.14512711
[159,] 33.38837 0.14501562
[160,] 33.39046 0.14490546
[161,] 33.39253 0.14479651
[162,] 33.39448 0.14469335
[163,] 33.39644 0.14459004
[164,] 33.39840 0.14448670
[165,] 33.40034 0.14438402
[166,] 33.40228 0.14428226
[167,] 33.40423 0.14417929
[168,] 33.40614 0.14407847
[169,] 33.40807 0.14397722
[170,] 33.41000 0.14387500
[171,] 33.41187 0.14377536
[172,] 33.41376 0.14367607
[173,] 33.41562 0.14357796
[174,] 33.41746 0.14347984
[175,] 33.41931 0.14338114
[176,] 33.42116 0.14328255
[177,] 33.42302 0.14318361
[178,] 33.42486 0.14308466
[179,] 33.42670 0.14298558
[180,] 33.42854 0.14288621
[181,] 33.43037 0.14278725
[182,] 33.43219 0.14268732
[183,] 33.43402 0.14258726
[184,] 33.43585 0.14248670
[185,] 33.43767 0.14238625
[186,] 33.43946 0.14228628
[187,] 33.44128 0.14218607
[188,] 33.44305 0.14208732
[189,] 33.44482 0.14198783
[190,] 33.44659 0.14188759
[191,] 33.44836 0.14178635
[192,] 33.45015 0.14168486
[193,] 33.45194 0.14158211
[194,] 33.45374 0.14147921
[195,] 33.45374 0.14137542
[196,] 33.45736 0.14127174
[197,] 33.45917 0.14116751
[198,] 33.46100 0.14106340
[199,] 33.46284 0.14095864
[200,] 33.46470 0.14085295
[201,] 33.46658 0.14074634
[202,] 33.46658 0.14064134
[203,] 33.47029 0.14053608
[204,] 33.47217 0.14043013
[205,] 33.47403 0.14032389
[206,] 33.47590 0.14021681
[207,] 33.47778 0.14010879
[208,] 33.47966 0.14000032
[209,] 33.47966 0.13989114
[210,] 33.48347 0.13978058
[211,] 33.48536 0.13966942
[212,] 33.48727 0.13955779
[213,] 33.48918 0.13944475
[214,] 33.49111 0.13933068
[215,] 33.49302 0.13921679
[216,] 33.49302 0.13910240
[217,] 33.49684 0.13898738
[218,] 33.49875 0.13887213
[219,] 33.50067 0.13875637
[220,] 33.50257 0.13864119
[221,] 33.50448 0.13852482
[222,] 33.50636 0.13840986
[223,] 33.50636 0.13829452
[224,] 33.51010 0.13817922
[225,] 33.51197 0.13806522
[226,] 33.51383 0.13795128
[227,] 33.51565 0.13783807
[228,] 33.51748 0.13772394
[229,] 33.51931 0.13760960
[230,] 33.51931 0.13749531
[231,] 33.52291 0.13738386
[232,] 33.52466 0.13727262
[233,] 33.52643 0.13716062
[234,] 33.52819 0.13704771
[235,] 33.52995 0.13693586
[236,] 33.53172 0.13682375
[237,] 33.53172 0.13671363
[238,] 33.53524 0.13660565
[239,] 33.53700 0.13649734
[240,] 33.53878 0.13638873
[241,] 33.54050 0.13628319
[242,] 33.54223 0.13617738
[243,] 33.54393 0.13607419
[244,] 33.54393 0.13597290
[245,] 33.54725 0.13587043
[246,] 33.54890 0.13576818
[247,] 33.55049 0.13566876
[248,] 33.55207 0.13556953
[249,] 33.55366 0.13546991
[250,] 33.55524 0.13537150
[251,] 33.55524 0.13527312
[252,] 33.55835 0.13517505
[253,] 33.55993 0.13507694
[254,] 33.56153 0.13497863
[255,] 33.56314 0.13488097
[256,] 33.56475 0.13478190
[257,] 33.56639 0.13468366
[258,] 33.56803 0.13458503
[259,] 33.56967 0.13448632
[260,] 33.57131 0.13438885
[261,] 33.57289 0.13429483
[262,] 33.57432 0.13421126
[263,] 33.57578 0.13412836
[264,] 33.57722 0.13404589
[265,] 33.57865 0.13396299
[266,] 33.58007 0.13388012
[267,] 33.58150 0.13379623
[268,] 33.58294 0.13371285
[269,] 33.58438 0.13362920
[270,] 33.58584 0.13354500
[271,] 33.58729 0.13346035
[272,] 33.58876 0.13337531
[273,] 33.59026 0.13329017
[274,] 33.59175 0.13320398
[275,] 33.59324 0.13311731
[276,] 33.59473 0.13302927
[277,] 33.59625 0.13294080
[278,] 33.59778 0.13285172
[279,] 33.59934 0.13276331
[280,] 33.60092 0.13267397
[281,] 33.60246 0.13258646
[282,] 33.60404 0.13249908
[283,] 33.60560 0.13241367
[284,] 33.60717 0.13232749
[285,] 33.60875 0.13224014
[286,] 33.61032 0.13215367
[287,] 33.61193 0.13206727
[288,] 33.61354 0.13198039
[289,] 33.61513 0.13189528
[290,] 33.61674 0.13181069
[291,] 33.61835 0.13172469
[292,] 33.61997 0.13163839
[293,] 33.62160 0.13155147
[294,] 33.62319 0.13146597
[295,] 33.62478 0.13138050
[296,] 33.62635 0.13129698
[297,] 33.62792 0.13121286
[298,] 33.62951 0.13112897
[299,] 33.63112 0.13104415
[300,] 33.63274 0.13095813
[301,] 33.63437 0.13087090
[302,] 33.63602 0.13078302
[303,] 33.63768 0.13069686
[304,] 33.63938 0.13061345
[305,] 33.64108 0.13052942
[306,] 33.64282 0.13044495
[307,] 33.64456 0.13036027
[308,] 33.64629 0.13027535
[309,] 33.64802 0.13018928
[310,] 33.64976 0.13010169
[311,] 33.65151 0.13001438
[312,] 33.65329 0.12992720
[313,] 33.65509 0.12983988
[314,] 33.65689 0.12975326
[315,] 33.65868 0.12966545
[316,] 33.66052 0.12957802
[317,] 33.66235 0.12948972
[318,] 33.66418 0.12940020
[319,] 33.66604 0.12931176
[320,] 33.66792 0.12922226
[321,] 33.66982 0.12913295
[322,] 33.67170 0.12904632
[323,] 33.67360 0.12895864
[324,] 33.67552 0.12887027
[325,] 33.67744 0.12878281
[326,] 33.67938 0.12869465
[327,] 33.68132 0.12860713
[328,] 33.68328 0.12851827
[329,] 33.68524 0.12842905
[330,] 33.68722 0.12833911
[331,] 33.68921 0.12824787
[332,] 33.69119 0.12815568
[333,] 33.69320 0.12806292
[334,] 33.69523 0.12796845
[335,] 33.69725 0.12787394
[336,] 33.69930 0.12777863
[337,] 33.70134 0.12768233
[338,] 33.70338 0.12758501
[339,] 33.70542 0.12748735
[340,] 33.70746 0.12738856
[341,] 33.70949 0.12728988
[342,] 33.71153 0.12719075
[343,] 33.71358 0.12709212
[344,] 33.71563 0.12699312
[345,] 33.71769 0.12689296
[346,] 33.71975 0.12679173
[347,] 33.72179 0.12668982
[348,] 33.72387 0.12658660
[349,] 33.72594 0.12648187
[350,] 33.72802 0.12637561
[351,] 33.73010 0.12627031
[352,] 33.73219 0.12616377
[353,] 33.73429 0.12605626
[354,] 33.73641 0.12594819
[355,] 33.73856 0.12583933
[356,] 33.74073 0.12572948
[357,] 33.74290 0.12561800
[358,] 33.74507 0.12550528
[359,] 33.74725 0.12539194
[360,] 33.74944 0.12527691
[361,] 33.75163 0.12516018
[362,] 33.75383 0.12504129
[363,] 33.75604 0.12492213
[364,] 33.75825 0.12480163
[365,] 33.76046 0.12467934
[366,] 33.76267 0.12455674
[367,] 33.76489 0.12443276
[368,] 33.76710 0.12430693
[369,] 33.76934 0.12418031
[370,] 33.77154 0.12405403
[371,] 33.77377 0.12392717
[372,] 33.77603 0.12379862
[373,] 33.77829 0.12366858
[374,] 33.78055 0.12353704
[375,] 33.78281 0.12340510
[376,] 33.78505 0.12327300
[377,] 33.78731 0.12313911
[378,] 33.78958 0.12300481
[379,] 33.78958 0.12286939
[380,] 33.79414 0.12273213
[381,] 33.79642 0.12259535
[382,] 33.79869 0.12245906
[383,] 33.80097 0.12232020
[384,] 33.80327 0.12217921
[385,] 33.80556 0.12203606
[386,] 33.80786 0.12189155
[387,] 33.81018 0.12174468
[388,] 33.81251 0.12159556
[389,] 33.81487 0.12144436
[390,] 33.81487 0.12129300
[391,] 33.81967 0.12113930
[392,] 33.82209 0.12098322
[393,] 33.82209 0.12082508
[394,] 33.82696 0.12066611
[395,] 33.82941 0.12050466
[396,] 33.83189 0.12034092
[397,] 33.83438 0.12017463
[398,] 33.83685 0.12000731
[399,] 33.83934 0.11983828
[400,] 33.84186 0.11966849
[401,] 33.84440 0.11949733
[402,] 33.84693 0.11932400
[403,] 33.84948 0.11914794
[404,] 33.84948 0.11896987
[405,] 33.85463 0.11878976
[406,] 33.85726 0.11861023
[407,] 33.85726 0.11842729
[408,] 33.86251 0.11824421
[409,] 33.86510 0.11805986
[410,] 33.86771 0.11787257
[411,] 33.87034 0.11768251
[412,] 33.87292 0.11749643
[413,] 33.87550 0.11730790
[414,] 33.87809 0.11711576
[415,] 33.88070 0.11691994
[416,] 33.88332 0.11672234
[417,] 33.88596 0.11652195
[418,] 33.88596 0.11632106
[419,] 33.89122 0.11611689
[420,] 33.89380 0.11591412
[421,] 33.89380 0.11570861
[422,] 33.89898 0.11550427
[423,] 33.90156 0.11529895
[424,] 33.90411 0.11509368
[425,] 33.90667 0.11488708
[426,] 33.90927 0.11468089
[427,] 33.91188 0.11447126
[428,] 33.91444 0.11426243
[429,] 33.91702 0.11405222
[430,] 33.91959 0.11384124
[431,] 33.92220 0.11362802
[432,] 33.92220 0.11341410
[433,] 33.92750 0.11319871
[434,] 33.93009 0.11298538
[435,] 33.93009 0.11277286
[436,] 33.93518 0.11255671
[437,] 33.93773 0.11233860
[438,] 33.94028 0.11211850
[439,] 33.94280 0.11190009
[440,] 33.94532 0.11167856
[441,] 33.94785 0.11145612
[442,] 33.95037 0.11123046
[443,] 33.95290 0.11100250
[444,] 33.95547 0.11077152
[445,] 33.95802 0.11053785
[446,] 33.95802 0.11030006
[447,] 33.96313 0.11006014
[448,] 33.96563 0.10982248
[449,] 33.96563 0.10958200
[450,] 33.97052 0.10934761
[451,] 33.97292 0.10911006
[452,] 33.97526 0.10887693
[453,] 33.97764 0.10864169
[454,] 33.98004 0.10840150
[455,] 33.98246 0.10816171
[456,] 33.98487 0.10792271
[457,] 33.98730 0.10767865
[458,] 33.98978 0.10743403
[459,] 33.99225 0.10718640
[460,] 33.99225 0.10693529
[461,] 33.99723 0.10668614
[462,] 33.99972 0.10643313
[463,] 33.99972 0.10618059
[464,] 34.00473 0.10592261
[465,] 34.00727 0.10566278
[466,] 34.00978 0.10540267
[467,] 34.01231 0.10513802
[468,] 34.01487 0.10487029
[469,] 34.01744 0.10459941
[470,] 34.01997 0.10432811
[471,] 34.02249 0.10405401
[472,] 34.02500 0.10377548
[473,] 34.02757 0.10349315
[474,] 34.02757 0.10320900
[475,] 34.03271 0.10291936
[476,] 34.03529 0.10262493
[477,] 34.03529 0.10232931
[478,] 34.04045 0.10202768
[479,] 34.04307 0.10172367
[480,] 34.04570 0.10141870
[481,] 34.04838 0.10111607
[482,] 34.05108 0.10080658
[483,] 34.05374 0.10049528
[484,] 34.05636 0.10018602
[485,] 34.05900 0.09986969
[486,] 34.06162 0.09954966
[487,] 34.06428 0.09922356
[488,] 34.06428 0.09890068
[489,] 34.06958 0.09857253
[490,] 34.07220 0.09824226
[491,] 34.07220 0.09791023
[492,] 34.07745 0.09757456
[493,] 34.08010 0.09723101
[494,] 34.08271 0.09688830
[495,] 34.08534 0.09654405
[496,] 34.08800 0.09619397
[497,] 34.09065 0.09584180
[498,] 34.09319 0.09549384
[499,] 34.09566 0.09514726
[500,] 34.09815 0.09479234
[501,] 34.10064 0.09443767
[502,] 34.10064 0.09407640
[503,] 34.10565 0.09371130
> (midr <- midrange(x))
[1] 31.0075
> midm <- array(NA,dim=8)
> for (j in 1:8) midm[j] <- midmean(x,j)
> midm
[1] 33.78731 33.78731 33.78731 33.78731 33.79588 33.78731 33.78731 33.78731
> postscript(file="/var/wessaorg/rcomp/tmp/1pz171413132270.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/25p8z1413132270.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/32nds1413132270.tab") 
> 
> try(system("convert tmp/1pz171413132270.ps tmp/1pz171413132270.png",intern=TRUE))
character(0)
> try(system("convert tmp/25p8z1413132270.ps tmp/25p8z1413132270.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 21.839   0.222  22.065