R version 3.3.1 (2016-06-21) -- "Bug in Your Hair" Copyright (C) 2016 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) 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. 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,1.07 + ,1.071 + ,1.068 + ,1.064 + ,1.067) > 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] 1.374003 > sqrtn <- sqrt(length(x)) > (armse <- sd(x) / sqrtn) [1] 0.002499874 > (armose <- arm / armse) [1] 549.6287 > (geo <- geomean(x)) [1] 1.370483 > (har <- harmean(x)) [1] 1.366755 > (qua <- quamean(x)) [1] 1.377319 > (win <- winmean(x)) [,1] [,2] [1,] 1.374004 0.0024996770 [2,] 1.374004 0.0024995145 [3,] 1.374008 0.0024991713 [4,] 1.374005 0.0024990771 [5,] 1.374005 0.0024986756 [6,] 1.374001 0.0024985366 [7,] 1.374030 0.0024956203 [8,] 1.374047 0.0024942920 [9,] 1.374071 0.0024909366 [10,] 1.374119 0.0024871976 [11,] 1.374112 0.0024869501 [12,] 1.374128 0.0024856870 [13,] 1.374164 0.0024820148 [14,] 1.374192 0.0024767999 [15,] 1.374254 0.0024722947 [16,] 1.374331 0.0024645807 [17,] 1.374400 0.0024585774 [18,] 1.374400 0.0024585774 [19,] 1.374426 0.0024568058 [20,] 1.374536 0.0024494832 [21,] 1.374593 0.0024457253 [22,] 1.374714 0.0024380329 [23,] 1.374761 0.0024336164 [24,] 1.374761 0.0024336164 [25,] 1.374778 0.0024325601 [26,] 1.374796 0.0024314661 [27,] 1.374778 0.0024308949 [28,] 1.374778 0.0024308949 [29,] 1.374778 0.0024308949 [30,] 1.374798 0.0024296374 [31,] 1.374798 0.0024276926 [32,] 1.374864 0.0024237160 [33,] 1.374886 0.0024223600 [34,] 1.374933 0.0024195828 [35,] 1.375005 0.0024153379 [36,] 1.375005 0.0024131428 [37,] 1.375030 0.0024116651 [38,] 1.375030 0.0024116651 [39,] 1.375030 0.0024116651 [40,] 1.375085 0.0024084896 [41,] 1.375057 0.0024076401 [42,] 1.375057 0.0024076401 [43,] 1.375027 0.0024067569 [44,] 1.375027 0.0024067569 [45,] 1.375027 0.0024067569 [46,] 1.375059 0.0024049414 [47,] 1.375059 0.0024049414 [48,] 1.375059 0.0024020765 [49,] 1.375092 0.0024001575 [50,] 1.375092 0.0024001575 [51,] 1.375057 0.0023991270 [52,] 1.375129 0.0023950775 [53,] 1.375201 0.0023909832 [54,] 1.375238 0.0023889100 [55,] 1.375238 0.0023889100 [56,] 1.375200 0.0023877857 [57,] 1.375239 0.0023856056 [58,] 1.375239 0.0023856056 [59,] 1.375239 0.0023856056 [60,] 1.375198 0.0023844111 [61,] 1.375198 0.0023844111 [62,] 1.375198 0.0023844111 [63,] 1.375198 0.0023844111 [64,] 1.375198 0.0023844111 [65,] 1.375198 0.0023844111 [66,] 1.375243 0.0023818959 [67,] 1.375243 0.0023818959 [68,] 1.375243 0.0023818959 [69,] 1.375290 0.0023753033 [70,] 1.375290 0.0023753033 [71,] 1.375290 0.0023753033 [72,] 1.375290 0.0023753033 [73,] 1.375290 0.0023753033 [74,] 1.375290 0.0023753033 [75,] 1.375290 0.0023753033 [76,] 1.375342 0.0023724393 [77,] 1.375342 0.0023724393 [78,] 1.375342 0.0023724393 [79,] 1.375342 0.0023724393 [80,] 1.375342 0.0023724393 [81,] 1.375342 0.0023678090 [82,] 1.375342 0.0023678090 [83,] 1.375342 0.0023678090 [84,] 1.375342 0.0023678090 [85,] 1.375342 0.0023678090 [86,] 1.375342 0.0023678090 [87,] 1.375342 0.0023678090 [88,] 1.375342 0.0023678090 [89,] 1.375403 0.0023644785 [90,] 1.375403 0.0023644785 [91,] 1.375403 0.0023644785 [92,] 1.375340 0.0023626879 [93,] 1.375340 0.0023626879 [94,] 1.375340 0.0023626879 [95,] 1.375340 0.0023626879 [96,] 1.375406 0.0023591064 [97,] 1.375406 0.0023591064 [98,] 1.375406 0.0023591064 [99,] 1.375406 0.0023591064 [100,] 1.375406 0.0023591064 [101,] 1.375406 0.0023533994 [102,] 1.375406 0.0023533994 [103,] 1.375476 0.0023495811 [104,] 1.375476 0.0023495811 [105,] 1.375476 0.0023495811 [106,] 1.375476 0.0023495811 [107,] 1.375476 0.0023495811 [108,] 1.375476 0.0023495811 [109,] 1.375551 0.0023455538 [110,] 1.375476 0.0023434425 [111,] 1.375476 0.0023434425 [112,] 1.375552 0.0023393159 [113,] 1.375552 0.0023393159 [114,] 1.375630 0.0023351292 [115,] 1.375552 0.0023329370 [116,] 1.375552 0.0023329370 [117,] 1.375472 0.0023307261 [118,] 1.375472 0.0023307261 [119,] 1.375472 0.0023307261 [120,] 1.375472 0.0023307261 [121,] 1.375472 0.0023240234 [122,] 1.375472 0.0023240234 [123,] 1.375472 0.0023240234 [124,] 1.375472 0.0023240234 [125,] 1.375472 0.0023240234 [126,] 1.375472 0.0023240234 [127,] 1.375385 0.0023216634 [128,] 1.375385 0.0023216634 [129,] 1.375473 0.0023169457 [130,] 1.375473 0.0023169457 [131,] 1.375473 0.0023097512 [132,] 1.375473 0.0023097512 [133,] 1.375473 0.0023097512 [134,] 1.375473 0.0023097512 [135,] 1.375565 0.0023048407 [136,] 1.375565 0.0023048407 [137,] 1.375565 0.0023048407 [138,] 1.375471 0.0023023136 [139,] 1.375566 0.0022972694 [140,] 1.375470 0.0022947258 [141,] 1.375470 0.0022947258 [142,] 1.375470 0.0022947258 [143,] 1.375470 0.0022947258 [144,] 1.375569 0.0022895121 [145,] 1.375569 0.0022895121 [146,] 1.375569 0.0022895121 [147,] 1.375569 0.0022895121 [148,] 1.375467 0.0022868443 [149,] 1.375467 0.0022868443 [150,] 1.375467 0.0022868443 [151,] 1.375571 0.0022813890 [152,] 1.375571 0.0022813890 [153,] 1.375466 0.0022786526 [154,] 1.375466 0.0022786526 [155,] 1.375572 0.0022730645 [156,] 1.375572 0.0022730645 [157,] 1.375572 0.0022730645 [158,] 1.375572 0.0022730645 [159,] 1.375463 0.0022702431 [160,] 1.375573 0.0022644862 [161,] 1.375683 0.0022587082 [162,] 1.375572 0.0022558527 [163,] 1.375572 0.0022558527 [164,] 1.375572 0.0022558527 [165,] 1.375685 0.0022499426 [166,] 1.375685 0.0022499426 [167,] 1.375685 0.0022410518 [168,] 1.375800 0.0022350607 [169,] 1.375800 0.0022350607 [170,] 1.375800 0.0022350607 [171,] 1.375800 0.0022350607 [172,] 1.375682 0.0022320730 [173,] 1.375801 0.0022259150 [174,] 1.375801 0.0022259150 [175,] 1.375801 0.0022259150 [176,] 1.375921 0.0022196654 [177,] 1.375921 0.0022196654 [178,] 1.375921 0.0022196654 [179,] 1.375799 0.0022165767 [180,] 1.375922 0.0022101961 [181,] 1.376046 0.0022037951 [182,] 1.376046 0.0022037951 [183,] 1.376296 0.0021908974 [184,] 1.376674 0.0021715606 [185,] 1.376927 0.0021586780 [186,] 1.377055 0.0021522258 [187,] 1.376927 0.0021489847 [188,] 1.377055 0.0021424746 [189,] 1.377055 0.0021424746 [190,] 1.377185 0.0021359114 [191,] 1.377185 0.0021359114 [192,] 1.377185 0.0021359114 [193,] 1.377318 0.0021292609 [194,] 1.377318 0.0021192422 [195,] 1.377318 0.0021192422 [196,] 1.377318 0.0021192422 [197,] 1.377452 0.0021124816 [198,] 1.377452 0.0021124816 [199,] 1.377725 0.0020988720 [200,] 1.377725 0.0020988720 [201,] 1.377725 0.0020988720 [202,] 1.377587 0.0020954019 [203,] 1.377864 0.0020815742 [204,] 1.378004 0.0020746511 [205,] 1.378004 0.0020746511 [206,] 1.378145 0.0020676767 [207,] 1.378145 0.0020676767 [208,] 1.378572 0.0020360938 [209,] 1.379001 0.0020151559 [210,] 1.379145 0.0020081785 [211,] 1.379434 0.0019942106 [212,] 1.379434 0.0019942106 [213,] 1.379434 0.0019835163 [214,] 1.379434 0.0019835163 [215,] 1.379728 0.0019693817 [216,] 1.379728 0.0019693817 [217,] 1.380174 0.0019481211 [218,] 1.380771 0.0019199084 [219,] 1.381070 0.0019058547 [220,] 1.381070 0.0018950005 [221,] 1.381373 0.0018809275 [222,] 1.381373 0.0018809275 [223,] 1.381373 0.0018700018 [224,] 1.381373 0.0018700018 [225,] 1.382143 0.0018346216 [226,] 1.382298 0.0018275786 [227,] 1.382142 0.0018236567 [228,] 1.382298 0.0018165672 [229,] 1.382298 0.0018165672 [230,] 1.382456 0.0018094377 [231,] 1.382614 0.0018022993 [232,] 1.383090 0.0017809269 [233,] 1.383090 0.0017809269 [234,] 1.383251 0.0017737876 [235,] 1.383090 0.0017697253 [236,] 1.383090 0.0017697253 [237,] 1.383090 0.0017697253 [238,] 1.383090 0.0017697253 [239,] 1.383253 0.0017624502 [240,] 1.383253 0.0017624502 [241,] 1.383088 0.0017583215 [242,] 1.383254 0.0017509717 [243,] 1.383254 0.0017509717 [244,] 1.383254 0.0017509717 [245,] 1.383254 0.0017509717 [246,] 1.383422 0.0017435238 [247,] 1.383422 0.0017435238 [248,] 1.383592 0.0017360387 [249,] 1.383762 0.0017285468 [250,] 1.384105 0.0017135741 [251,] 1.384105 0.0017135741 [252,] 1.384277 0.0017060640 [253,] 1.384277 0.0017060640 [254,] 1.384277 0.0016941570 [255,] 1.384277 0.0016941570 [256,] 1.384277 0.0016941570 [257,] 1.384453 0.0016865399 [258,] 1.384453 0.0016865399 [259,] 1.384453 0.0016865399 [260,] 1.384453 0.0016865399 [261,] 1.384632 0.0016788289 [262,] 1.384632 0.0016788289 [263,] 1.384812 0.0016710833 [264,] 1.384812 0.0016710833 [265,] 1.384630 0.0016665586 [266,] 1.384630 0.0016665586 [267,] 1.384630 0.0016665586 [268,] 1.384630 0.0016665586 [269,] 1.384630 0.0016665586 [270,] 1.384630 0.0016665586 [271,] 1.384630 0.0016665586 [272,] 1.384817 0.0016585648 [273,] 1.385003 0.0016505658 [274,] 1.385003 0.0016505658 [275,] 1.385003 0.0016505658 [276,] 1.385003 0.0016505658 [277,] 1.385193 0.0016424739 [278,] 1.385193 0.0016424739 [279,] 1.385193 0.0016424739 [280,] 1.385193 0.0016424739 [281,] 1.385385 0.0016342894 [282,] 1.385385 0.0016342894 [283,] 1.385385 0.0016212484 [284,] 1.385774 0.0016048095 [285,] 1.385969 0.0015965984 [286,] 1.386165 0.0015883837 [287,] 1.386165 0.0015883837 [288,] 1.386165 0.0015883837 [289,] 1.386561 0.0015718587 [290,] 1.386561 0.0015718587 [291,] 1.386561 0.0015718587 [292,] 1.386561 0.0015718587 [293,] 1.386561 0.0015718587 [294,] 1.386561 0.0015718587 [295,] 1.386762 0.0015634635 [296,] 1.386762 0.0015634635 [297,] 1.386762 0.0015634635 [298,] 1.386762 0.0015634635 [299,] 1.386762 0.0015634635 [300,] 1.386762 0.0015498206 [301,] 1.386762 0.0015498206 [302,] 1.386762 0.0015498206 [303,] 1.386762 0.0015498206 [304,] 1.386762 0.0015498206 [305,] 1.386762 0.0015498206 [306,] 1.386762 0.0015498206 [307,] 1.386762 0.0015498206 [308,] 1.386762 0.0015498206 [309,] 1.386762 0.0015498206 [310,] 1.386762 0.0015357849 [311,] 1.386762 0.0015357849 [312,] 1.386762 0.0015357849 [313,] 1.386762 0.0015357849 [314,] 1.386762 0.0015357849 [315,] 1.386762 0.0015357849 [316,] 1.386762 0.0015357849 [317,] 1.386979 0.0015268203 [318,] 1.386979 0.0015268203 [319,] 1.386979 0.0015268203 [320,] 1.386979 0.0015268203 [321,] 1.386979 0.0015268203 [322,] 1.386979 0.0015268203 [323,] 1.387201 0.0015177095 [324,] 1.387422 0.0015085936 [325,] 1.387867 0.0014903759 [326,] 1.387867 0.0014903759 [327,] 1.387867 0.0014903759 [328,] 1.387643 0.0014848204 [329,] 1.387868 0.0014756242 [330,] 1.388094 0.0014664239 [331,] 1.388094 0.0014664239 [332,] 1.388094 0.0014664239 [333,] 1.388322 0.0014571639 [334,] 1.388093 0.0014515338 [335,] 1.388093 0.0014515338 [336,] 1.388093 0.0014515338 [337,] 1.388093 0.0014515338 [338,] 1.388324 0.0014421469 [339,] 1.388556 0.0014327558 [340,] 1.388556 0.0014327558 [341,] 1.388556 0.0014327558 [342,] 1.388556 0.0014327558 [343,] 1.388322 0.0014270136 [344,] 1.388322 0.0014270136 [345,] 1.388322 0.0014270136 [346,] 1.388559 0.0014174400 [347,] 1.388559 0.0014174400 [348,] 1.388559 0.0014174400 [349,] 1.388559 0.0014174400 [350,] 1.388798 0.0014077784 [351,] 1.388798 0.0014077784 [352,] 1.388798 0.0014077784 [353,] 1.388798 0.0014077784 [354,] 1.388798 0.0014077784 [355,] 1.389041 0.0013980008 [356,] 1.389041 0.0013980008 [357,] 1.389041 0.0013822288 [358,] 1.389041 0.0013822288 [359,] 1.389287 0.0013723719 [360,] 1.389287 0.0013723719 [361,] 1.389287 0.0013723719 [362,] 1.389287 0.0013723719 [363,] 1.389287 0.0013723719 [364,] 1.389287 0.0013723719 [365,] 1.389287 0.0013723719 [366,] 1.389287 0.0013723719 [367,] 1.389538 0.0013623165 [368,] 1.389538 0.0013623165 [369,] 1.389285 0.0013561963 [370,] 1.389285 0.0013561963 [371,] 1.389285 0.0013561963 [372,] 1.389285 0.0013561963 [373,] 1.389285 0.0013561963 [374,] 1.389285 0.0013561963 [375,] 1.389285 0.0013561963 [376,] 1.389285 0.0013561963 [377,] 1.389543 0.0013458728 [378,] 1.389802 0.0013355402 [379,] 1.389802 0.0013355402 [380,] 1.389802 0.0013355402 [381,] 1.389802 0.0013355402 [382,] 1.389802 0.0013355402 [383,] 1.389802 0.0013355402 [384,] 1.389802 0.0013355402 [385,] 1.389802 0.0013355402 [386,] 1.390066 0.0013250066 [387,] 1.390066 0.0013250066 [388,] 1.390066 0.0013250066 [389,] 1.390066 0.0013250066 [390,] 1.390066 0.0013250066 [391,] 1.390066 0.0013250066 [392,] 1.390066 0.0013250066 [393,] 1.391411 0.0012544080 [394,] 1.392490 0.0012118978 [395,] 1.394383 0.0011380520 [396,] 1.394383 0.0011380520 [397,] 1.394926 0.0011170379 [398,] 1.397650 0.0010131764 [399,] 1.397650 0.0010131764 [400,] 1.398198 0.0009926369 [401,] 1.398747 0.0009548066 [402,] 1.399297 0.0009344480 [403,] 1.399573 0.0009242968 [404,] 1.399573 0.0009242968 [405,] 1.399850 0.0009141319 [406,] 1.399850 0.0009141319 [407,] 1.400129 0.0009039541 [408,] 1.400129 0.0009039541 [409,] 1.400129 0.0009039541 [410,] 1.400690 0.0008835624 [411,] 1.400971 0.0008734005 [412,] 1.400971 0.0008734005 [413,] 1.400971 0.0008734005 [414,] 1.400971 0.0008734005 [415,] 1.400971 0.0008556217 [416,] 1.400971 0.0008556217 [417,] 1.401257 0.0008453668 [418,] 1.401257 0.0008453668 [419,] 1.401257 0.0008453668 [420,] 1.401257 0.0008453668 [421,] 1.401257 0.0008453668 [422,] 1.401546 0.0008350296 [423,] 1.401546 0.0008350296 [424,] 1.402126 0.0008143805 [425,] 1.402417 0.0008040959 [426,] 1.402417 0.0008040959 [427,] 1.402709 0.0007938074 [428,] 1.403295 0.0007733204 [429,] 1.403295 0.0007733204 [430,] 1.403295 0.0007733204 [431,] 1.403590 0.0007630774 [432,] 1.403294 0.0007550577 [433,] 1.403294 0.0007550577 [434,] 1.403294 0.0007550577 [435,] 1.403592 0.0007447392 [436,] 1.403592 0.0007447392 [437,] 1.403592 0.0007447392 [438,] 1.403592 0.0007447392 [439,] 1.403893 0.0007343752 [440,] 1.403893 0.0007343752 [441,] 1.403591 0.0007262339 [442,] 1.403591 0.0007262339 [443,] 1.403591 0.0007262339 [444,] 1.403591 0.0007262339 [445,] 1.403286 0.0007181276 [446,] 1.403286 0.0007181276 [447,] 1.403286 0.0007181276 [448,] 1.403593 0.0007075385 [449,] 1.403900 0.0006969737 [450,] 1.404208 0.0006864349 [451,] 1.404517 0.0006759243 [452,] 1.404207 0.0006676798 [453,] 1.404207 0.0006676798 [454,] 1.404518 0.0006571204 [455,] 1.404830 0.0006465921 [456,] 1.405142 0.0006360971 [457,] 1.405142 0.0006360971 [458,] 1.405142 0.0006360971 [459,] 1.405456 0.0006255915 [460,] 1.405456 0.0006255915 [461,] 1.405771 0.0006151002 [462,] 1.405771 0.0006151002 [463,] 1.405771 0.0006151002 [464,] 1.406089 0.0006046028 [465,] 1.406407 0.0005941469 [466,] 1.406407 0.0005941469 [467,] 1.406407 0.0005941469 [468,] 1.406407 0.0005941469 [469,] 1.406407 0.0005941469 [470,] 1.406086 0.0005854598 [471,] 1.406408 0.0005748997 [472,] 1.406408 0.0005748997 [473,] 1.406408 0.0005748997 [474,] 1.406732 0.0005643392 [475,] 1.406732 0.0005643392 [476,] 1.406732 0.0005643392 [477,] 1.407059 0.0005537811 [478,] 1.407059 0.0005537811 [479,] 1.407059 0.0005537811 [480,] 1.407059 0.0005537811 [481,] 1.407388 0.0005432056 [482,] 1.407388 0.0005432056 [483,] 1.407388 0.0005432056 [484,] 1.407388 0.0005432056 [485,] 1.407388 0.0005432056 [486,] 1.407388 0.0005432056 [487,] 1.407721 0.0005325709 > (tri <- trimean(x)) [,1] [,2] [1,] 1.374127 0.0024927161 [2,] 1.374251 0.0024856884 [3,] 1.374375 0.0024786755 [4,] 1.374498 0.0024717120 [5,] 1.374622 0.0024647053 [6,] 1.374747 0.0024577137 [7,] 1.374747 0.0024506780 [8,] 1.374994 0.0024440101 [9,] 1.375114 0.0024374526 [10,] 1.375231 0.0024312259 [11,] 1.375344 0.0024253371 [12,] 1.375458 0.0024194162 [13,] 1.375571 0.0024135521 [14,] 1.375571 0.0024079365 [15,] 1.375790 0.0024026696 [16,] 1.375894 0.0023976795 [17,] 1.375994 0.0023931667 [18,] 1.376091 0.0023889969 [19,] 1.376187 0.0023847887 [20,] 1.376282 0.0023806444 [21,] 1.376372 0.0023768657 [22,] 1.376459 0.0023732503 [23,] 1.376459 0.0023699890 [24,] 1.376621 0.0023669104 [25,] 1.376702 0.0023638036 [26,] 1.376781 0.0023607155 [27,] 1.376861 0.0023576463 [28,] 1.376861 0.0023545721 [29,] 1.377021 0.0023514694 [30,] 1.377102 0.0023483381 [31,] 1.377182 0.0023452257 [32,] 1.377263 0.0023421557 [33,] 1.377341 0.0023392001 [34,] 1.377419 0.0023362645 [35,] 1.377496 0.0023333962 [36,] 1.377571 0.0023306420 [37,] 1.377646 0.0023279321 [38,] 1.377721 0.0023252435 [39,] 1.377795 0.0023225296 [40,] 1.377870 0.0023197900 [41,] 1.377944 0.0023171187 [42,] 1.378019 0.0023144456 [43,] 1.378094 0.0023117472 [44,] 1.378170 0.0023090467 [45,] 1.378246 0.0023063204 [46,] 1.378246 0.0023035682 [47,] 1.378398 0.0023008376 [48,] 1.378474 0.0022980809 [49,] 1.378550 0.0022953697 [50,] 1.378626 0.0022926805 [51,] 1.378626 0.0022899655 [52,] 1.378779 0.0022872480 [53,] 1.378855 0.0022846011 [54,] 1.378929 0.0022820248 [55,] 1.379003 0.0022794714 [56,] 1.379003 0.0022768931 [57,] 1.379152 0.0022743136 [58,] 1.379227 0.0022717573 [59,] 1.379302 0.0022691759 [60,] 1.379377 0.0022665692 [61,] 1.379377 0.0022639609 [62,] 1.379529 0.0022613269 [63,] 1.379605 0.0022586670 [64,] 1.379682 0.0022559809 [65,] 1.379759 0.0022532683 [66,] 1.379836 0.0022505289 [67,] 1.379913 0.0022478127 [68,] 1.379989 0.0022450696 [69,] 1.380067 0.0022422993 [70,] 1.380143 0.0022396270 [71,] 1.380220 0.0022369281 [72,] 1.380297 0.0022342023 [73,] 1.380374 0.0022314493 [74,] 1.380452 0.0022286688 [75,] 1.380529 0.0022258605 [76,] 1.380607 0.0022230242 [77,] 1.380685 0.0022202113 [78,] 1.380762 0.0022173701 [79,] 1.380840 0.0022145005 [80,] 1.380919 0.0022116021 [81,] 1.380997 0.0022086746 [82,] 1.381076 0.0022057956 [83,] 1.381154 0.0022028875 [84,] 1.381234 0.0021999500 [85,] 1.381313 0.0021969828 [86,] 1.381393 0.0021939856 [87,] 1.381472 0.0021909580 [88,] 1.381553 0.0021878996 [89,] 1.381633 0.0021848102 [90,] 1.381713 0.0021817440 [91,] 1.381793 0.0021786466 [92,] 1.381793 0.0021755176 [93,] 1.381955 0.0021723826 [94,] 1.382036 0.0021692155 [95,] 1.382118 0.0021660159 [96,] 1.382200 0.0021627835 [97,] 1.382200 0.0021595741 [98,] 1.382364 0.0021563317 [99,] 1.382446 0.0021530559 [100,] 1.382528 0.0021497463 [101,] 1.382611 0.0021464025 [102,] 1.382611 0.0021431081 [103,] 1.382777 0.0021397794 [104,] 1.382860 0.0021364739 [105,] 1.382942 0.0021331340 [106,] 1.383026 0.0021297592 [107,] 1.383026 0.0021263493 [108,] 1.383193 0.0021229037 [109,] 1.383277 0.0021194220 [110,] 1.383360 0.0021159631 [111,] 1.383445 0.0021124950 [112,] 1.383445 0.0021089905 [113,] 1.383614 0.0021055088 [114,] 1.383698 0.0021019904 [115,] 1.383782 0.0020984950 [116,] 1.383867 0.0020949899 [117,] 1.383867 0.0020914476 [118,] 1.384039 0.0020878949 [119,] 1.384126 0.0020843043 [120,] 1.384213 0.0020806752 [121,] 1.384300 0.0020770072 [122,] 1.384300 0.0020733895 [123,] 1.384476 0.0020697328 [124,] 1.384564 0.0020660367 [125,] 1.384652 0.0020623007 [126,] 1.384741 0.0020585242 [127,] 1.384741 0.0020547067 [128,] 1.384920 0.0020508756 [129,] 1.385011 0.0020470026 [130,] 1.385101 0.0020431521 [131,] 1.385191 0.0020392594 [132,] 1.385282 0.0020354171 [133,] 1.385372 0.0020315326 [134,] 1.385464 0.0020276053 [135,] 1.385555 0.0020236346 [136,] 1.385646 0.0020196864 [137,] 1.385737 0.0020156944 [138,] 1.385829 0.0020116582 [139,] 1.385921 0.0020076054 [140,] 1.386014 0.0020035750 [141,] 1.386107 0.0019995277 [142,] 1.386201 0.0019954351 [143,] 1.386294 0.0019912965 [144,] 1.386389 0.0019871112 [145,] 1.386482 0.0019829480 [146,] 1.386577 0.0019787376 [147,] 1.386671 0.0019744796 [148,] 1.386766 0.0019701733 [149,] 1.386862 0.0019658467 [150,] 1.386958 0.0019614708 [151,] 1.387054 0.0019570447 [152,] 1.387150 0.0019526397 [153,] 1.387247 0.0019481840 [154,] 1.387344 0.0019437061 [155,] 1.387442 0.0019391763 [156,] 1.387540 0.0019346673 [157,] 1.387637 0.0019301060 [158,] 1.387735 0.0019254915 [159,] 1.387834 0.0019208231 [160,] 1.387933 0.0019161295 [161,] 1.388032 0.0019114560 [162,] 1.388131 0.0019068029 [163,] 1.388231 0.0019021245 [164,] 1.388331 0.0018973907 [165,] 1.388431 0.0018926007 [166,] 1.388531 0.0018878306 [167,] 1.388632 0.0018830037 [168,] 1.388732 0.0018782265 [169,] 1.388833 0.0018734696 [170,] 1.388933 0.0018686557 [171,] 1.389034 0.0018637839 [172,] 1.389135 0.0018588533 [173,] 1.389238 0.0018538931 [174,] 1.389340 0.0018489524 [175,] 1.389442 0.0018439515 [176,] 1.389545 0.0018388896 [177,] 1.389647 0.0018338468 [178,] 1.389749 0.0018287421 [179,] 1.389852 0.0018235745 [180,] 1.389956 0.0018183738 [181,] 1.390060 0.0018131915 [182,] 1.390163 0.0018080279 [183,] 1.390267 0.0018028001 [184,] 1.390267 0.0017976742 [185,] 1.390468 0.0017927334 [186,] 1.390567 0.0017878961 [187,] 1.390664 0.0017830810 [188,] 1.390763 0.0017782375 [189,] 1.390861 0.0017734166 [190,] 1.390960 0.0017685353 [191,] 1.391058 0.0017636765 [192,] 1.391157 0.0017587568 [193,] 1.391256 0.0017537751 [194,] 1.391256 0.0017488154 [195,] 1.391453 0.0017439100 [196,] 1.391552 0.0017389425 [197,] 1.391651 0.0017339117 [198,] 1.391750 0.0017289032 [199,] 1.391849 0.0017238306 [200,] 1.391947 0.0017188667 [201,] 1.392045 0.0017138392 [202,] 1.392144 0.0017087471 [203,] 1.392244 0.0017036222 [204,] 1.392244 0.0016986077 [205,] 1.392440 0.0016936164 [206,] 1.392538 0.0016885605 [207,] 1.392635 0.0016835279 [208,] 1.392733 0.0016784297 [209,] 1.392828 0.0016736542 [210,] 1.392921 0.0016690796 [211,] 1.393013 0.0016645330 [212,] 1.393104 0.0016601013 [213,] 1.393195 0.0016556123 [214,] 1.393195 0.0016511860 [215,] 1.393378 0.0016467020 [216,] 1.393468 0.0016423349 [217,] 1.393468 0.0016379109 [218,] 1.393647 0.0016336916 [219,] 1.393731 0.0016297618 [220,] 1.393814 0.0016259508 [221,] 1.393897 0.0016222091 [222,] 1.393978 0.0016185873 [223,] 1.394060 0.0016149182 [224,] 1.394060 0.0016113199 [225,] 1.394225 0.0016076743 [226,] 1.394302 0.0016044001 [227,] 1.394302 0.0016011655 [228,] 1.394458 0.0015979225 [229,] 1.394535 0.0015947196 [230,] 1.394613 0.0015914743 [231,] 1.394691 0.0015882696 [232,] 1.394767 0.0015851056 [233,] 1.394841 0.0015821476 [234,] 1.394841 0.0015791501 [235,] 1.394989 0.0015761950 [236,] 1.395064 0.0015732352 [237,] 1.395064 0.0015702355 [238,] 1.395214 0.0015671954 [239,] 1.395290 0.0015641143 [240,] 1.395365 0.0015610761 [241,] 1.395440 0.0015579968 [242,] 1.395517 0.0015549108 [243,] 1.395593 0.0015518682 [244,] 1.395593 0.0015487841 [245,] 1.395746 0.0015456578 [246,] 1.395822 0.0015424887 [247,] 1.395822 0.0015393632 [248,] 1.395975 0.0015361948 [249,] 1.396051 0.0015330704 [250,] 1.396126 0.0015299905 [251,] 1.396199 0.0015270421 [252,] 1.396273 0.0015240529 [253,] 1.396346 0.0015211094 [254,] 1.396346 0.0015181249 [255,] 1.396492 0.0015152231 [256,] 1.396566 0.0015122805 [257,] 1.396640 0.0015092966 [258,] 1.396713 0.0015063594 [259,] 1.396787 0.0015033808 [260,] 1.396861 0.0015003601 [261,] 1.396935 0.0014972968 [262,] 1.397009 0.0014942804 [263,] 1.397082 0.0014912213 [264,] 1.397155 0.0014882095 [265,] 1.397229 0.0014851549 [266,] 1.397304 0.0014820944 [267,] 1.397379 0.0014789899 [268,] 1.397454 0.0014758408 [269,] 1.397530 0.0014726464 [270,] 1.397606 0.0014694058 [271,] 1.397682 0.0014661184 [272,] 1.397759 0.0014627834 [273,] 1.397835 0.0014594961 [274,] 1.397910 0.0014562570 [275,] 1.397986 0.0014529707 [276,] 1.398062 0.0014496364 [277,] 1.398138 0.0014462534 [278,] 1.398213 0.0014429188 [279,] 1.398289 0.0014395352 [280,] 1.398365 0.0014361019 [281,] 1.398442 0.0014326178 [282,] 1.398517 0.0014291825 [283,] 1.398593 0.0014256962 [284,] 1.398670 0.0014222994 [285,] 1.398744 0.0014190524 [286,] 1.398818 0.0014158565 [287,] 1.398891 0.0014127121 [288,] 1.398964 0.0014095207 [289,] 1.399037 0.0014062813 [290,] 1.399109 0.0014031937 [291,] 1.399181 0.0014000596 [292,] 1.399253 0.0013968781 [293,] 1.399326 0.0013936484 [294,] 1.399399 0.0013903697 [295,] 1.399472 0.0013870412 [296,] 1.399544 0.0013837655 [297,] 1.399617 0.0013804398 [298,] 1.399690 0.0013770632 [299,] 1.399764 0.0013736348 [300,] 1.399837 0.0013701537 [301,] 1.399912 0.0013667689 [302,] 1.399986 0.0013633317 [303,] 1.400061 0.0013598411 [304,] 1.400136 0.0013562962 [305,] 1.400212 0.0013526959 [306,] 1.400287 0.0013490394 [307,] 1.400364 0.0013453255 [308,] 1.400440 0.0013415531 [309,] 1.400517 0.0013377212 [310,] 1.400595 0.0013338287 [311,] 1.400672 0.0013300337 [312,] 1.400750 0.0013261783 [313,] 1.400829 0.0013222611 [314,] 1.400908 0.0013182811 [315,] 1.400987 0.0013142371 [316,] 1.401066 0.0013101277 [317,] 1.401146 0.0013059518 [318,] 1.401225 0.0013018285 [319,] 1.401305 0.0012976381 [320,] 1.401385 0.0012933792 [321,] 1.401465 0.0012890504 [322,] 1.401546 0.0012846504 [323,] 1.401627 0.0012801776 [324,] 1.401707 0.0012757567 [325,] 1.401787 0.0012713884 [326,] 1.401864 0.0012671983 [327,] 1.401942 0.0012629387 [328,] 1.402020 0.0012586082 [329,] 1.402100 0.0012542540 [330,] 1.402179 0.0012499547 [331,] 1.402257 0.0012457111 [332,] 1.402335 0.0012413960 [333,] 1.402414 0.0012370079 [334,] 1.402492 0.0012326756 [335,] 1.402571 0.0012283190 [336,] 1.402651 0.0012238878 [337,] 1.402732 0.0012193804 [338,] 1.402813 0.0012147951 [339,] 1.402893 0.0012102657 [340,] 1.402972 0.0012057931 [341,] 1.403051 0.0012012428 [342,] 1.403131 0.0011966131 [343,] 1.403212 0.0011919021 [344,] 1.403294 0.0011871593 [345,] 1.403376 0.0011823322 [346,] 1.403459 0.0011774191 [347,] 1.403541 0.0011725616 [348,] 1.403624 0.0011676170 [349,] 1.403706 0.0011625832 [350,] 1.403790 0.0011574582 [351,] 1.403872 0.0011523881 [352,] 1.403955 0.0011472254 [353,] 1.404038 0.0011419680 [354,] 1.404122 0.0011366136 [355,] 1.404206 0.0011311598 [356,] 1.404290 0.0011257591 [357,] 1.404373 0.0011202574 [358,] 1.404458 0.0011148642 [359,] 1.404542 0.0011093693 [360,] 1.404626 0.0011039289 [361,] 1.404710 0.0010983851 [362,] 1.404795 0.0010927353 [363,] 1.404880 0.0010869768 [364,] 1.404966 0.0010811067 [365,] 1.405052 0.0010751219 [366,] 1.405139 0.0010690194 [367,] 1.405226 0.0010627960 [368,] 1.405226 0.0010566210 [369,] 1.405398 0.0010503226 [370,] 1.405487 0.0010439562 [371,] 1.405576 0.0010374603 [372,] 1.405665 0.0010308311 [373,] 1.405755 0.0010240647 [374,] 1.405846 0.0010171569 [375,] 1.405846 0.0010101034 [376,] 1.406028 0.0010028998 [377,] 1.406120 0.0009955412 [378,] 1.406211 0.0009882179 [379,] 1.406302 0.0009809309 [380,] 1.406392 0.0009734850 [381,] 1.406392 0.0009658747 [382,] 1.406575 0.0009580946 [383,] 1.406668 0.0009501390 [384,] 1.406760 0.0009420017 [385,] 1.406854 0.0009336762 [386,] 1.406948 0.0009251556 [387,] 1.407041 0.0009166517 [388,] 1.407041 0.0009079451 [389,] 1.407228 0.0008990281 [390,] 1.407323 0.0008898923 [391,] 1.407418 0.0008805292 [392,] 1.407514 0.0008709292 [393,] 1.407610 0.0008610824 [394,] 1.407700 0.0008524812 [395,] 1.407700 0.0008445806 [396,] 1.407858 0.0008380111 [397,] 1.407933 0.0008312919 [398,] 1.408005 0.0008248411 [399,] 1.408062 0.0008202196 [400,] 1.408120 0.0008155015 [401,] 1.408120 0.0008110549 [402,] 1.408227 0.0008071353 [403,] 1.408276 0.0008034856 [404,] 1.408325 0.0007999344 [405,] 1.408373 0.0007963112 [406,] 1.408421 0.0007927878 [407,] 1.408468 0.0007891925 [408,] 1.408468 0.0007856983 [409,] 1.408561 0.0007821325 [410,] 1.408608 0.0007784933 [411,] 1.408653 0.0007751311 [412,] 1.408695 0.0007718733 [413,] 1.408739 0.0007685486 [414,] 1.408782 0.0007651554 [415,] 1.408782 0.0007616920 [416,] 1.408870 0.0007584201 [417,] 1.408914 0.0007550800 [418,] 1.408957 0.0007518487 [419,] 1.409000 0.0007485498 [420,] 1.409043 0.0007451814 [421,] 1.409043 0.0007417418 [422,] 1.409131 0.0007382289 [423,] 1.409174 0.0007348271 [424,] 1.409217 0.0007313525 [425,] 1.409257 0.0007281738 [426,] 1.409296 0.0007251091 [427,] 1.409334 0.0007219792 [428,] 1.409334 0.0007189647 [429,] 1.409406 0.0007162428 [430,] 1.409441 0.0007134633 [431,] 1.409476 0.0007106245 [432,] 1.409509 0.0007079036 [433,] 1.409545 0.0007052207 [434,] 1.409545 0.0007024800 [435,] 1.409616 0.0006996798 [436,] 1.409650 0.0006970016 [437,] 1.409685 0.0006942651 [438,] 1.409720 0.0006914688 [439,] 1.409755 0.0006886112 [440,] 1.409788 0.0006858778 [441,] 1.409788 0.0006830843 [442,] 1.409858 0.0006803291 [443,] 1.409894 0.0006775123 [444,] 1.409930 0.0006746324 [445,] 1.409967 0.0006716875 [446,] 1.410005 0.0006687753 [447,] 1.410044 0.0006657965 [448,] 1.410044 0.0006627492 [449,] 1.410121 0.0006598356 [450,] 1.410157 0.0006570566 [451,] 1.410191 0.0006544128 [452,] 1.410224 0.0006519050 [453,] 1.410259 0.0006494408 [454,] 1.410259 0.0006469191 [455,] 1.410328 0.0006445373 [456,] 1.410361 0.0006422956 [457,] 1.410391 0.0006401944 [458,] 1.410422 0.0006380437 [459,] 1.410453 0.0006358423 [460,] 1.410482 0.0006337831 [461,] 1.410482 0.0006316750 [462,] 1.410540 0.0006297100 [463,] 1.410568 0.0006276981 [464,] 1.410597 0.0006256379 [465,] 1.410623 0.0006237226 [466,] 1.410648 0.0006219518 [467,] 1.410674 0.0006201379 [468,] 1.410674 0.0006182798 [469,] 1.410725 0.0006163762 [470,] 1.410750 0.0006144261 [471,] 1.410778 0.0006125418 [472,] 1.410805 0.0006108088 [473,] 1.410831 0.0006090322 [474,] 1.410831 0.0006072109 [475,] 1.410883 0.0006055426 [476,] 1.410908 0.0006038318 [477,] 1.410933 0.0006020773 [478,] 1.410956 0.0006004773 [479,] 1.410980 0.0005988360 [480,] 1.411004 0.0005971522 [481,] 1.411004 0.0005954249 [482,] 1.411050 0.0005938546 [483,] 1.411073 0.0005922432 [484,] 1.411095 0.0005905894 [485,] 1.411118 0.0005888921 [486,] 1.411141 0.0005871502 [487,] 1.411164 0.0005853623 > (midr <- midrange(x)) [1] 1.283 > midm <- array(NA,dim=8) > for (j in 1:8) midm[j] <- midmean(x,j) > midm [1] 1.404632 1.404632 1.404632 1.404632 1.404632 1.404632 1.404632 1.404632 > postscript(file="/var/wessaorg/rcomp/tmp/1hw1c1476187290.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/2t1c61476187290.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/3i9me1476187290.tab") > > try(system("convert tmp/1hw1c1476187290.ps tmp/1hw1c1476187290.png",intern=TRUE)) character(0) > try(system("convert tmp/2t1c61476187290.ps tmp/2t1c61476187290.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 31.242 0.098 31.427