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Workshop 2 Question 6 distributions

R Software Module: rwasp_centraltendency.wasp (opens new window with default values)

Title produced by software: Central Tendency

Date of computation: Sun, 21 Oct 2007 09:57:03 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2007/Oct/21/cl9y0jtl5xypvop1192985665.htm/, Retrieved Sun, 21 Oct 2007 18:54:27 +0200

User-defined keywords:

Workshop 2 Question 6, distributions, kim, wim, hoyi, central tendency

Dataseries X:

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0.0257493744938832 0.021734532514729 0.0181409407214812 0.0152701505942662 0.0130169628370724 0.0113755336274530 0.0104102672587416 0.0100957980377989 0.0102701844619835 0.0107193845356072 0.0112601657760742 0.0117752995862039 0.0122043778163178 0.0125298799458765 0.0127639590477691 0.012937217037477 0.0130890257027324 0.0132543391742880 0.0134562040704081 0.0137014134900641 0.0139761760838112 0.014254906668871 0.0145009100196785 0.0146742460172555 0.0147425838993809 0.0146806157352704 0.0144813558459163 0.0141518828509622 0.0137166291746623 0.0132144865802642 0.0126954985073174 0.0122127590106538 0.0118197530780406 0.0115605302029674 0.0114653295564385 0.0115477666075857 0.0118008984086779 0.0122015373336842 0.0127161972913673 0.0133047190084541 0.0139270942039374 0.0145468382768111 0.0151326313986313 0.0156627183912659 0.0161249314936152 0.0165157734928721 0.0168385143727890 0.0171040905359246 0.0173246160284153 0.0175143229302128 0.0176871470530748 0.0178499332357475 0.0180101805121891 0.0181670326317408 0.0183152217343903 0.0184445154323164 0.0185417822082271 0.0185853019135757 0.0185546256675338 0.0184274237550889 0.0181815126038245 0.0178003184355686 0.0172750557804465 0.0166064564640451 0.0158060406733841 0.0148957772281856 0.0139084949432564 0.0128841617006653 0.0118732217969068 0.0109276070046339 0.0101118222886584 0.0094955906524746 0.00916083841819465 0.00919703433683982 0.00968438506916855 0.0106742913471305 0.0121649595096515 0.0140954137567437 0.0163422947029298 0.0187253547553665 0.0210153504000639 0.0229581518854807 0.0243101462873557 0.024886670194248 0.0246046329518060 0.0235246397989794 0.0218629790162297 0.0199805985887911 0.0183298783311204 0.0173304254644116 0.0171531920939682 0.0175490373660681 0.0179383444747054 0.0177538738190025 0.0168765909408637 0.0161209786732948 0.0174324825582754 0.0223196274919401 0.0295050170940449

Text written by user:

Output produced by software:

Summary of compuational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.0155758761290765 0.000408111794454197 38.165709349095 Geometric Mean 0.0150857615662504 Harmonic Mean 0.0146255667585770 Quadratic Mean 0.0160913122644504 Winsorized Mean ( 1 / 33 ) 0.0155383059605763 0.000396437857445492 39.1948086408794 Winsorized Mean ( 2 / 33 ) 0.0155269090316066 0.00039105116495105 39.7055690488741 Winsorized Mean ( 3 / 33 ) 0.0155240834914324 0.000388097703480426 40.000451824924 Winsorized Mean ( 4 / 33 ) 0.0155288077865508 0.000382804246379527 40.5659235325068 Winsorized Mean ( 5 / 33 ) 0.0154899450472823 0.000373687567125533 41.4515932826813 Winsorized Mean ( 6 / 33 ) 0.0154652101539384 0.00036492063514891 42.379653722864 Winsorized Mean ( 7 / 33 ) 0.0154299668087114 0.000354272020690375 43.5540090878269 Winsorized Mean ( 8 / 33 ) 0.0154144012016551 0.000344037077371126 44.8044766553664 Winsorized Mean ( 9 / 33 ) 0.0154068236277438 0.000341240127400162 45.1495073135895 Winsorized Mean ( 10 / 33 ) 0.0153552115423258 0.000324965098415081 47.2518791009132 Winsorized Mean ( 11 / 33 ) 0.0152771900934555 0.000300432807983684 50.8506051519021 Winsorized Mean ( 12 / 33 ) 0.015139023307753 0.000276187133455022 54.8143684985182 Winsorized Mean ( 13 / 33 ) 0.0151324239151624 0.000272147566444164 55.6037451037324 Winsorized Mean ( 14 / 33 ) 0.0151397436249753 0.000269994027309544 56.0743649622212 Winsorized Mean ( 15 / 33 ) 0.0151397315243806 0.000269480795921236 56.1811147715537 Winsorized Mean ( 16 / 33 ) 0.0151587218447565 0.000262832761459880 57.6744001035439 Winsorized Mean ( 17 / 33 ) 0.0151601826676776 0.000261882352057193 57.8892871115146 Winsorized Mean ( 18 / 33 ) 0.0151458752577493 0.000259192963881 58.4347469582661 Winsorized Mean ( 19 / 33 ) 0.0151533240488663 0.00025750165601245 58.8474819289459 Winsorized Mean ( 20 / 33 ) 0.0151852490149630 0.000246618488507238 61.5738467414918 Winsorized Mean ( 21 / 33 ) 0.0151899364381037 0.000245270743601471 61.9313017731342 Winsorized Mean ( 22 / 33 ) 0.0151847694541868 0.000244475456722901 62.1116313994575 Winsorized Mean ( 23 / 33 ) 0.0151563379658637 0.000240519278228197 63.015065060539 Winsorized Mean ( 24 / 33 ) 0.0152158009713762 0.000229003120358317 66.4436403642373 Winsorized Mean ( 25 / 33 ) 0.015235297769983 0.000221351258534554 68.8286024251571 Winsorized Mean ( 26 / 33 ) 0.015227703664737 0.000219149456007217 69.4854732572804 Winsorized Mean ( 27 / 33 ) 0.0152280628846922 0.000216116067936333 70.4624280374114 Winsorized Mean ( 28 / 33 ) 0.0152431873779762 0.000209942704766774 72.606416092952 Winsorized Mean ( 29 / 33 ) 0.0152182724673130 0.000203393665900127 74.8217620247119 Winsorized Mean ( 30 / 33 ) 0.0152319183351131 0.000199411996215240 76.3841625589671 Winsorized Mean ( 31 / 33 ) 0.0152288566917333 0.000193879702854874 78.5479679795706 Winsorized Mean ( 32 / 33 ) 0.0152364215511007 0.000185527714983508 82.1247733927792 Winsorized Mean ( 33 / 33 ) 0.0152477692704432 0.000183830960747619 82.9445116776431 Trimmed Mean ( 1 / 33 ) 0.0154984111470756 0.000384984930535941 40.257189094389 Trimmed Mean ( 2 / 33 ) 0.0154568365519538 0.000371915737295532 41.5600497692074 Trimmed Mean ( 3 / 33 ) 0.0154195399095579 0.000360286262034393 42.7980235007849 Trimmed Mean ( 4 / 33 ) 0.0153816285007463 0.000348243988333216 44.1691142304183 Trimmed Mean ( 5 / 33 ) 0.0153406994296939 0.000336232489975608 45.6252738419383 Trimmed Mean ( 6 / 33 ) 0.015306733185691 0.000325148189793675 47.0761753137977 Trimmed Mean ( 7 / 33 ) 0.0152759700095018 0.000314677290758479 48.5448758398851 Trimmed Mean ( 8 / 33 ) 0.0152497295910479 0.000305130560404981 49.9777196056921 Trimmed Mean ( 9 / 33 ) 0.0152245714283162 0.000296312037770951 51.3801988702355 Trimmed Mean ( 10 / 33 ) 0.0151991945397883 0.000286622136893758 53.0286833547061 Trimmed Mean ( 11 / 33 ) 0.0151791352108906 0.000278568332498064 54.4898089268495 Trimmed Mean ( 12 / 33 ) 0.0151673686249828 0.000273678410783056 55.4204059486663 Trimmed Mean ( 13 / 33 ) 0.0151705720341218 0.000271928770061464 55.7887715621 Trimmed Mean ( 14 / 33 ) 0.0151746637608575 0.000270354813739852 56.1286982500679 Trimmed Mean ( 15 / 33 ) 0.0151782425325472 0.000268670460235941 56.4939015596207 Trimmed Mean ( 16 / 33 ) 0.0151820361542472 0.000266594967071968 56.9479473712225 Trimmed Mean ( 17 / 33 ) 0.0151842554971699 0.000264953875930505 57.3090521655649 Trimmed Mean ( 18 / 33 ) 0.0151864807167028 0.000262948715313719 57.7545347524673 Trimmed Mean ( 19 / 33 ) 0.0151901418646413 0.000260753498220787 58.2547960747947 Trimmed Mean ( 20 / 33 ) 0.0151933933934207 0.000258170647667998 58.8501966844776 Trimmed Mean ( 21 / 33 ) 0.015194100668392 0.000256484286744039 59.2398889665903 Trimmed Mean ( 22 / 33 ) 0.0151944576024167 0.000254382043613995 59.7308575186743 Trimmed Mean ( 23 / 33 ) 0.01519528018104 0.000251675798389677 60.3764059884403 Trimmed Mean ( 24 / 33 ) 0.0151985668641623 0.000248720129773233 61.1071041094235 Trimmed Mean ( 25 / 33 ) 0.0151971160337081 0.000246677727881719 61.6071672307401 Trimmed Mean ( 26 / 33 ) 0.0151938990193326 0.000245046156994438 62.0042330216075 Trimmed Mean ( 27 / 33 ) 0.0151938990193326 0.000242972081566979 62.5335179307179 Trimmed Mean ( 28 / 33 ) 0.0151878815189517 0.00024046707053492 63.159922417511 Trimmed Mean ( 29 / 33 ) 0.0151831121112484 0.000237974817078953 63.801339560274 Trimmed Mean ( 30 / 33 ) 0.0151800344142984 0.000235560728556849 64.4421271206713 Trimmed Mean ( 31 / 33 ) 0.0151754069294690 0.000232666214935967 65.2239386523972 Trimmed Mean ( 32 / 33 ) 0.0151705299465527 0.000229463573417958 66.1130205573859 Trimmed Mean ( 33 / 33 ) 0.0151643526086263 0.000226479121707179 66.9569560951968 Median 0.0147425838993809 Midrange 0.0193329277561198 Midmean - Weighted Average at Xnp 0.0151437713119514 Midmean - Weighted Average at X(n+1)p 0.0151985668641623 Midmean - Empirical Distribution Function 0.0151985668641623 Midmean - Empirical Distribution Function - Averaging 0.0151985668641623 Midmean - Empirical Distribution Function - Interpolation 0.0151971160337081 Midmean - Closest Observation 0.0151437713119514 Midmean - True Basic - Statistics Graphics Toolkit 0.0151985668641623 Midmean - MS Excel (old versions) 0.0151985668641623 Number of observations 99

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/cl9y0jtl5xypvop1192985665/12psv1192985821.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/cl9y0jtl5xypvop1192985665/12psv1192985821.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/cl9y0jtl5xypvop1192985665/2cotz1192985821.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Oct/21/cl9y0jtl5xypvop1192985665/2cotz1192985821.ps (open in new window)

Parameters:

R code (references can be found in the software module):

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)) sqrtn <- sqrt(length(x)) (armse <- sd(x) / sqrtn) (armose <- arm / armse) (geo <- geomean(x)) (har <- harmean(x)) (qua <- quamean(x)) (win <- winmean(x)) (tri <- trimean(x)) (midr <- midrange(x)) midm <- array(NA,dim=8) for (j in 1:8) midm[j] <- midmean(x,j) midm bitmap(file='test1.png') 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() bitmap(file='test2.png') 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() load(file='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='mytable.tab')

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