Central tendency residuals (goudprijs)

*The author of this computation has been verified*

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

Title produced by software: Central Tendency

Date of computation: Sun, 14 Dec 2008 14:08:06 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229288956nm00hjd97l8g0o0.htm/, Retrieved Sun, 14 Dec 2008 22:09:16 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/14/t1229288956nm00hjd97l8g0o0.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

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Dataseries X:

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10.0699948358949 66.1571577530624 -151.075300538186 -248.831522488301 -639.593888718853 76.1920145357235 192.845671000015 185.456059757973 545.777877412695 204.807985464276 -188.598125817465 -361.597229103207 237.791037067447 88.6706048611213 -1.67573867458668 -101.324261325413 266.354817734124 664.11529839286 -127.829170960315 140.359503190461 -46.8833565357527 -148.337869337294 -690.093664539694 10.9872887023776 297.880016150804 15.5158275040067 -127.162354841272 224.150091900778 315.072479043656 -54.3933999841192 -609.217437130968 -497.593440361128 187.700712912703 20.6527597500335 167.152983928598 419.171277396816 485.910124202384 -448.179303238101 225.321369297593 -122.105927480186 -23.5350176864831 -319.802851650811 345.431533876987 251.546832269847 -532.83385641665 33.5562031825193 10.5260951309392 234.075819428605 65.2437564404718 144.410348380949 44.3891628849124 -259.491301623020 -242.033896793660 45.4061112817417 132.055082289697 124.469667769854 47.1454064444406 636.29504998809 -21.2305967857192 96.1454064444406 410.065349916627 611.106824194445 440.397637083328 699.885598321398 635.548177341234 314.740191876957 -168.489956551633 1003.25781280948 997.808433821407 -1888.88470160714 705.066246630886 -378.446688845301 -402.208761047506 -322.459848500052 727.362843575409 -232.001345071387 302.935546797629 659.266287007895 -365.765614472202 199.675738674587 -355.592543646871 -157.590548333308 -157.422611321443 125.999551642872 790.418374222234 612.816011305564 649.927520956342 -70.3788952579816 1555.07806121425 649.009819269802 -47.7477693611145 -1784.73615666280 -262.220777515919 -43.6752903174565 1027.97143890485 -1076.38916288491 411.905887103178 1036.56133699599 -275.228601472158

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 79.5248013472212 50.9604937550311 1.56051865842391 Geometric Mean NaN Harmonic Mean -480.603787896093 Quadratic Mean 510.712681022763 Winsorized Mean ( 1 / 33 ) 75.3392641828381 49.2508102632083 1.5297060856503 Winsorized Mean ( 2 / 33 ) 89.4757711664088 44.5020010898391 2.01060107355124 Winsorized Mean ( 3 / 33 ) 100.432797598222 41.6253483883227 2.41277974808247 Winsorized Mean ( 4 / 33 ) 102.253015652072 41.1913175390133 2.48239244970072 Winsorized Mean ( 5 / 33 ) 93.3129344393802 38.7800156455182 2.40621188223178 Winsorized Mean ( 6 / 33 ) 94.1206950495313 37.2721103785855 2.52523117402034 Winsorized Mean ( 7 / 33 ) 95.0359145826323 36.581423583812 2.59792827266262 Winsorized Mean ( 8 / 33 ) 98.6103379211 35.8692047108278 2.74916432399553 Winsorized Mean ( 9 / 33 ) 99.5376326721962 34.6871781788398 2.86957999751381 Winsorized Mean ( 10 / 33 ) 101.448042855756 34.257092164488 2.96137343965580 Winsorized Mean ( 11 / 33 ) 101.819410447039 33.8853376660103 3.00482206937461 Winsorized Mean ( 12 / 33 ) 102.213432711579 33.7960252071291 3.02442172075364 Winsorized Mean ( 13 / 33 ) 101.332310593095 33.4115757066497 3.03285039540734 Winsorized Mean ( 14 / 33 ) 105.912123875918 32.7545344120540 3.23351028421095 Winsorized Mean ( 15 / 33 ) 102.870431574944 32.1376227970613 3.20093468719007 Winsorized Mean ( 16 / 33 ) 109.798118535353 31.15449973585 3.52431011463191 Winsorized Mean ( 17 / 33 ) 100.81368330269 29.0728806953790 3.46761933772575 Winsorized Mean ( 18 / 33 ) 90.424905608615 27.3545558290635 3.30566162995566 Winsorized Mean ( 19 / 33 ) 83.7360020560655 25.8260667166069 3.24230565091125 Winsorized Mean ( 20 / 33 ) 80.821106300132 25.0477542738635 3.22668074017582 Winsorized Mean ( 21 / 33 ) 81.408079936509 24.5571768505925 3.31504229626236 Winsorized Mean ( 22 / 33 ) 90.644231507036 23.2811871671759 3.89345400885892 Winsorized Mean ( 23 / 33 ) 80.2998893272624 20.6706387957218 3.88473187117382 Winsorized Mean ( 24 / 33 ) 75.5823992387761 19.4006098455996 3.89587749252736 Winsorized Mean ( 25 / 33 ) 75.540896674424 19.3848414334417 3.89690557613254 Winsorized Mean ( 26 / 33 ) 74.1076573643448 18.7929004355681 3.94338583436998 Winsorized Mean ( 27 / 33 ) 73.4754484245449 18.5308185542984 3.96504062727987 Winsorized Mean ( 28 / 33 ) 70.3596706355386 16.7416651397436 4.20266861439664 Winsorized Mean ( 29 / 33 ) 66.2173078980458 16.2056975832646 4.08605106678205 Winsorized Mean ( 30 / 33 ) 63.5811358249203 15.5372089758395 4.09218514881209 Winsorized Mean ( 31 / 33 ) 68.9251752592017 14.6198995506389 4.71447666384203 Winsorized Mean ( 32 / 33 ) 76.0979965719838 13.1277101931642 5.79674562069545 Winsorized Mean ( 33 / 33 ) 81.0360691976662 12.483756907714 6.49132066546347 Trimmed Mean ( 1 / 33 ) 84.6057935439978 45.3383424193587 1.86609807569570 Trimmed Mean ( 2 / 33 ) 94.2624925624694 40.6166168418068 2.32078641432895 Trimmed Mean ( 3 / 33 ) 96.8102636281146 38.2058244278788 2.53391374424765 Trimmed Mean ( 4 / 33 ) 95.4965974631304 36.7282916056310 2.60008275060878 Trimmed Mean ( 5 / 33 ) 93.6177058881159 35.1801219838232 2.66109668213100 Trimmed Mean ( 6 / 33 ) 93.687067666104 34.1265459322974 2.74528420930636 Trimmed Mean ( 7 / 33 ) 93.6028929387328 33.3045140603056 2.81051669960543 Trimmed Mean ( 8 / 33 ) 93.358711970151 32.5166740004220 2.87110274467000 Trimmed Mean ( 9 / 33 ) 92.5563802276449 31.7567562920527 2.91454137747714 Trimmed Mean ( 10 / 33 ) 91.5843071024542 31.109210170577 2.94396118063693 Trimmed Mean ( 11 / 33 ) 90.3161125056011 30.4336474890893 2.96764009433901 Trimmed Mean ( 12 / 33 ) 88.9357167526286 29.7098412236987 2.99347667606137 Trimmed Mean ( 13 / 33 ) 87.4351529627473 28.8741597511861 3.02814536305790 Trimmed Mean ( 14 / 33 ) 85.9445585906948 27.9563420966401 3.07424191239325 Trimmed Mean ( 15 / 33 ) 83.898193204321 26.9791659798094 3.10974005894431 Trimmed Mean ( 16 / 33 ) 82.0292861409462 25.9257749266529 3.16400517913222 Trimmed Mean ( 17 / 33 ) 79.3859069034017 24.8273767555586 3.19751489192784 Trimmed Mean ( 18 / 33 ) 77.4051880765767 23.8925449391850 3.23972135549396 Trimmed Mean ( 19 / 33 ) 76.23127911877 23.078003727505 3.30320074556169 Trimmed Mean ( 20 / 33 ) 75.5685069771177 22.3605889410630 3.37954009960550 Trimmed Mean ( 21 / 33 ) 75.1123601938033 21.6201550733042 3.47418230531331 Trimmed Mean ( 22 / 33 ) 74.5727270729999 20.7946134283314 3.58615596918955 Trimmed Mean ( 23 / 33 ) 73.2081653757704 19.9996596411666 3.66047056246304 Trimmed Mean ( 24 / 33 ) 72.6096311292251 19.5018416853245 3.72321918620973 Trimmed Mean ( 25 / 33 ) 72.3593725893904 19.1000458579178 3.78843973086036 Trimmed Mean ( 26 / 33 ) 72.0913122622259 18.5815904791575 3.87971698887072 Trimmed Mean ( 27 / 33 ) 72.0913122622259 18.0262425655287 3.99924232685545 Trimmed Mean ( 28 / 33 ) 71.7881228663178 17.3527618336966 4.13698542942688 Trimmed Mean ( 29 / 33 ) 71.911308206777 16.8587359386385 4.26552195066793 Trimmed Mean ( 30 / 33 ) 72.4097220269046 16.3094011361878 4.43975357661905 Trimmed Mean ( 31 / 33 ) 73.1971364719465 15.7160581381371 4.65747427431081 Trimmed Mean ( 32 / 33 ) 73.586928324538 15.1366788413635 4.86149763073848 Trimmed Mean ( 33 / 33 ) 73.3515156763399 14.7228286388589 4.98216188448586 Median 65.2437564404718 Midrange -166.903320196445 Midmean - Weighted Average at Xnp 67.7603741709365 Midmean - Weighted Average at X(n+1)p 72.6096311292251 Midmean - Empirical Distribution Function 72.6096311292251 Midmean - Empirical Distribution Function - Averaging 72.6096311292251 Midmean - Empirical Distribution Function - Interpolation 72.3593725893904 Midmean - Closest Observation 67.7603741709365 Midmean - True Basic - Statistics Graphics Toolkit 72.6096311292251 Midmean - MS Excel (old versions) 72.6096311292251 Number of observations 99

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229288956nm00hjd97l8g0o0/1gp7c1229288880.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229288956nm00hjd97l8g0o0/1gp7c1229288880.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229288956nm00hjd97l8g0o0/2957b1229288880.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t1229288956nm00hjd97l8g0o0/2957b1229288880.ps (open in new window)

Parameters (Session):

Parameters (R input):

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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