Central tendency: Olieprijs

*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 14:10:49 -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/t12292891157ypauy66y85f2ea.htm/, Retrieved Sun, 14 Dec 2008 22:11:55 +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/t12292891157ypauy66y85f2ea.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:

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Dataseries X:

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-0.0463938195318503 1.87634877354262 -5.98794584695238 2.82602066052441 4.43195282537111 -3.94751703954726 1.22927123489519 4.41172896305817 -3.57710571577626 -2.41925073047108 2.95299649220911 0.0915362846665211 -5.20492542276921 0.880473097545242 3.2798147417068 0.438549785056356 0.484633321454211 3.85013294299182 -1.13654710715366 -2.74718495656704 -0.679311806986149 1.30631494180356 -0.148646683264623 1.55227652210596 -2.44096791490145 -2.97784333292984 5.57329514594911 1.12194310364864 -1.04087682680161 -3.83003781228902 -4.03776314089129 4.68866775559596 2.25463980919983 -0.866819907273172 1.01212753442150 -3.65709652817235 3.35325159949372 -0.86406912291703 -0.27263270045615 1.41308288422351 -1.59388394982757 2.10148634130058 -0.838594110351096 2.69058933436736 -4.06008355827142 1.76658879726948 3.64675529774814 -3.2761076168816 4.75259675413993 -8.79417950458012 -3.77773110218793 9.26414497494964 -1.42275915207198 5.16684425300134 -4.40727976334891 -4.17465289515489 5.4801443506471 -0.629986959829367 3.1075836263977 -4.37710626879047 -6.62008445210054 0.755583518994527 3.82086631697741 5.47547060988158 -6.11687310914291 1.27159723092725 5.63740269284587 -1.35094088894380 -6.05325781073222 5.59793022927748 -2.72879573562851 -12.5132392048018 3.78814017248436 4.74221909759251 3.20161366348641 -8.40610826273181 7.97141435583212 4.31336818839343 0.549701554457343 -3.28268610489192 0.808962768596913 3.51510626148411 -8.94605459505255 6.34336488158016 3.60580651526414 4.26727897887824 -9.71119107268834 -0.430286016328921 2.28286496227264 4.84295245668508 0.934367340633585 6.98749714154725 -2.24571064824758 -7.66082833027896 -23.4170823925021 -3.15861573695452 -10.2961646150048 -1.76908002260412

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 2 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean -0.308397951104134 0.50046738923133 -0.616219873142592 Geometric Mean NaN Harmonic Mean -6.31650294658261 Quadratic Mean 4.93867059456242 Winsorized Mean ( 1 / 32 ) -0.210325373873698 0.455449627819251 -0.461797224164529 Winsorized Mean ( 2 / 32 ) -0.185158896822430 0.440228013100375 -0.420597715984539 Winsorized Mean ( 3 / 32 ) -0.186969877975 0.432885941729460 -0.431914876302105 Winsorized Mean ( 4 / 32 ) -0.184554600876980 0.421703227177806 -0.43764094980275 Winsorized Mean ( 5 / 32 ) -0.178819772973712 0.41976740143083 -0.425997284124929 Winsorized Mean ( 6 / 32 ) -0.156568579594940 0.414601996014176 -0.377635855833137 Winsorized Mean ( 7 / 32 ) -0.109987926941309 0.403047107168216 -0.272890997069988 Winsorized Mean ( 8 / 32 ) -0.0254107728667865 0.387316173024765 -0.0656073116398416 Winsorized Mean ( 9 / 32 ) -0.00754072312498535 0.375289233597303 -0.0200930974030467 Winsorized Mean ( 10 / 32 ) -0.0340995494418803 0.369601940540088 -0.092260201318347 Winsorized Mean ( 11 / 32 ) -0.0369105813441091 0.367001671847056 -0.100573332972421 Winsorized Mean ( 12 / 32 ) 0.0576987371419012 0.351399951597551 0.164196770317095 Winsorized Mean ( 13 / 32 ) 0.156405126187909 0.334795675214931 0.467165909737335 Winsorized Mean ( 14 / 32 ) 0.124042063949850 0.329155047335819 0.37684995248849 Winsorized Mean ( 15 / 32 ) 0.151934336091071 0.324421807363035 0.468323437706064 Winsorized Mean ( 16 / 32 ) 0.154580631963517 0.319700945924848 0.483516342175145 Winsorized Mean ( 17 / 32 ) 0.150457474144169 0.318105606257360 0.472979636902229 Winsorized Mean ( 18 / 32 ) 0.0904146290241385 0.305882179407316 0.295586454887068 Winsorized Mean ( 19 / 32 ) 0.107517072122432 0.302082829045592 0.355919177737193 Winsorized Mean ( 20 / 32 ) 0.111513105919991 0.299806388112992 0.371950399795897 Winsorized Mean ( 21 / 32 ) 0.107066612908425 0.292558247461405 0.365966824854425 Winsorized Mean ( 22 / 32 ) 0.115831150235629 0.289039165594507 0.400745518336186 Winsorized Mean ( 23 / 32 ) 0.163642836086641 0.277410490639706 0.589894187884827 Winsorized Mean ( 24 / 32 ) 0.125616017969073 0.272313005273529 0.461292760670375 Winsorized Mean ( 25 / 32 ) 0.136854544025236 0.266185289558682 0.51413263389623 Winsorized Mean ( 26 / 32 ) 0.164067344748823 0.257584571219183 0.636945543641336 Winsorized Mean ( 27 / 32 ) 0.201709846385562 0.246520049400286 0.818228971137501 Winsorized Mean ( 28 / 32 ) 0.162796156885545 0.240545513903251 0.676779018838916 Winsorized Mean ( 29 / 32 ) 0.210395214867468 0.225612426576525 0.93255153565801 Winsorized Mean ( 30 / 32 ) 0.175584763318484 0.219883002533631 0.798537227958895 Winsorized Mean ( 31 / 32 ) 0.101506059379629 0.198260562875896 0.511983109031967 Winsorized Mean ( 32 / 32 ) 0.247924172872086 0.178625368502938 1.38795611703949 Trimmed Mean ( 1 / 32 ) -0.308397951104134 0.438472176502996 -0.703346683394464 Trimmed Mean ( 2 / 32 ) -0.167396476985966 0.418971191647078 -0.399541735382543 Trimmed Mean ( 3 / 32 ) -0.089343146393755 0.406055832833364 -0.220026752898337 Trimmed Mean ( 4 / 32 ) -0.089343146393755 0.394454992514537 -0.226497694513177 Trimmed Mean ( 5 / 32 ) -0.0175351292603477 0.384987373273731 -0.0455472840868472 Trimmed Mean ( 6 / 32 ) 0.0192227662836284 0.374651142643788 0.0513084416291373 Trimmed Mean ( 7 / 32 ) 0.0534044168711279 0.364071481816349 0.146686624848213 Trimmed Mean ( 8 / 32 ) 0.0534044168711279 0.354585027087536 0.150611032027429 Trimmed Mean ( 9 / 32 ) 0.0976408604878195 0.347020606437729 0.281369056120708 Trimmed Mean ( 10 / 32 ) 0.112324329425220 0.340598834096558 0.329784832420701 Trimmed Mean ( 11 / 32 ) 0.131205303279135 0.334118662918012 0.392690734882208 Trimmed Mean ( 12 / 32 ) 0.151445299216577 0.326960103743874 0.463191984228182 Trimmed Mean ( 13 / 32 ) 0.162078589822270 0.321295385412619 0.504453525263436 Trimmed Mean ( 14 / 32 ) 0.162689578213662 0.317321343496419 0.512696613537118 Trimmed Mean ( 15 / 32 ) 0.166667998799643 0.313407229867611 0.531793726871095 Trimmed Mean ( 16 / 32 ) 0.166667998799643 0.309387398260674 0.538703255971716 Trimmed Mean ( 17 / 32 ) 0.169422862817976 0.305204652318564 0.555112320637685 Trimmed Mean ( 18 / 32 ) 0.171186248102637 0.300311526834756 0.570028896016472 Trimmed Mean ( 19 / 32 ) 0.178515524648649 0.296202754977618 0.602680162992197 Trimmed Mean ( 20 / 32 ) 0.184829361624664 0.291671115921535 0.633691001732193 Trimmed Mean ( 21 / 32 ) 0.191244533998823 0.286356492077572 0.667854717074187 Trimmed Mean ( 22 / 32 ) 0.198519169154784 0.280888442510423 0.706754494348471 Trimmed Mean ( 23 / 32 ) 0.205602583362893 0.274575927118568 0.748800470312568 Trimmed Mean ( 24 / 32 ) 0.209178283565565 0.268645743802860 0.778639857101424 Trimmed Mean ( 25 / 32 ) 0.216286879076378 0.261934101270467 0.825730128407544 Trimmed Mean ( 26 / 32 ) 0.223055895454649 0.254434765873029 0.87667223733867 Trimmed Mean ( 27 / 32 ) 0.223055895454649 0.246419398639257 0.905188052102947 Trimmed Mean ( 28 / 32 ) 0.230390528345872 0.238246401563046 0.9670262670679 Trimmed Mean ( 29 / 32 ) 0.236305035848651 0.228669893043289 1.03338936623334 Trimmed Mean ( 30 / 32 ) 0.238609176008502 0.219429066427742 1.08740915637571 Trimmed Mean ( 31 / 32 ) 0.244328057900744 0.208204822421831 1.17349855329347 Trimmed Mean ( 32 / 32 ) 0.244328057900744 0.198609541360419 1.23019295159319 Median 0.461591553255284 Midrange -7.07646870877623 Midmean - Weighted Average at Xnp 0.145013522016011 Midmean - Weighted Average at X(n+1)p 0.209178283565565 Midmean - Empirical Distribution Function 0.209178283565565 Midmean - Empirical Distribution Function - Averaging 0.209178283565565 Midmean - Empirical Distribution Function - Interpolation 0.216286879076378 Midmean - Closest Observation 0.209178283565565 Midmean - True Basic - Statistics Graphics Toolkit 0.209178283565565 Midmean - MS Excel (old versions) 0.209178283565565 Number of observations 98

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t12292891157ypauy66y85f2ea/19lbf1229289043.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t12292891157ypauy66y85f2ea/19lbf1229289043.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t12292891157ypauy66y85f2ea/270kz1229289043.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/14/t12292891157ypauy66y85f2ea/270kz1229289043.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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