ct residuals: olieprijs

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Wed, 30 Dec 2009 07:47:19 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/30/t1262184480nl3k7t7wrwmsqkr.htm/, Retrieved Wed, 30 Dec 2009 15:48:03 +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/2009/Dec/30/t1262184480nl3k7t7wrwmsqkr.htm/}, year = {2009}, } @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 = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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0.0326799763806121 -0.948210140865705 1.31775695277446 -5.72384201804306 1.2568079894424 3.58561706384319 -3.09576079606216 -0.70454278049026 3.59924783422083 -2.34471000535835 -2.989620808753 1.82848026673821 0.867406044652756 -5.31350276508493 -0.929286157076638 1.29698608341727 0.556921007230199 -0.0233100263777321 2.55596018827231 -0.361416691444688 -1.58934021456799 -1.10837043086146 1.47786959811465 1.03724274024881 2.09592562691563 -2.52485282024109 -3.02854928038647 5.11841760936073 2.67335286331825 -0.0337627205147761 -3.22198681075089 -4.0913310871613 3.44922099409475 2.87616723248858 -0.209660432960636 0.285059576872534 -3.04811897760346 2.63115167630307 -0.534608108493555 0.93987263670119 1.47647427083872 -0.692555149561432 1.57805388587354 -0.509641129678686 3.95104867743037 -2.24260248313829 2.10977095770074 4.59638453198225 -1.05219825518802 5.79575946283695 -6.0187205018209 -3.39863275328804 etc...

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.396146270279163 0.444145097813144 0.891929849568722 Geometric Mean NaN Harmonic Mean -1.98555777448717 Quadratic Mean 4.67504730860719 Winsorized Mean ( 1 / 37 ) 0.468497749917001 0.416514556100566 1.12480522722448 Winsorized Mean ( 2 / 37 ) 0.453373178340393 0.411712373436336 1.10118910091610 Winsorized Mean ( 3 / 37 ) 0.469413425626386 0.4044558775763 1.16060478200822 Winsorized Mean ( 4 / 37 ) 0.489495588754137 0.389095512276780 1.25803452702363 Winsorized Mean ( 5 / 37 ) 0.531517225890124 0.362556279062553 1.46602681179443 Winsorized Mean ( 6 / 37 ) 0.538354363075814 0.351258116219285 1.53264604636132 Winsorized Mean ( 7 / 37 ) 0.553016054829216 0.34809388515854 1.58869798754823 Winsorized Mean ( 8 / 37 ) 0.547364784680398 0.346731093188938 1.57864349472152 Winsorized Mean ( 9 / 37 ) 0.564667863647258 0.34387425869152 1.64207657123241 Winsorized Mean ( 10 / 37 ) 0.574988070445483 0.340063893894086 1.69082363864411 Winsorized Mean ( 11 / 37 ) 0.557104254216432 0.33696927309103 1.65327909309385 Winsorized Mean ( 12 / 37 ) 0.604954596045224 0.327820365344289 1.84538442390508 Winsorized Mean ( 13 / 37 ) 0.606119716086938 0.317632464239909 1.90824233768855 Winsorized Mean ( 14 / 37 ) 0.614733687129917 0.313025646598264 1.96384447667594 Winsorized Mean ( 15 / 37 ) 0.642447427616551 0.308440742509798 2.08288769631704 Winsorized Mean ( 16 / 37 ) 0.695711059121004 0.301146928639092 2.31020473051138 Winsorized Mean ( 17 / 37 ) 0.723234859753027 0.291770483113748 2.47878007410047 Winsorized Mean ( 18 / 37 ) 0.670011571723155 0.282746864051961 2.36965164572091 Winsorized Mean ( 19 / 37 ) 0.653571157438686 0.275253353357472 2.37443485961783 Winsorized Mean ( 20 / 37 ) 0.662752159703519 0.270700613916844 2.44828465703853 Winsorized Mean ( 21 / 37 ) 0.644116064597014 0.266742799980841 2.41474583247712 Winsorized Mean ( 22 / 37 ) 0.639439018469385 0.265633641777218 2.40722151829576 Winsorized Mean ( 23 / 37 ) 0.642090029088934 0.264954729558446 2.42339523494822 Winsorized Mean ( 24 / 37 ) 0.639819734189732 0.262622606692152 2.43627059470830 Winsorized Mean ( 25 / 37 ) 0.659337121369108 0.258981380125055 2.54588619865541 Winsorized Mean ( 26 / 37 ) 0.719227170935112 0.245981304351063 2.92390989970781 Winsorized Mean ( 27 / 37 ) 0.71036746528952 0.234524214631329 3.02897279245222 Winsorized Mean ( 28 / 37 ) 0.679914430569182 0.230123013875516 2.95456946751523 Winsorized Mean ( 29 / 37 ) 0.699239008602617 0.227002919921529 3.08030843323219 Winsorized Mean ( 30 / 37 ) 0.673642059949347 0.224056111693792 3.00657748122482 Winsorized Mean ( 31 / 37 ) 0.778256436434149 0.209254454167156 3.71918695605142 Winsorized Mean ( 32 / 37 ) 0.812878075571483 0.197705820571802 4.11155358613363 Winsorized Mean ( 33 / 37 ) 0.805250956752924 0.189398337009186 4.25162633140685 Winsorized Mean ( 34 / 37 ) 0.831006553893117 0.170889397221445 4.86283272926678 Winsorized Mean ( 35 / 37 ) 0.820405830811375 0.165906381839850 4.94499260193207 Winsorized Mean ( 36 / 37 ) 0.834729550243109 0.163549231576076 5.10384269127446 Winsorized Mean ( 37 / 37 ) 0.805789923324524 0.152799820364674 5.27350046224803 Trimmed Mean ( 1 / 37 ) 0.489667490666626 0.401185005382025 1.22055282250727 Trimmed Mean ( 2 / 37 ) 0.511628623593806 0.38383939847134 1.33292368014173 Trimmed Mean ( 3 / 37 ) 0.542420787513467 0.3670796453723 1.47766511805178 Trimmed Mean ( 4 / 37 ) 0.56864673304572 0.351205519933696 1.61912811949275 Trimmed Mean ( 5 / 37 ) 0.590393705858505 0.338528638427402 1.74399929235268 Trimmed Mean ( 6 / 37 ) 0.603596310457476 0.331765630728706 1.81934550945409 Trimmed Mean ( 7 / 37 ) 0.603596310457476 0.326830019856815 1.84682028511919 Trimmed Mean ( 8 / 37 ) 0.626559042167585 0.321906195715367 1.94640255610860 Trimmed Mean ( 9 / 37 ) 0.63837431445398 0.31656743990095 2.016550769257 Trimmed Mean ( 10 / 37 ) 0.648363833427785 0.311012529991299 2.08468717786361 Trimmed Mean ( 11 / 37 ) 0.657515192631196 0.305332634111675 2.15343896843568 Trimmed Mean ( 12 / 37 ) 0.66916160241598 0.299332777890041 2.23551061508471 Trimmed Mean ( 13 / 37 ) 0.676148835462209 0.293931281998461 2.30036364576449 Trimmed Mean ( 14 / 37 ) 0.676148835462209 0.289270913197531 2.33742420898189 Trimmed Mean ( 15 / 37 ) 0.690069642869842 0.284553881578006 2.42509305809863 Trimmed Mean ( 16 / 37 ) 0.694530457969518 0.279742350609999 2.4827504897097 Trimmed Mean ( 17 / 37 ) 0.694424088872265 0.275181844162577 2.52350983032878 Trimmed Mean ( 18 / 37 ) 0.69191585705441 0.271154003049286 2.55174494668495 Trimmed Mean ( 19 / 37 ) 0.693766219057279 0.267641585456256 2.59214657495993 Trimmed Mean ( 20 / 37 ) 0.697073596254954 0.264466643489679 2.63577132850086 Trimmed Mean ( 21 / 37 ) 0.699834233542787 0.261256269804337 2.67872703712303 Trimmed Mean ( 22 / 37 ) 0.704229909984138 0.257898491139144 2.73064765471693 Trimmed Mean ( 23 / 37 ) 0.709259133031787 0.253958547199294 2.79281457881075 Trimmed Mean ( 24 / 37 ) 0.714404592340039 0.249255479936068 2.86615400601535 Trimmed Mean ( 25 / 37 ) 0.720059591830943 0.243869090784293 2.95264803553088 Trimmed Mean ( 26 / 37 ) 0.7246292150318 0.237854890690778 3.04651803848696 Trimmed Mean ( 27 / 37 ) 0.725033821573455 0.232555912772665 3.11767528474845 Trimmed Mean ( 28 / 37 ) 0.725033821573455 0.227821164731971 3.18246911969943 Trimmed Mean ( 29 / 37 ) 0.729586926687705 0.222593389557716 3.27766663752848 Trimmed Mean ( 30 / 37 ) 0.731864559485166 0.216534832825031 3.37989297119944 Trimmed Mean ( 31 / 37 ) 0.731864559485166 0.209370991285163 3.49553944886457 Trimmed Mean ( 32 / 37 ) 0.733061570880982 0.203092732235252 3.6094918947264 Trimmed Mean ( 33 / 37 ) 0.726909048644423 0.197233186173609 3.68553113574194 Trimmed Mean ( 34 / 37 ) 0.720780823274413 0.191258433012041 3.76862244410962 Trimmed Mean ( 35 / 37 ) 0.712003882027156 0.187206561659475 3.80330622877566 Trimmed Mean ( 36 / 37 ) 0.703188778499648 0.182713706521001 3.84858252776358 Trimmed Mean ( 37 / 37 ) 0.692227047521026 0.176978105784082 3.91137109561767 Median 0.867406044652756 Midrange -4.70076024083755 Midmean - Weighted Average at Xnp 0.671293664311786 Midmean - Weighted Average at X(n+1)p 0.725033821573454 Midmean - Empirical Distribution Function 0.725033821573454 Midmean - Empirical Distribution Function - Averaging 0.725033821573454 Midmean - Empirical Distribution Function - Interpolation 0.726130094669425 Midmean - Closest Observation 0.671293664311786 Midmean - True Basic - Statistics Graphics Toolkit 0.725033821573454 Midmean - MS Excel (old versions) 0.725033821573454 Number of observations 111

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/30/t1262184480nl3k7t7wrwmsqkr/1bi1q1262184436.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262184480nl3k7t7wrwmsqkr/1bi1q1262184436.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262184480nl3k7t7wrwmsqkr/2xlry1262184436.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/30/t1262184480nl3k7t7wrwmsqkr/2xlry1262184436.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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