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central tendency G29

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

Title produced by software: Central Tendency

Date of computation: Fri, 07 Dec 2007 09:01:07 -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/Dec/07/t1197042458qv8uudj3nprrclv.htm/, Retrieved Fri, 07 Dec 2007 16:47:38 +0100

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Dataseries X:

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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 3 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.0030518140314442 0.00259591002115397 1.17562396484281 Geometric Mean NaN Harmonic Mean -0.0324252020577491 Quadratic Mean 0.0295275027751629 Winsorized Mean ( 1 / 43 ) 0.00298536046912657 0.00257701185068637 1.15845818416840 Winsorized Mean ( 2 / 43 ) 0.00307545592928437 0.00255288239756587 1.20469941436267 Winsorized Mean ( 3 / 43 ) 0.00306816589077450 0.00254598960282699 1.20509757281322 Winsorized Mean ( 4 / 43 ) 0.00301808383583737 0.00253569051321818 1.19024140371411 Winsorized Mean ( 5 / 43 ) 0.00306895232392878 0.00252446675067401 1.21568340050801 Winsorized Mean ( 6 / 43 ) 0.00304472312014 0.00251070659064326 1.21269571342461 Winsorized Mean ( 7 / 43 ) 0.00308320043175888 0.00246216702482896 1.25223041356143 Winsorized Mean ( 8 / 43 ) 0.00314046311111058 0.00233616013018211 1.34428418263682 Winsorized Mean ( 9 / 43 ) 0.0030437035913059 0.00231584708756553 1.31429385284048 Winsorized Mean ( 10 / 43 ) 0.00290378823967222 0.00225301745993331 1.28884409078578 Winsorized Mean ( 11 / 43 ) 0.00314560823791623 0.00219648848369139 1.43210777624008 Winsorized Mean ( 12 / 43 ) 0.00299594078689828 0.00213540514105550 1.40298472139925 Winsorized Mean ( 13 / 43 ) 0.00301114605401491 0.00212444157589191 1.41738237859082 Winsorized Mean ( 14 / 43 ) 0.00304762677641743 0.00211035147675991 1.44413232107504 Winsorized Mean ( 15 / 43 ) 0.00302695166798667 0.00208835817169026 1.44944086173530 Winsorized Mean ( 16 / 43 ) 0.00285233070454065 0.00205232005127801 1.38980794090301 Winsorized Mean ( 17 / 43 ) 0.00285332608115084 0.00202697159000721 1.40767936522519 Winsorized Mean ( 18 / 43 ) 0.00290684157802036 0.00201646072691101 1.44155625707291 Winsorized Mean ( 19 / 43 ) 0.00294715542970888 0.00200590815713775 1.46923747192604 Winsorized Mean ( 20 / 43 ) 0.00294324001577047 0.00197980975384767 1.48662769745952 Winsorized Mean ( 21 / 43 ) 0.00303681118050762 0.00195769519585754 1.55121756795107 Winsorized Mean ( 22 / 43 ) 0.002521853021238 0.00187146224159084 1.34753080516030 Winsorized Mean ( 23 / 43 ) 0.00242938541710560 0.00184302872253558 1.31814843002736 Winsorized Mean ( 24 / 43 ) 0.00267715515924160 0.00179595623084172 1.49065724056476 Winsorized Mean ( 25 / 43 ) 0.00264647779684294 0.00176767978925474 1.49714773735050 Winsorized Mean ( 26 / 43 ) 0.00242963367468858 0.00173170937025655 1.40302623316558 Winsorized Mean ( 27 / 43 ) 0.00239421009740117 0.00170296802227847 1.40590431885964 Winsorized Mean ( 28 / 43 ) 0.00240277464147421 0.00169277678886261 1.41942792297421 Winsorized Mean ( 29 / 43 ) 0.00161232271331084 0.00156601507855556 1.02957036326751 Winsorized Mean ( 30 / 43 ) 0.00130303974403709 0.00152027016566389 0.857110646164699 Winsorized Mean ( 31 / 43 ) 0.00138827938197310 0.00148133710420432 0.937179915383812 Winsorized Mean ( 32 / 43 ) 0.00145306086175759 0.00147082094581273 0.987925053620085 Winsorized Mean ( 33 / 43 ) 0.00100132131186386 0.0013732555470258 0.729158760022867 Winsorized Mean ( 34 / 43 ) 0.00069424638833589 0.00132004803507878 0.525925095062512 Winsorized Mean ( 35 / 43 ) 0.00107085418989960 0.00125758515780311 0.851516243854439 Winsorized Mean ( 36 / 43 ) 0.00113815507281201 0.00120991521895802 0.940689938417489 Winsorized Mean ( 37 / 43 ) 0.00125489889796166 0.00117728838879627 1.06592310762934 Winsorized Mean ( 38 / 43 ) 0.000975661354281442 0.00114907673205873 0.849082856750056 Winsorized Mean ( 39 / 43 ) 0.00106103237056077 0.00113161522713690 0.937626452098283 Winsorized Mean ( 40 / 43 ) 0.00105361018814858 0.00112426596283770 0.937153861252921 Winsorized Mean ( 41 / 43 ) 0.00107226867300680 0.00110679810849521 0.96880240829526 Winsorized Mean ( 42 / 43 ) 0.00129439482698546 0.00108306387199439 1.19512326138431 Winsorized Mean ( 43 / 43 ) 0.00130100800308676 0.00107297646240186 1.21252240722453 Trimmed Mean ( 1 / 43 ) 0.00297904051955785 0.00252512753124173 1.17975844098968 Trimmed Mean ( 2 / 43 ) 0.00297251833160294 0.00246798959581093 1.20442903675460 Trimmed Mean ( 3 / 43 ) 0.00291853885964804 0.00241899275626164 1.20650996250126 Trimmed Mean ( 4 / 43 ) 0.00286536561718162 0.00236769394476803 1.21019256881292 Trimmed Mean ( 5 / 43 ) 0.00282397769657953 0.00231425135832764 1.22025539119495 Trimmed Mean ( 6 / 43 ) 0.00276995765054867 0.00225801819357260 1.22672069624297 Trimmed Mean ( 7 / 43 ) 0.00271858845405986 0.00219878613204939 1.23640422068971 Trimmed Mean ( 8 / 43 ) 0.00265912581800022 0.002143223468435 1.24071327939589 Trimmed Mean ( 9 / 43 ) 0.00258920181933892 0.00210556904291696 1.22969219558431 Trimmed Mean ( 10 / 43 ) 0.0025294355312821 0.00206716573170051 1.22362493364347 Trimmed Mean ( 11 / 43 ) 0.00248430328886871 0.00203458297280991 1.22103808105584 Trimmed Mean ( 12 / 43 ) 0.00241044325559847 0.00200664983371455 1.20122764575045 Trimmed Mean ( 13 / 43 ) 0.00234933550354534 0.00198405564339835 1.18410766923922 Trimmed Mean ( 14 / 43 ) 0.00228431375106203 0.00196001127582144 1.16545949466780 Trimmed Mean ( 15 / 43 ) 0.00221326946515233 0.00193465521395688 1.14401235382175 Trimmed Mean ( 16 / 43 ) 0.00214112856881857 0.00190877024834617 1.12173194792497 Trimmed Mean ( 17 / 43 ) 0.00208076996651057 0.00188399909009978 1.10444319078751 Trimmed Mean ( 18 / 43 ) 0.00201773407859875 0.00185890660982034 1.08544133844021 Trimmed Mean ( 19 / 43 ) 0.00194771279201426 0.00183148372086503 1.06346170038265 Trimmed Mean ( 20 / 43 ) 0.00187146908990746 0.00180135149317554 1.03892499437093 Trimmed Mean ( 21 / 43 ) 0.00179201021092107 0.00177000428432695 1.01243269679569 Trimmed Mean ( 22 / 43 ) 0.00170204980471565 0.00173661275410728 0.980097491907803 Trimmed Mean ( 23 / 43 ) 0.00164413380530087 0.00170902972890061 0.962027621578306 Trimmed Mean ( 24 / 43 ) 0.00158976050528863 0.0016805381821826 0.945982972683147 Trimmed Mean ( 25 / 43 ) 0.00151577637535196 0.00165301963812679 0.916974209132593 Trimmed Mean ( 26 / 43 ) 0.00144000469567801 0.0016243285040888 0.886523072182258 Trimmed Mean ( 27 / 43 ) 0.00137453693245116 0.00159528366295350 0.8616254051686 Trimmed Mean ( 28 / 43 ) 0.00130780033261425 0.00156472608495004 0.835801451252734 Trimmed Mean ( 29 / 43 ) 0.00123674817675764 0.00152988405634848 0.808393401856549 Trimmed Mean ( 30 / 43 ) 0.00121253572537565 0.00150564547408793 0.80532618484452 Trimmed Mean ( 31 / 43 ) 0.00120672725850634 0.00148252563505731 0.813967212418346 Trimmed Mean ( 32 / 43 ) 0.00119510431859209 0.00145968183794784 0.818743021610981 Trimmed Mean ( 33 / 43 ) 0.00117859817074072 0.00143327647122265 0.822310415613901 Trimmed Mean ( 34 / 43 ) 0.00118995868479692 0.00141454617821091 0.841230002333296 Trimmed Mean ( 35 / 43 ) 0.00122183649448957 0.00139837863772126 0.87375225960304 Trimmed Mean ( 36 / 43 ) 0.00123159926004953 0.00138653755265606 0.888255249697544 Trimmed Mean ( 37 / 43 ) 0.00123768729043015 0.0013774963757117 0.898504934207676 Trimmed Mean ( 38 / 43 ) 0.00123655506331564 0.00136955031685066 0.902891298042373 Trimmed Mean ( 39 / 43 ) 0.00125392105323587 0.00136214815243693 0.920546748892598 Trimmed Mean ( 40 / 43 ) 0.00126694179633639 0.00135375693806662 0.935870953426682 Trimmed Mean ( 41 / 43 ) 0.00128157997583438 0.00134244145152559 0.954663590265301 Trimmed Mean ( 42 / 43 ) 0.00129621474985322 0.00132911871629888 0.975243771649467 Trimmed Mean ( 43 / 43 ) 0.00129634474434378 0.00131435030022270 0.986300793726088 Median 0.00124730647435565 Midrange 0.0076729320362272 Midmean - Weighted Average at Xnp 0.000866951651125913 Midmean - Weighted Average at X(n+1)p 0.00119510431859209 Midmean - Empirical Distribution Function 0.00119510431859209 Midmean - Empirical Distribution Function - Averaging 0.00119510431859209 Midmean - Empirical Distribution Function - Interpolation 0.00119510431859209 Midmean - Closest Observation 0.00088770261114249 Midmean - True Basic - Statistics Graphics Toolkit 0.00119510431859209 Midmean - MS Excel (old versions) 0.00119510431859209 Number of observations 129

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/07/t1197042458qv8uudj3nprrclv/1pqw01197043261.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/07/t1197042458qv8uudj3nprrclv/1pqw01197043261.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/07/t1197042458qv8uudj3nprrclv/2nmnx1197043261.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/07/t1197042458qv8uudj3nprrclv/2nmnx1197043261.ps (open in new window)

Parameters:

par1 = 12 ;

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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