Central tendency multiple regression

*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, 07 Dec 2008 08:13:41 -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/07/t1228663859h1p2k25p4rtx5y9.htm/, Retrieved Sun, 07 Dec 2008 15:31:01 +0000

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/07/t1228663859h1p2k25p4rtx5y9.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}, }

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2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

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Dataseries X:

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13.33965673 6.784101174 -1.549232159 -0.215898826 -5.66034327 -9.104787715 -19.00880759 -0.883807588 -14.75880759 -17.25880759 -19.00880759 -23.63380759 -9.21928636 -10.77484192 -12.10817525 -10.77484192 -8.21928636 -13.6637308 -19.56775068 -8.442750678 0.682249322 4.182249322 -8.567750678 -9.192750678 3.221770551 7.666214995 13.33288166 10.666215 14.22177055 14.77732611 16.87330623 9.998306233 14.12330623 23.62330623 32.87330623 35.24830623 -47.9929991 -44.54855465 -40.88188799 -35.54855465 -33.9929991 -28.43744354 -25.34146341 -29.21646341 -25.09146341 -24.59146341 -23.34146341 -15.96646341 -1.551942186 2.892502258 7.559168925 2.892502258 5.448057814 11.00361337 3.099593496 5.224593496 13.3495935 24.8495935 27.0995935 27.4745935 30.88911472 34.33355917 40.00022584 35.33355917 38.88911472 40.44467028 33.54065041 26.66565041 31.79065041 29.29065041 31.54065041 32.91565041 36.33017164 38.77461608 39.44128275 39.77461608 48.33017164 47.88572719 35.98170732 27.10670732 27.23170732 8.731707317 5.981707317 -3.643292683 7.771228546 1.21567299 -5.117660343 -4.78432701 -10.22877145 -22.6732159 -25.57723577 -30.45223577 -47.32723577 -48.82723577 -46.57723577 -43.20223577 -32.78771454 -36.3432701 -40.67660343 -37.3432701 -48.78771454 -40.23215899

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 1 seconds R Server 'George Udny Yule' @ 72.249.76.132 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 6.86274464271858e-10 2.62177885915745 2.61759096071284e-10 Geometric Mean NaN Harmonic Mean -29.2551922831889 Quadratic Mean 26.3485514406166 Winsorized Mean ( 1 / 34 ) -0.00396983480392173 2.62091565074331 -0.00151467476749809 Winsorized Mean ( 2 / 34 ) -0.134290255784314 2.59354737815163 -0.0517786013533401 Winsorized Mean ( 3 / 34 ) -0.127780876666667 2.58797473333761 -0.0493748547930653 Winsorized Mean ( 4 / 34 ) -0.107216553529412 2.58134217208642 -0.0415351961815864 Winsorized Mean ( 5 / 34 ) -0.0241112697058824 2.5614247791053 -0.0094132257572305 Winsorized Mean ( 6 / 34 ) 0.0226034861764707 2.54301023201155 0.00888847629944103 Winsorized Mean ( 7 / 34 ) 0.173985289901961 2.5155505757135 0.0691639005717913 Winsorized Mean ( 8 / 34 ) -0.00163509284313664 2.48449728958510 -0.0006581181835017 Winsorized Mean ( 9 / 34 ) 0.00683374127450966 2.47372150340179 0.0027625346123693 Winsorized Mean ( 10 / 34 ) 0.226514205980393 2.41999700627032 0.093601027353953 Winsorized Mean ( 11 / 34 ) 0.325163398725489 2.4022369949555 0.135358584273036 Winsorized Mean ( 12 / 34 ) 0.311042032843137 2.37279168558793 0.131086953284762 Winsorized Mean ( 13 / 34 ) 0.408242113921569 2.32916930530302 0.175273696502908 Winsorized Mean ( 14 / 34 ) 0.487889014313725 2.29314108189071 0.21276013855696 Winsorized Mean ( 15 / 34 ) 0.825114689313725 2.2438821351202 0.367717482304179 Winsorized Mean ( 16 / 34 ) 0.84913297009804 2.19348077068130 0.387116669290107 Winsorized Mean ( 17 / 34 ) 0.937302948431372 2.17015134517994 0.431906719553541 Winsorized Mean ( 18 / 34 ) 1.32706860960784 2.08801668608698 0.63556417841412 Winsorized Mean ( 19 / 34 ) 1.07323383460784 2.04114964753912 0.525798701678621 Winsorized Mean ( 20 / 34 ) 0.766163852254903 1.98677456236422 0.385632002124683 Winsorized Mean ( 21 / 34 ) 0.819099050490197 1.96690592851886 0.416440379081578 Winsorized Mean ( 22 / 34 ) 0.998691482254903 1.93710015887107 0.515560064192517 Winsorized Mean ( 23 / 34 ) 1.06300813205882 1.92860658899549 0.551179353075055 Winsorized Mean ( 24 / 34 ) 1.11813858382353 1.89534826172011 0.589938327644743 Winsorized Mean ( 25 / 34 ) 1.43417003235294 1.74327207846315 0.822688580899713 Winsorized Mean ( 26 / 34 ) 1.26406269235294 1.68496470889469 0.750201286519613 Winsorized Mean ( 27 / 34 ) -0.522702013529412 1.46143979032819 -0.357662366242287 Winsorized Mean ( 28 / 34 ) -0.617676948431373 1.33488871029419 -0.462717935711093 Winsorized Mean ( 29 / 34 ) -0.408197830980392 1.27194069353880 -0.320925207483301 Winsorized Mean ( 30 / 34 ) -0.0819650368627449 1.22606502209224 -0.066852112559964 Winsorized Mean ( 31 / 34 ) 0.0157044323529409 1.16003387175569 0.0135379084484598 Winsorized Mean ( 32 / 34 ) 0.500604441764706 1.10356235911769 0.453625875899702 Winsorized Mean ( 33 / 34 ) 0.929785055294118 1.05520573100215 0.881141021107877 Winsorized Mean ( 34 / 34 ) 0.153362291960785 0.966800091678013 0.158628751983881 Trimmed Mean ( 1 / 34 ) 0.00497064199999993 2.58381132847614 0.00192376352917822 Trimmed Mean ( 2 / 34 ) 0.0142760362244898 2.54188353746335 0.0056163219180122 Trimmed Mean ( 3 / 34 ) 0.0932018788541667 2.51050396396979 0.0371247686487574 Trimmed Mean ( 4 / 34 ) 0.173131811702128 2.47703055923430 0.0698949034184083 Trimmed Mean ( 5 / 34 ) 0.250837065108696 2.44098250923961 0.102760697448354 Trimmed Mean ( 6 / 34 ) 0.313158687666667 2.4052704811483 0.130196869799509 Trimmed Mean ( 7 / 34 ) 0.369288669772727 2.36877355108146 0.155898679974761 Trimmed Mean ( 8 / 34 ) 0.402379940116279 2.33283139231893 0.172485650459418 Trimmed Mean ( 9 / 34 ) 0.463703650476190 2.29754423974409 0.201825776607392 Trimmed Mean ( 10 / 34 ) 0.526848272073171 2.25868307119857 0.233254624693140 Trimmed Mean ( 11 / 34 ) 0.5651408655 2.22327246968558 0.254193254855498 Trimmed Mean ( 12 / 34 ) 0.59366965525641 2.18500656113820 0.271701543517178 Trimmed Mean ( 13 / 34 ) 0.625279323552632 2.14514756766067 0.291485459079401 Trimmed Mean ( 14 / 34 ) 0.648291584864865 2.10549100760839 0.307905178660086 Trimmed Mean ( 15 / 34 ) 0.664522797361111 2.06451108306843 0.321879016688759 Trimmed Mean ( 16 / 34 ) 0.648922442142857 2.02380868538247 0.320644163072272 Trimmed Mean ( 17 / 34 ) 0.630152705147059 1.98330107524365 0.317729220748617 Trimmed Mean ( 18 / 34 ) 0.602229955757576 1.93847762917824 0.310671604713268 Trimmed Mean ( 19 / 34 ) 0.53805153328125 1.89752281821677 0.28355471044448 Trimmed Mean ( 20 / 34 ) 0.49171146983871 1.85550162974023 0.265001906739123 Trimmed Mean ( 21 / 34 ) 0.468383017333333 1.81319617966971 0.258318996358494 Trimmed Mean ( 22 / 34 ) 0.439012709137931 1.76458292664628 0.248791203013795 Trimmed Mean ( 23 / 34 ) 0.392675667857143 1.70972013470395 0.229672482581564 Trimmed Mean ( 24 / 34 ) 0.337624209444445 1.64307706109094 0.205482881746444 Trimmed Mean ( 25 / 34 ) 0.273832169230769 1.56600292921420 0.174860572813983 Trimmed Mean ( 26 / 34 ) 0.1791485996 1.49905653844930 0.119507566929610 Trimmed Mean ( 27 / 34 ) 0.09047773625 1.42597859698009 0.0634495752191595 Trimmed Mean ( 28 / 34 ) 0.140835493478261 1.37977479500569 0.102071362651381 Trimmed Mean ( 29 / 34 ) 0.203634413181818 1.34519611650933 0.151378977892258 Trimmed Mean ( 30 / 34 ) 0.254871596190476 1.31231614253532 0.194215088826140 Trimmed Mean ( 31 / 34 ) 0.254871596190476 1.27797069894378 0.199434616459612 Trimmed Mean ( 32 / 34 ) 0.306690676315789 1.24591443976755 0.246157092755911 Trimmed Mean ( 33 / 34 ) 0.289521228333333 1.21450885359953 0.238385440727961 Trimmed Mean ( 34 / 34 ) 0.231315425882353 1.18135977106113 0.195804387070484 Median 0.948961156 Midrange -0.248532064999999 Midmean - Weighted Average at Xnp -0.208045503921570 Midmean - Weighted Average at X(n+1)p 0.273832169230768 Midmean - Empirical Distribution Function 0.273832169230768 Midmean - Empirical Distribution Function - Averaging 0.273832169230768 Midmean - Empirical Distribution Function - Interpolation 0.179148599599998 Midmean - Closest Observation 0.273832169230768 Midmean - True Basic - Statistics Graphics Toolkit 0.273832169230768 Midmean - MS Excel (old versions) 0.273832169230768 Number of observations 102

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663859h1p2k25p4rtx5y9/11rkf1228662819.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663859h1p2k25p4rtx5y9/11rkf1228662819.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663859h1p2k25p4rtx5y9/2kky21228662819.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/07/t1228663859h1p2k25p4rtx5y9/2kky21228662819.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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