Central Tendency Estimated ARIMA Residuals Voeding

*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: Sat, 13 Dec 2008 07:45:10 -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/13/t1229179607bed21hsd45hpama.htm/, Retrieved Sat, 13 Dec 2008 15:46:48 +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/13/t1229179607bed21hsd45hpama.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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0.0999999451304278 -2.70419114542474e-05 -1.90964567729398e-11 0.0988083588488808 -0.124314836821806 0.0255064765015352 -0.19761672216071 0.248629656546156 -0.149821379403173 -0.66615244129388 -0.315499620574354 0.127522373278691 0.197747936923039 0.249322182371023 0.522557733302254 0.529723643874334 -0.561333540761254 0.499283096794017 -2.17247271013605 0.703365703816985 0.371710162537639 0.100513144713076 -0.176667786867057 -0.36823811621575 -1.52752728986394 0.274796471801878 0.145382928925343 -0.135531384094193 -0.0106198974585823 0.148353762766291 0.38749313475121 0.559963830780504 0.765680604389601 0.0423603234888219 0.257314637565983 -0.223821493381720 0.882004306273416 -0.103064851510211 0.239429006837156 0.130504250908032 0.163763644931976 0.113614917937952 -0.159073482923162 0.0220445885892957 -0.0519195045833385 -0.732256969062064 -0.0535570694882068 -0.205668400332286 0.077144812709605 -0.0173663588126090 0.257797790611647 -0.596306135749927 0.806233316640942 0.296464249850388 0.294267895839994 -0.0226214371300699 -0.137232652826782 -0.262469893185184 0.106227189437803 0.0382289505855198 0.172688400861091 -0.0476549189955904 0.153824531808823 0.53725071226205 0.843320502377011 -0.0331012799234713 -0.0191432636050592 1.17468712358173 0.209596014742687 -0.171476412377316 0.00992105368787577 0.5 0.440145910043142 -0.446938015789598 0.246658621236847 0.179359226155270 -0.0268860227178038 0.445902200841559 0.267457509182037 0.316015030441264 1.11583545858674 0.699674961054811 -0.262114147794961 0.223040886348031 -0.0499269090658458 0.244200932108981 0.405668400332289 -0.367299395081588 0.132764308746147 -0.0948266849455166 -0.141700996907417 0.349356187728176 0.320489501725021 -0.957150396262378

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 'Gwilym Jenkins' @ 72.249.127.135 Central Tendency - Ungrouped Data Measure Value S.E. Value/S.E. Arithmetic Mean 0.0764065855753148 0.0483586153517502 1.57999944827927 Geometric Mean NaN Harmonic Mean -1.79506566715043e-09 Quadratic Mean 0.472571311488501 Winsorized Mean ( 1 / 31 ) 0.0826416255250714 0.0451608597711203 1.82993915403532 Winsorized Mean ( 2 / 31 ) 0.0898021732120552 0.0399389650680677 2.2484852338814 Winsorized Mean ( 3 / 31 ) 0.095745033530265 0.0378150366905497 2.53193020315627 Winsorized Mean ( 4 / 31 ) 0.09697981404227 0.0368392221073959 2.63251525125988 Winsorized Mean ( 5 / 31 ) 0.09853798389826 0.035594376525896 2.76835819350764 Winsorized Mean ( 6 / 31 ) 0.0967927303503487 0.0343512464546773 2.81773560904278 Winsorized Mean ( 7 / 31 ) 0.105036703493608 0.0326437244067203 3.21766910493780 Winsorized Mean ( 8 / 31 ) 0.0998442583276115 0.0292795912554095 3.41002910377599 Winsorized Mean ( 9 / 31 ) 0.0977594755993282 0.0289025371525519 3.38238387458303 Winsorized Mean ( 10 / 31 ) 0.102469337952468 0.0278490738110999 3.6794522736202 Winsorized Mean ( 11 / 31 ) 0.107836380345958 0.0267465842512742 4.03178137936699 Winsorized Mean ( 12 / 31 ) 0.105002084016762 0.0262668423811776 3.99751452774564 Winsorized Mean ( 13 / 31 ) 0.110198730460319 0.0254791206449243 4.32506019324775 Winsorized Mean ( 14 / 31 ) 0.104952036410933 0.0238335059144696 4.40355006047244 Winsorized Mean ( 15 / 31 ) 0.105318321630054 0.0235135024040200 4.47905717406216 Winsorized Mean ( 16 / 31 ) 0.103015585559041 0.0221431650387406 4.65225207773188 Winsorized Mean ( 17 / 31 ) 0.100667435042522 0.0215386675333325 4.67380049795249 Winsorized Mean ( 18 / 31 ) 0.100020192811996 0.0207893505890529 4.81112636893352 Winsorized Mean ( 19 / 31 ) 0.0973719422322085 0.0199141633520109 4.88958237968738 Winsorized Mean ( 20 / 31 ) 0.0929578351029746 0.0188667492966736 4.92707215436228 Winsorized Mean ( 21 / 31 ) 0.092956466259724 0.0186043390181476 4.9964938915083 Winsorized Mean ( 22 / 31 ) 0.0887789209311887 0.0179717113992167 4.93992580667961 Winsorized Mean ( 23 / 31 ) 0.0909859894952934 0.0175395119407594 5.18748696101712 Winsorized Mean ( 24 / 31 ) 0.0914400902459689 0.0162190813011887 5.63780947563702 Winsorized Mean ( 25 / 31 ) 0.0916792402313022 0.0157017486058371 5.83879175069845 Winsorized Mean ( 26 / 31 ) 0.100422403200025 0.0139707464940044 7.18804848710994 Winsorized Mean ( 27 / 31 ) 0.100753989585115 0.0138982854229625 7.24938267699206 Winsorized Mean ( 28 / 31 ) 0.0989667973407634 0.0135418192846820 7.30823497642678 Winsorized Mean ( 29 / 31 ) 0.0994540788632768 0.0134336071534655 7.40337853616784 Winsorized Mean ( 30 / 31 ) 0.103469803468429 0.0128185867426103 8.07185733856955 Winsorized Mean ( 31 / 31 ) 0.104709001451534 0.0124876813200116 8.38498347036904 Trimmed Mean ( 1 / 31 ) 0.0889130938112382 0.0411341621493595 2.16153895364130 Trimmed Mean ( 2 / 31 ) 0.0954632940212346 0.036195948596836 2.63740274041552 Trimmed Mean ( 3 / 31 ) 0.0954632940212346 0.0338392154468009 2.82108473145052 Trimmed Mean ( 4 / 31 ) 0.0994858025416875 0.0320776885123916 3.10140185142256 Trimmed Mean ( 5 / 31 ) 0.100186882657596 0.0303831307205082 3.29745093022857 Trimmed Mean ( 6 / 31 ) 0.100186882657596 0.0287972763556992 3.47904021964106 Trimmed Mean ( 7 / 31 ) 0.101303643894283 0.0272894021984563 3.71219725362887 Trimmed Mean ( 8 / 31 ) 0.100660955977916 0.0259611352270218 3.87737112024063 Trimmed Mean ( 9 / 31 ) 0.100787221733062 0.0251908520520976 4.00094532430353 Trimmed Mean ( 10 / 31 ) 0.101214561277463 0.0243671529250362 4.15372947298528 Trimmed Mean ( 11 / 31 ) 0.101050743211559 0.023612551393443 4.27953513060939 Trimmed Mean ( 12 / 31 ) 0.101050743211559 0.0229336308401178 4.40622524693259 Trimmed Mean ( 13 / 31 ) 0.0996717620427962 0.0222189542039587 4.48588899044753 Trimmed Mean ( 14 / 31 ) 0.0985184578105734 0.0215094166076099 4.58024778671675 Trimmed Mean ( 15 / 31 ) 0.0978435064841965 0.0209612483572709 4.66782821406994 Trimmed Mean ( 16 / 31 ) 0.09708798753397 0.0203554650200561 4.76962758838032 Trimmed Mean ( 17 / 31 ) 0.0965075768940152 0.0198644297586435 4.85831096420085 Trimmed Mean ( 18 / 31 ) 0.0961109981252934 0.0193697675540265 4.96190766653338 Trimmed Mean ( 19 / 31 ) 0.095746450208081 0.0188921212901081 5.06806243395302 Trimmed Mean ( 20 / 31 ) 0.095597525987547 0.0184541195930290 5.18028104812211 Trimmed Mean ( 21 / 31 ) 0.0958361134328833 0.0180891156969505 5.29799880980582 Trimmed Mean ( 22 / 31 ) 0.096093910417909 0.0176701522428230 5.43820500793584 Trimmed Mean ( 23 / 31 ) 0.0967450553154011 0.0172481251577833 5.60901862842434 Trimmed Mean ( 24 / 31 ) 0.0967450553154011 0.0167859541791474 5.76345284175649 Trimmed Mean ( 25 / 31 ) 0.0977744994506682 0.0164438856459824 5.94594863742296 Trimmed Mean ( 26 / 31 ) 0.098320170276021 0.0160880878728709 6.1113645731521 Trimmed Mean ( 27 / 31 ) 0.098130160761736 0.0159566340288706 6.14980330965714 Trimmed Mean ( 28 / 31 ) 0.0978897709864946 0.0157627617431837 6.21019162640235 Trimmed Mean ( 29 / 31 ) 0.097789334005045 0.0155471479433028 6.28985678670212 Trimmed Mean ( 30 / 31 ) 0.097630626077384 0.0152417158330028 6.4054878825378 Trimmed Mean ( 31 / 31 ) 0.097630626077384 0.0149374054894209 6.53598284832857 Median 0.103370167075440 Midrange -0.49889279327716 Midmean - Weighted Average at Xnp 0.0925424416872183 Midmean - Weighted Average at X(n+1)p 0.096745055315401 Midmean - Empirical Distribution Function 0.096745055315401 Midmean - Empirical Distribution Function - Averaging 0.096745055315401 Midmean - Empirical Distribution Function - Interpolation 0.097256730350458 Midmean - Closest Observation 0.096745055315401 Midmean - True Basic - Statistics Graphics Toolkit 0.096745055315401 Midmean - MS Excel (old versions) 0.096745055315401 Number of observations 94

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229179607bed21hsd45hpama/17ekt1229179507.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229179607bed21hsd45hpama/17ekt1229179507.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229179607bed21hsd45hpama/2p4e81229179508.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/13/t1229179607bed21hsd45hpama/2p4e81229179508.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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