Paper_EDAres_output12
| Summary of compuational 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 | -0.000164861794267291 | 9.5728453030916e-05 | -1.72218174479481 |
| Geometric Mean | NaN | ||
| Harmonic Mean | -0.00100670063293597 | ||
| Quadratic Mean | 0.00094749849581928 | ||
| Winsorized Mean ( 1 / 32 ) | -0.000159521542877365 | 9.30817322712652e-05 | -1.71377926672525 |
| Winsorized Mean ( 2 / 32 ) | -0.000155208368856656 | 9.20590274030967e-05 | -1.68596576821357 |
| Winsorized Mean ( 3 / 32 ) | -0.000173952770167208 | 8.7842280680234e-05 | -1.98028522051284 |
| Winsorized Mean ( 4 / 32 ) | -0.000170124884646011 | 8.6677401458863e-05 | -1.96273632783918 |
| Winsorized Mean ( 5 / 32 ) | -0.000170878406824586 | 8.63359519852927e-05 | -1.97922653188205 |
| Winsorized Mean ( 6 / 32 ) | -0.000172368095862711 | 8.45720300198195e-05 | -2.03812177409383 |
| Winsorized Mean ( 7 / 32 ) | -0.000163166899746503 | 8.1886799060039e-05 | -1.99259101124309 |
| Winsorized Mean ( 8 / 32 ) | -0.000175402985154488 | 7.98892937201077e-05 | -2.19557561453744 |
| Winsorized Mean ( 9 / 32 ) | -0.000171303066579088 | 7.8347753626527e-05 | -2.18644515828323 |
| Winsorized Mean ( 10 / 32 ) | -0.000170248954136445 | 7.6878469410847e-05 | -2.21452059908497 |
| Winsorized Mean ( 11 / 32 ) | -0.000172955057314059 | 7.63240176957856e-05 | -2.26606332496054 |
| Winsorized Mean ( 12 / 32 ) | -0.000165722979659316 | 7.3921851554008e-05 | -2.24186727165833 |
| Winsorized Mean ( 13 / 32 ) | -0.000178328755608836 | 7.17860261395322e-05 | -2.48417087835750 |
| Winsorized Mean ( 14 / 32 ) | -0.000172872699660855 | 6.91990235843459e-05 | -2.49819564939587 |
| Winsorized Mean ( 15 / 32 ) | -0.000170905852322802 | 6.84615635058612e-05 | -2.49637670498382 |
| Winsorized Mean ( 16 / 32 ) | -0.000168991572713705 | 6.70065478061643e-05 | -2.52201580661284 |
| Winsorized Mean ( 17 / 32 ) | -0.000164455551487113 | 6.55766427577931e-05 | -2.50783731174723 |
| Winsorized Mean ( 18 / 32 ) | -0.000163604527136618 | 6.45120638372178e-05 | -2.53602996719248 |
| Winsorized Mean ( 19 / 32 ) | -0.000149218514339386 | 6.23557002981838e-05 | -2.39302122541846 |
| Winsorized Mean ( 20 / 32 ) | -0.000142788788175931 | 6.07318820709974e-05 | -2.35113392351330 |
| Winsorized Mean ( 21 / 32 ) | -0.000150386494432554 | 5.8873811838959e-05 | -2.55438691219646 |
| Winsorized Mean ( 22 / 32 ) | -0.000151170112778791 | 5.66515274739915e-05 | -2.66842077379454 |
| Winsorized Mean ( 23 / 32 ) | -0.000162884099765728 | 5.4056644555871e-05 | -3.01321143966633 |
| Winsorized Mean ( 24 / 32 ) | -0.000164760362569747 | 5.21568358479315e-05 | -3.15894091141039 |
| Winsorized Mean ( 25 / 32 ) | -0.000191232829447908 | 4.80323806404549e-05 | -3.98133148717688 |
| Winsorized Mean ( 26 / 32 ) | -0.000188586746589577 | 4.71295939073116e-05 | -4.00145070124019 |
| Winsorized Mean ( 27 / 32 ) | -0.000185963477516595 | 4.66091941496769e-05 | -3.98984537083837 |
| Winsorized Mean ( 28 / 32 ) | -0.000191402670980182 | 4.58631512216079e-05 | -4.1733432152391 |
| Winsorized Mean ( 29 / 32 ) | -0.000171757330378645 | 4.1894270204197e-05 | -4.09978093761944 |
| Winsorized Mean ( 30 / 32 ) | -0.000179884958166086 | 4.05592520031891e-05 | -4.43511527658207 |
| Winsorized Mean ( 31 / 32 ) | -0.000106455498371027 | 3.12534090797877e-05 | -3.40620436315456 |
| Winsorized Mean ( 32 / 32 ) | -0.000108831481875138 | 3.00776813468858e-05 | -3.6183467940891 |
| Trimmed Mean ( 1 / 32 ) | -0.000161787834589383 | 9.002742454051e-05 | -1.79709500094144 |
| Trimmed Mean ( 2 / 32 ) | -0.000164152660723663 | 8.65322594605164e-05 | -1.89701114644491 |
| Trimmed Mean ( 3 / 32 ) | -0.000168922949719400 | 8.31503031772132e-05 | -2.03153738789605 |
| Trimmed Mean ( 4 / 32 ) | -0.000167093924102016 | 8.11221885425296e-05 | -2.05978077150142 |
| Trimmed Mean ( 5 / 32 ) | -0.000166248074647877 | 7.91961051673321e-05 | -2.09919508410943 |
| Trimmed Mean ( 6 / 32 ) | -0.000165189713007487 | 7.7078925931254e-05 | -2.14312421990438 |
| Trimmed Mean ( 7 / 32 ) | -0.000163789052938174 | 7.50917594213567e-05 | -2.18118544831421 |
| Trimmed Mean ( 8 / 32 ) | -0.000163895707771032 | 7.34188482309568e-05 | -2.23233831257416 |
| Trimmed Mean ( 9 / 32 ) | -0.000162125357404347 | 7.18987201498063e-05 | -2.25491298129573 |
| Trimmed Mean ( 10 / 32 ) | -0.00016083725787105 | 7.04323273228484e-05 | -2.28357153574953 |
| Trimmed Mean ( 11 / 32 ) | -0.000159616281058242 | 6.89914054956301e-05 | -2.31356760906041 |
| Trimmed Mean ( 12 / 32 ) | -0.000157999459693901 | 6.73941039882151e-05 | -2.34441071761306 |
| Trimmed Mean ( 13 / 32 ) | -0.000157116771697853 | 6.59378861077399e-05 | -2.38279964633884 |
| Trimmed Mean ( 14 / 32 ) | -0.000154813207834217 | 6.45719323642796e-05 | -2.39753097306807 |
| Trimmed Mean ( 15 / 32 ) | -0.000152936896995086 | 6.33770802039672e-05 | -2.41312626745958 |
| Trimmed Mean ( 16 / 32 ) | -0.000151140001462314 | 6.20584595103715e-05 | -2.43544558880091 |
| Trimmed Mean ( 17 / 32 ) | -0.000149412430050889 | 6.07058734401958e-05 | -2.46125163157536 |
| Trimmed Mean ( 18 / 32 ) | -0.000147996606856892 | 5.93012675133403e-05 | -2.49567358444065 |
| Trimmed Mean ( 19 / 32 ) | -0.000146561395796687 | 5.77623828074663e-05 | -2.53731561395598 |
| Trimmed Mean ( 20 / 32 ) | -0.000146321655777797 | 5.62479560234301e-05 | -2.60136840735771 |
| Trimmed Mean ( 21 / 32 ) | -0.000146635688453518 | 5.46569135658489e-05 | -2.68283880092966 |
| Trimmed Mean ( 22 / 32 ) | -0.000146305947268548 | 5.30077775756967e-05 | -2.76008453777596 |
| Trimmed Mean ( 23 / 32 ) | -0.000145881438278563 | 5.13484037944372e-05 | -2.84101213472126 |
| Trimmed Mean ( 24 / 32 ) | -0.000144402945975331 | 4.97363570745379e-05 | -2.90336796800217 |
| Trimmed Mean ( 25 / 32 ) | -0.000142632735836687 | 4.80422414289511e-05 | -2.96890260725291 |
| Trimmed Mean ( 26 / 32 ) | -0.000138391273121526 | 4.6649302958953e-05 | -2.96663110364793 |
| Trimmed Mean ( 27 / 32 ) | -0.000133978484245213 | 4.49957938468476e-05 | -2.97757796431454 |
| Trimmed Mean ( 28 / 32 ) | -0.000129357595954424 | 4.29016993625091e-05 | -3.01520913801999 |
| Trimmed Mean ( 29 / 32 ) | -0.000123759544072400 | 4.02190490988113e-05 | -3.07713749691954 |
| Trimmed Mean ( 30 / 32 ) | -0.000119345954526999 | 3.77170413947428e-05 | -3.16424486422293 |
| Trimmed Mean ( 31 / 32 ) | -0.000113648165949202 | 3.45491090036024e-05 | -3.28946734740257 |
| Trimmed Mean ( 32 / 32 ) | -0.000114344230553542 | 3.32228915669604e-05 | -3.44173023961752 |
| Median | -0.000112110865902668 | ||
| Midrange | -0.00030933789912897 | ||
| Midmean - Weighted Average at Xnp | -0.000158667663280381 | ||
| Midmean - Weighted Average at X(n+1)p | -0.000144402945975331 | ||
| Midmean - Empirical Distribution Function | -0.000158667663280381 | ||
| Midmean - Empirical Distribution Function - Averaging | -0.000144402945975331 | ||
| Midmean - Empirical Distribution Function - Interpolation | -0.000144402945975331 | ||
| Midmean - Closest Observation | -0.000158667663280381 | ||
| Midmean - True Basic - Statistics Graphics Toolkit | -0.000144402945975331 | ||
| Midmean - MS Excel (old versions) | -0.000145881438278563 | ||
| Number of observations | 96 | ||
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| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/13/t1197554985vv0sr9684m2fepu/1kcpx1197555920.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/13/t1197554985vv0sr9684m2fepu/1kcpx1197555920.ps (open in new window) |
 |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/13/t1197554985vv0sr9684m2fepu/2ksjz1197555920.png (open in new window) |
| http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2007/Dec/13/t1197554985vv0sr9684m2fepu/2ksjz1197555920.ps (open in new window) |
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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Software written by Ed van Stee & Patrick Wessa
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