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Tijdreeks 1 - Stap 14

*Unverified author*

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

Title produced by software: Central Tendency

Date of computation: Tue, 27 Jul 2010 08:30:15 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Jul/27/t1280219546ufpvw7igjd5cmde.htm/, Retrieved Tue, 27 Jul 2010 10:32:26 +0200

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/2010/Jul/27/t1280219546ufpvw7igjd5cmde.htm/},
    year = {2010},
}
@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 = {2010},
    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:

Patrick Fieremans

Dataseries X:

» Textbox « » Textfile « » CSV «

136 135 134 132 152 151 136 126 127 127 128 130 125 118 111 104 126 131 122 116 115 115 113 122 114 106 93 89 114 122 115 116 120 120 120 121 118 112 99 96 120 135 128 134 134 132 130 125 124 114 101 101 123 143 133 136 137 135 141 136 133 124 110 104 130 160 142 142 137 135 139 135 134 120 103 101 127 159 141 140 135 127 130 128 126 110 101 102 129 169 146 145 138 123 124 137 132 112 105 106 137 175 151 142 140 122 127 135 128 117 107 108 134 171 154 146 148 122 124 135

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	126.941666666667	1.46305079327963	86.7650441459448
Geometric Mean	125.937056321508
Harmonic Mean	124.925738200123
Quadratic Mean	127.941034595369
Winsorized Mean ( 1 / 40 )	126.941666666667	1.44726612907441	87.7113504672785
Winsorized Mean ( 2 / 40 )	126.958333333333	1.42940291511672	88.8191369911728
Winsorized Mean ( 3 / 40 )	126.808333333333	1.36524978647076	92.8828809130522
Winsorized Mean ( 4 / 40 )	126.841666666667	1.34742184742590	94.1365667396467
Winsorized Mean ( 5 / 40 )	126.633333333333	1.30818011020920	96.8011456106622
Winsorized Mean ( 6 / 40 )	126.533333333333	1.29109931517421	98.0043377346689
Winsorized Mean ( 7 / 40 )	126.475	1.28157392400481	98.6872451374297
Winsorized Mean ( 8 / 40 )	126.541666666667	1.27059481391163	99.5924627435698
Winsorized Mean ( 9 / 40 )	126.391666666667	1.22356048349453	103.298258134071
Winsorized Mean ( 10 / 40 )	126.308333333333	1.18627750479979	106.474524571424
Winsorized Mean ( 11 / 40 )	126.308333333333	1.18627750479979	106.474524571424
Winsorized Mean ( 12 / 40 )	126.308333333333	1.15686937696907	109.181153765392
Winsorized Mean ( 13 / 40 )	126.2	1.11172765340714	113.517010765390
Winsorized Mean ( 14 / 40 )	126.083333333333	1.09720700505941	114.912986111045
Winsorized Mean ( 15 / 40 )	126.208333333333	1.07823487833023	117.050872560143
Winsorized Mean ( 16 / 40 )	126.341666666667	1.05854505039519	119.354076257311
Winsorized Mean ( 17 / 40 )	126.483333333333	1.00083881719379	126.377325859497
Winsorized Mean ( 18 / 40 )	126.483333333333	1.00083881719379	126.377325859497
Winsorized Mean ( 19 / 40 )	126.483333333333	0.960128327479592	131.735862502215
Winsorized Mean ( 20 / 40 )	126.65	0.937892774864407	135.036758352585
Winsorized Mean ( 21 / 40 )	126.475	0.917382900731544	137.865006966170
Winsorized Mean ( 22 / 40 )	126.475	0.872627836902247	144.935784364816
Winsorized Mean ( 23 / 40 )	126.475	0.827144737858464	152.905524524586
Winsorized Mean ( 24 / 40 )	126.475	0.827144737858464	152.905524524586
Winsorized Mean ( 25 / 40 )	126.475	0.827144737858464	152.905524524586
Winsorized Mean ( 26 / 40 )	126.691666666667	0.800104670440144	158.343865930657
Winsorized Mean ( 27 / 40 )	126.466666666667	0.77630646484337	162.908171442554
Winsorized Mean ( 28 / 40 )	126.466666666667	0.77630646484337	162.908171442554
Winsorized Mean ( 29 / 40 )	126.708333333333	0.746738551336602	169.682324699234
Winsorized Mean ( 30 / 40 )	126.708333333333	0.746738551336602	169.682324699234
Winsorized Mean ( 31 / 40 )	126.708333333333	0.689293454816893	183.823497014044
Winsorized Mean ( 32 / 40 )	126.975	0.658223406771051	192.905628535579
Winsorized Mean ( 33 / 40 )	126.975	0.658223406771051	192.905628535579
Winsorized Mean ( 34 / 40 )	127.541666666667	0.595489092738477	214.179685609589
Winsorized Mean ( 35 / 40 )	127.541666666667	0.595489092738477	214.179685609589
Winsorized Mean ( 36 / 40 )	127.541666666667	0.595489092738477	214.179685609589
Winsorized Mean ( 37 / 40 )	127.541666666667	0.595489092738477	214.179685609589
Winsorized Mean ( 38 / 40 )	127.541666666667	0.595489092738477	214.179685609589
Winsorized Mean ( 39 / 40 )	127.541666666667	0.52738664311414	241.837119562892
Winsorized Mean ( 40 / 40 )	127.875	0.493311354457785	259.217629686533
Trimmed Mean ( 1 / 40 )	126.855932203390	1.39369430205501	91.0213466585466
Trimmed Mean ( 2 / 40 )	126.767241379310	1.33347837684094	95.0650896039473
Trimmed Mean ( 3 / 40 )	126.666666666667	1.27646136644957	99.232667745351
Trimmed Mean ( 4 / 40 )	126.616071428571	1.23990424546437	102.117620688645
Trimmed Mean ( 5 / 40 )	126.554545454545	1.20490977542380	105.032383366658
Trimmed Mean ( 6 / 40 )	126.537037037037	1.17670550578265	107.535008900018
Trimmed Mean ( 7 / 40 )	126.537735849057	1.14925457526192	110.104182809295
Trimmed Mean ( 8 / 40 )	126.548076923077	1.12052610600414	112.936303978097
Trimmed Mean ( 9 / 40 )	126.549019607843	1.09042063975943	116.055231342432
Trimmed Mean ( 10 / 40 )	126.57	1.06526944474109	118.815010253826
Trimmed Mean ( 11 / 40 )	126.602040816327	1.04332204577945	121.345121890666
Trimmed Mean ( 12 / 40 )	126.635416666667	1.01848268117662	124.337329448124
Trimmed Mean ( 13 / 40 )	126.670212765957	0.994998738012342	127.306907965532
Trimmed Mean ( 14 / 40 )	126.717391304348	0.97512370574659	129.950067419731
Trimmed Mean ( 15 / 40 )	126.777777777778	0.95449233884474	132.822205709081
Trimmed Mean ( 16 / 40 )	126.829545454545	0.933698277878985	135.835685316519
Trimmed Mean ( 17 / 40 )	126.872093023256	0.912715542315406	139.005075668376
Trimmed Mean ( 18 / 40 )	126.904761904762	0.89664048310541	141.533607165764
Trimmed Mean ( 19 / 40 )	126.939024390244	0.878041461711276	144.570649480315
Trimmed Mean ( 20 / 40 )	126.975	0.86199191602424	147.304165665087
Trimmed Mean ( 21 / 40 )	127	0.846335463208128	150.058700740948
Trimmed Mean ( 22 / 40 )	127.039473684211	0.83064988074202	152.939856646613
Trimmed Mean ( 23 / 40 )	127.081081081081	0.818102471418989	155.336385747204
Trimmed Mean ( 24 / 40 )	127.125	0.809020326622817	157.134494420767
Trimmed Mean ( 25 / 40 )	127.171428571429	0.798023289917725	159.358041523500
Trimmed Mean ( 26 / 40 )	127.220588235294	0.784744129417131	162.1172857066
Trimmed Mean ( 27 / 40 )	127.257575757576	0.772580999913184	164.717454573534
Trimmed Mean ( 28 / 40 )	127.3125	0.760793693224907	167.341686890619
Trimmed Mean ( 29 / 40 )	127.370967741935	0.74630550330705	170.668670105641
Trimmed Mean ( 30 / 40 )	127.416666666667	0.733111805266233	173.802502907990
Trimmed Mean ( 31 / 40 )	127.465517241379	0.716757974982754	177.836203698251
Trimmed Mean ( 32 / 40 )	127.517857142857	0.705263638385634	180.808778735210
Trimmed Mean ( 33 / 40 )	127.555555555556	0.695732557970297	183.339925800916
Trimmed Mean ( 34 / 40 )	127.596153846154	0.683383702325262	186.712316100016
Trimmed Mean ( 35 / 40 )	127.6	0.677932029583124	188.219459225823
Trimmed Mean ( 36 / 40 )	127.604166666667	0.670251608925439	190.382484678022
Trimmed Mean ( 37 / 40 )	127.608695652174	0.65973077576564	193.425409787924
Trimmed Mean ( 38 / 40 )	127.613636363636	0.645537543423501	197.685847498286
Trimmed Mean ( 39 / 40 )	127.619047619048	0.626507363193099	203.699198312077
Trimmed Mean ( 40 / 40 )	127.625	0.615700039354829	207.284378499852
Median	127
Midrange	132
Midmean - Weighted Average at Xnp	127.640625
Midmean - Weighted Average at X(n+1)p	127.640625
Midmean - Empirical Distribution Function	127.640625
Midmean - Empirical Distribution Function - Averaging	127.640625
Midmean - Empirical Distribution Function - Interpolation	127.640625
Midmean - Closest Observation	127.640625
Midmean - True Basic - Statistics Graphics Toolkit	127.640625
Midmean - MS Excel (old versions)	127.640625
Number of observations	120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/27/t1280219546ufpvw7igjd5cmde/1kllh1280219411.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/27/t1280219546ufpvw7igjd5cmde/1kllh1280219411.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/27/t1280219546ufpvw7igjd5cmde/2kllh1280219411.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/27/t1280219546ufpvw7igjd5cmde/2kllh1280219411.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')

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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