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tijdreeks 1 - stap 14

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

Title produced by software: Central Tendency

Date of computation: Tue, 20 Jul 2010 10:24:27 +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/20/t1279621499vvc1zhhy6et9zji.htm/, Retrieved Tue, 20 Jul 2010 12:25:00 +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/20/t1279621499vvc1zhhy6et9zji.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:

Vanhille Olivier

Dataseries X:

» Textbox « » Textfile « » CSV «

568 567 566 564 584 583 568 558 559 559 560 562 563 552 552 555 575 567 548 541 544 546 551 550 546 532 523 528 555 543 525 517 519 521 520 516 509 494 484 482 508 500 480 467 471 482 481 477 471 455 441 434 459 448 432 414 415 423 425 427 415 399 386 377 397 379 361 350 348 363 367 365 354 327 312 307 335 317 298 286 288 303 310 301 293 264 255 251 279 253 233 226 232 245 250 242 230 196 188 181 212 186 166 155 157 173 182 182 168 131 114 106 134 103 83 74 83 96 95 100

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Central Tendency - Ungrouped Data
Measure	Value	S.E.	Value/S.E.
Arithmetic Mean	380.241666666667	14.1557215710802	26.8613411727093
Geometric Mean	338.435992051815
Harmonic Mean	285.803374880624
Quadratic Mean	410.40160209239
Winsorized Mean ( 1 / 40 )	380.308333333333	14.1412729857439	26.8935005863143
Winsorized Mean ( 2 / 40 )	380.175	14.1255160908171	26.9140608778995
Winsorized Mean ( 3 / 40 )	380.3	14.0534424144933	27.0609854001179
Winsorized Mean ( 4 / 40 )	380.333333333333	14.0477643253182	27.0742962741666
Winsorized Mean ( 5 / 40 )	380.458333333333	14.0149075084231	27.1466888457647
Winsorized Mean ( 6 / 40 )	380.608333333333	13.9897886721531	27.2061531630524
Winsorized Mean ( 7 / 40 )	380.725	13.9542016126706	27.283897034589
Winsorized Mean ( 8 / 40 )	381.125	13.8520319905098	27.5140138472907
Winsorized Mean ( 9 / 40 )	382.325	13.6416582821252	28.0262847883358
Winsorized Mean ( 10 / 40 )	382.491666666667	13.5938433287737	28.1371248304044
Winsorized Mean ( 11 / 40 )	384.233333333333	13.2859824476215	28.920204798413
Winsorized Mean ( 12 / 40 )	384.333333333333	13.2459619835712	29.0151318424449
Winsorized Mean ( 13 / 40 )	385.308333333333	13.1070992151811	29.3969189526738
Winsorized Mean ( 14 / 40 )	385.425	13.0614024072125	29.5086996008306
Winsorized Mean ( 15 / 40 )	385.675	12.9330540981364	29.8208758019171
Winsorized Mean ( 16 / 40 )	386.741666666667	12.7872348366085	30.2443547497436
Winsorized Mean ( 17 / 40 )	386.458333333333	12.7213560038267	30.3787059506144
Winsorized Mean ( 18 / 40 )	386.458333333333	12.7213560038267	30.3787059506144
Winsorized Mean ( 19 / 40 )	386.933333333333	12.6189063466539	30.6629847867867
Winsorized Mean ( 20 / 40 )	387.1	12.5562064067031	30.8293753273559
Winsorized Mean ( 21 / 40 )	388.15	12.3331307235989	31.472138640134
Winsorized Mean ( 22 / 40 )	390.716666666667	11.9160942841126	32.7889875114195
Winsorized Mean ( 23 / 40 )	393.4	11.5839851296777	33.9606789542681
Winsorized Mean ( 24 / 40 )	393.8	11.4430914414198	34.4137772573056
Winsorized Mean ( 25 / 40 )	394.008333333333	11.3701258269729	34.6529439805006
Winsorized Mean ( 26 / 40 )	393.791666666667	11.2966114737554	34.85927329461
Winsorized Mean ( 27 / 40 )	393.791666666667	10.8368528812019	36.3381943986483
Winsorized Mean ( 28 / 40 )	393.558333333333	10.655148252858	36.9359791148631
Winsorized Mean ( 29 / 40 )	394.041666666667	10.4376325421956	37.7520156102159
Winsorized Mean ( 30 / 40 )	393.791666666667	10.3560825023068	38.0251573487321
Winsorized Mean ( 31 / 40 )	393.791666666667	10.2422608334029	38.4477287848794
Winsorized Mean ( 32 / 40 )	394.058333333333	10.1529500330897	38.8122005967772
Winsorized Mean ( 33 / 40 )	396.258333333333	9.84199041489055	40.2620117099291
Winsorized Mean ( 34 / 40 )	399.941666666667	9.31021017066938	42.957318829024
Winsorized Mean ( 35 / 40 )	401.691666666667	9.0583140063547	44.3450808157972
Winsorized Mean ( 36 / 40 )	400.191666666667	8.77306720023884	45.6159354000812
Winsorized Mean ( 37 / 40 )	401.425	8.57615956913012	46.8070815105784
Winsorized Mean ( 38 / 40 )	400.475	8.14597185687486	49.1623353279837
Winsorized Mean ( 39 / 40 )	399.5	7.84415784676277	50.929622759296
Winsorized Mean ( 40 / 40 )	396.833333333333	7.44074005134377	53.332508674546
Trimmed Mean ( 1 / 40 )	381.110169491525	14.0519605683039	27.1214943736157
Trimmed Mean ( 2 / 40 )	381.939655172414	13.9510564951297	27.3771133609665
Trimmed Mean ( 3 / 40 )	382.868421052632	13.846069242443	27.6517771469037
Trimmed Mean ( 4 / 40 )	383.785714285714	13.755830801675	27.8998571455955
Trimmed Mean ( 5 / 40 )	384.727272727273	13.6547621225101	28.1753185647257
Trimmed Mean ( 6 / 40 )	385.675925925926	13.5482920765064	28.4667560861574
Trimmed Mean ( 7 / 40 )	386.632075471698	13.43278716329	28.7827143221855
Trimmed Mean ( 8 / 40 )	387.605769230769	13.3085014007948	29.1246743384361
Trimmed Mean ( 9 / 40 )	388.558823529412	13.185949452518	29.4676409103945
Trimmed Mean ( 10 / 40 )	389.39	13.0823568058649	29.7645145884902
Trimmed Mean ( 11 / 40 )	390.234693877551	12.9706882888129	30.0858894445968
Trimmed Mean ( 12 / 40 )	390.916666666667	12.8890224836795	30.3294270113702
Trimmed Mean ( 13 / 40 )	391.617021276596	12.7989853391214	30.5975052631381
Trimmed Mean ( 14 / 40 )	392.25	12.7130917037869	30.8540211255739
Trimmed Mean ( 15 / 40 )	392.9	12.6180127077972	31.1380253847113
Trimmed Mean ( 16 / 40 )	393.556818181818	12.5228929128048	31.4269890289809
Trimmed Mean ( 17 / 40 )	394.151162790698	12.4296159880048	31.7106468269876
Trimmed Mean ( 18 / 40 )	394.797619047619	12.3268359065024	32.0274904316171
Trimmed Mean ( 19 / 40 )	395.475609756098	12.2047698211215	32.4033648772049
Trimmed Mean ( 20 / 40 )	396.15	12.0735103387016	32.811501285599
Trimmed Mean ( 21 / 40 )	396.846153846154	11.9259994484675	33.2757145898702
Trimmed Mean ( 22 / 40 )	397.5	11.7802317315545	33.7429694982365
Trimmed Mean ( 23 / 40 )	398	11.6607767082415	34.1315171328773
Trimmed Mean ( 24 / 40 )	398.333333333333	11.559145719527	34.4604474239325
Trimmed Mean ( 25 / 40 )	398.657142857143	11.4522301699332	34.8104375254158
Trimmed Mean ( 26 / 40 )	398.985294117647	11.3300607995012	35.2147531401783
Trimmed Mean ( 27 / 40 )	399.348484848485	11.1894783333544	35.6896428011375
Trimmed Mean ( 28 / 40 )	399.734375	11.0774071649357	36.0855540514315
Trimmed Mean ( 29 / 40 )	400.161290322581	10.9606814615096	36.5087966225292
Trimmed Mean ( 30 / 40 )	400.583333333333	10.8431626420921	36.9434035581378
Trimmed Mean ( 31 / 40 )	401.051724137931	10.7047326975365	37.4648985144885
Trimmed Mean ( 32 / 40 )	401.553571428571	10.545477261707	38.0782738858773
Trimmed Mean ( 33 / 40 )	402.074074074074	10.3569369917823	38.8217167289035
Trimmed Mean ( 34 / 40 )	402.480769230769	10.1692127854707	39.5783604612754
Trimmed Mean ( 35 / 40 )	402.66	10.0180901679035	40.1932896641382
Trimmed Mean ( 36 / 40 )	402.729166666667	9.86233879873173	40.8350569662499
Trimmed Mean ( 37 / 40 )	402.913043478261	9.7051620353462	41.5153340058468
Trimmed Mean ( 38 / 40 )	403.022727272727	9.53043908598296	42.2879495516087
Trimmed Mean ( 39 / 40 )	403.214285714286	9.37702580291737	43.0002320766611
Trimmed Mean ( 40 / 40 )	403.5	9.22371525346067	43.7459298029186
Median	414.5
Midrange	329
Midmean - Weighted Average at Xnp	398.114754098361
Midmean - Weighted Average at X(n+1)p	400.583333333333
Midmean - Empirical Distribution Function	398.114754098361
Midmean - Empirical Distribution Function - Averaging	400.583333333333
Midmean - Empirical Distribution Function - Interpolation	400.583333333333
Midmean - Closest Observation	398.114754098361
Midmean - True Basic - Statistics Graphics Toolkit	400.583333333333
Midmean - MS Excel (old versions)	400.161290322581
Number of observations	120

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/20/t1279621499vvc1zhhy6et9zji/1okgb1279621463.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/20/t1279621499vvc1zhhy6et9zji/1okgb1279621463.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/20/t1279621499vvc1zhhy6et9zji/2ztgw1279621463.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/20/t1279621499vvc1zhhy6et9zji/2ztgw1279621463.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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