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paper - autoregressief model met 4 vertragingen

*The author of this computation has been verified*
R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)
Title produced by software: Multiple Regression
Date of computation: Fri, 24 Dec 2010 09:54:39 +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/Dec/24/t1293184359cga09zigolcxhbk.htm/, Retrieved Fri, 24 Dec 2010 10:52:50 +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/2010/Dec/24/t1293184359cga09zigolcxhbk.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:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
600969 586840 671833 654294 631923 625568 600969 586840 671833 654294 558110 625568 600969 586840 671833 630577 558110 625568 600969 586840 628654 630577 558110 625568 600969 603184 628654 630577 558110 625568 656255 603184 628654 630577 558110 600730 656255 603184 628654 630577 670326 600730 656255 603184 628654 678423 670326 600730 656255 603184 641502 678423 670326 600730 656255 625311 641502 678423 670326 600730 628177 625311 641502 678423 670326 589767 628177 625311 641502 678423 582471 589767 628177 625311 641502 636248 582471 589767 628177 625311 599885 636248 582471 589767 628177 621694 599885 636248 582471 589767 637406 621694 599885 636248 582471 595994 637406 621694 599885 636248 696308 595994 637406 621694 599885 674201 696308 595994 637406 621694 648861 674201 696308 595994 637406 649605 648861 674201 696308 595994 672392 649605 648861 674201 696308 598396 672392 649605 648861 674201 613177 598396 672392 649605 648861 638104 613177 598396 672392 649605 615632 638104 61 etc...
 
Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132


Multiple Linear Regression - Estimated Regression Equation
yt[t] = + 28119.1565018716 + 0.224247526251785`yt-1`[t] + 0.36519346283177`yt-2`[t] + 0.602694136336523`yt-3`[t] -0.327862168171322`yt-4`[t] + 28942.8988936196M1[t] + 32090.1133255437M2[t] + 29065.2556587176M3[t] + 68882.0166486343M4[t] + 70753.4096217564M5[t] + 60369.6909960785M6[t] + 56577.5193671499M7[t] + 36920.8165999598M8[t] + 113460.268012881M9[t] + 86862.5721223365M10[t] + 73028.5702156483M11[t] + 126.260004778667t + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0		2-tail p-value1-tail p-value
(Intercept)28119.156501871660431.8153410.46530.6433180.321659
`yt-1`0.2242475262517850.1199161.870.0661260.033063
`yt-2`0.365193462831770.0935453.90390.0002330.000117
`yt-3`0.6026941363365230.0957016.297700
`yt-4`-0.3278621681713220.119846-2.73570.0080760.004038
M128942.898893619615665.7243661.84750.0693660.034683
M232090.113325543716529.4707621.94140.0566870.028344
M329065.255658717616712.4875251.73910.0868960.043448
M468882.016648634315934.7910924.32275.6e-052.8e-05
M570753.409621756415101.5878344.68521.5e-058e-06
M660369.690996078514042.5095194.29916.1e-053e-05
M756577.519367149912446.9503334.54552.5e-051.3e-05
M836920.816599959813809.9086012.67350.0095480.004774
M9113460.26801288114552.8471217.796400
M1086862.572122336513958.8400786.222800
M1173028.570215648314126.1143065.16983e-061e-06
t126.260004778667111.4205861.13320.2614310.130716


Multiple Linear Regression - Regression Statistics
Multiple R0.900321315396772
R-squared0.810578470957773
Adjusted R-squared0.762471415962922
F-TEST (value)16.8494718923145
F-TEST (DF numerator)16
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation20435.3029081579
Sum Squared Residuals26308901111.7345


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast		Residuals
Prediction Error
1600969621290.265763368-20321.2657633677
2625568599929.29340506625638.7065949337
3558110550731.621780437378.37821957008
4630577620912.2018531159664.79814688512
5628654624718.4881858483935.51181415225
6603184591772.65371916111411.3462808394
7656255647485.2526907528769.74730924792
8600730605636.204330678-4906.20433067765
9670326674511.613416094-4185.61341609392
10678423683705.771276453-5282.77127645277
11641502646365.200786738-4863.20078673821
12625311628290.067127864-2979.06712786447
13628177622307.2454533745869.75454662613
14589767595403.795760241-5636.79576024121
15582471587285.273428967-4814.27342896724
16636248618600.84132436317647.1586756365
17599885605904.267266021-6019.26726602116
18621694615327.4341594826366.56584051846
19637406638075.771895152-669.771895152296
20595994590485.9997987535508.00020124721
21696308688669.3007879037638.69921209665
22674201671888.8238122412312.17618775881
23648861659747.518917714-10886.5189177137
24649605647125.5322095752479.4677904254
25672392620894.60410941851497.3958905824
26598396621525.490400163-23129.4904001626
27613177619111.568002031-5934.5680020313
28638104648835.998038136-10731.9980381355
29615632609753.4431385545878.55686144636
30634465636728.782580927-2263.78258092714
31638686639256.723150642-570.723150641723
32604243605834.154784289-1591.15478428887
33706669695035.84757468311633.1524253171
34677185675324.1741087741860.82589122598
35644328670267.523417174-25939.5234171737
36644825652253.854444847-7428.85444484675
37605707618083.858402667-12376.8584026668
38600136602630.686187234-2494.68618723375
39612166595269.27392274916896.7260772511
40599659612136.363154024-12477.3631540241
41634210625190.3329399559019.66706004503
42618234627190.306557955-8956.3065579551
43613576621077.538342445-7501.53834244518
44627200619592.4770824347607.52291756552
45668973676655.658353318-7682.6583533185
46651479666957.686830938-15478.6868309383
47619661674320.472120442-54659.4721204424
48644260608603.90765853435656.0923414662
49579936607330.288282727-27394.2882827267
50601752591721.75757304210030.242426958
51595376595482.23116713-106.231167130079
52588902595129.731419025-6227.73141902504
53634341627584.9137767266756.08622327394
54594305614157.357148702-19852.3571487016
55606200616296.104666766-10096.1046667657
56610926614320.599288923-3394.59928892287
57633685657362.889254167-23677.8892541667
58639696658016.343640338-18320.3436403384
59659451652916.4036372456534.59636275483
60593248598806.520454667-5558.52045466652
61606677616405.196598967-9728.1965989671
62599434608448.631416304-9014.63141630372
63569578562452.9148938717125.08510612881
64629873622854.7441698127018.25583018813
65613438618702.0110309-5264.0110309006
66604172611159.053707096-6987.0537070965
67658328645541.40578639112786.594213609
68612633605097.7018684917535.29813150853
69707372691097.69061383516274.3093861654
70739770704861.20033125534908.7996687446
71777535687720.88112068789814.1188793132
72685030707199.118104514-22169.1181045139
73730234717780.5413894812453.4586105198
74714154709547.345257954606.65474204973
75630872651417.116804821-20545.1168048214
76719492724385.120041525-4893.12004152498
77677023691329.543661996-14306.5436619958
78679272658990.41212667720281.5878733225
79718317721035.203467852-2718.20346785207
80645672656430.862846432-10758.8628464319


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.17340707700170.34681415400340.8265929229983
210.09055982611719260.1811196522343850.909440173882808
220.04224102082963920.08448204165927830.95775897917036
230.01765463915133030.03530927830266070.98234536084867
240.006931775274217050.01386355054843410.993068224725783
250.1531938519491930.3063877038983860.846806148050807
260.1291532779073620.2583065558147250.870846722092637
270.07941785999954270.1588357199990850.920582140000457
280.1347318639016230.2694637278032450.865268136098377
290.08916235793773280.1783247158754660.910837642062267
300.05866866629225470.1173373325845090.941331333707745
310.04239342968751020.08478685937502040.95760657031249
320.02482236115273630.04964472230547270.975177638847264
330.01610146351210730.03220292702421460.983898536487893
340.00933342010664130.01866684021328260.990666579893359
350.0070668792403640.0141337584807280.992933120759636
360.004121042958645320.008242085917290630.995878957041355
370.00755549845359450.0151109969071890.992444501546406
380.004001881905033420.008003763810066830.995998118094967
390.003247754812724370.006495509625448740.996752245187276
400.002419604688926030.004839209377852070.997580395311074
410.001666735864526160.003333471729052320.998333264135474
420.001237893649386600.002475787298773200.998762106350613
430.0006782197384951630.001356439476990330.999321780261505
440.0005060978866127600.001012195773225520.999493902113387
450.000389085993372220.000778171986744440.999610914006628
460.0001989037670355280.0003978075340710550.999801096232964
470.01047546952831590.02095093905663180.989524530471684
480.08473442967771470.1694688593554290.915265570322285
490.06849759885274470.1369951977054890.931502401147255
500.05358837551773410.1071767510354680.946411624482266
510.05211034933241160.1042206986648230.947889650667588
520.03182139276248540.06364278552497090.968178607237514
530.03472539057642770.06945078115285540.965274609423572
540.02169422539939510.04338845079879020.978305774600605
550.01231408473049300.02462816946098590.987685915269507
560.07645348040654070.1529069608130810.92354651959346
570.05306120273421330.1061224054684270.946938797265787
580.04466398046806060.08932796093612130.95533601953194
590.1732372492585840.3464744985171670.826762750741416
600.1007720001296290.2015440002592570.899227999870371


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.24390243902439NOK
5% type I error level200.48780487804878NOK
10% type I error level250.609756097560976NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/10jp5h1293184469.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/10jp5h1293184469.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/1coqn1293184469.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/1coqn1293184469.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/2coqn1293184469.png (open in new window)
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http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/98foe1293184469.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/24/t1293184359cga09zigolcxhbk/98foe1293184469.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
 
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE)
a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
 




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Software written by Ed van Stee & Patrick Wessa


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