Loonkostindex met seizonaliteit

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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 13:29:23 +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/t1293197288hovdo497khghpy3.htm/, Retrieved Fri, 24 Dec 2010 14:28:18 +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/t1293197288hovdo497khghpy3.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:

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81.71 84.86 87.703 85.03 90.09 85.61 100.639 85.52 83.042 86.51 89.956 86.66 89.561 87.27 105.38 87.62 86.554 88.17 93.131 87.99 92.812 88.83 102.195 88.75 88.925 88.81 94.184 89.43 94.196 89.5 108.932 89.34 91.134 89.75 97.149 90.26 96.415 90.32 112.432 90.76 92.47 91.53 98.61410515 92.35 97.80117197 93.04 111.8560178 93.35 95.63981455 93.54 104.1120262 95.07 104.0148224 95.39 118.1743476 95.43 102.033431 96.09 109.3138852 96.35 108.1523649 96.6 121.30381 96.62 103.8725146 97.6 112.7185207 97.67 109.0381253 98.23 122.4434864 98.29 106.6325686 98.89 113.8153852 99.88 111.1071252 100.42 130.039536 100.81 109.6121057 101.5 116.8592117 102.59 113.8982545 103.58 128.9375926 103.47 111.8120023 103.77 119.9689463 104.65 117.018539 105.12 132.4743387 104.97 116.3369106 105.58 124.6405636 106.17 121.025249 106.52 137.2054829 107.87 120.0187687 109.63 127.0443429 111.54 124.349043 112.47 143.6114438 111.63

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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 7 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation LKI[t] = -32.3607428109776 + 1.57163858977850CPI[t] -18.3733655297165Q1[t] -12.3285261175605Q2[t] -14.5529109634028Q3[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -32.3607428109776 3.330902 -9.7153 0 0 CPI 1.57163858977850 0.034001 46.2233 0 0 Q1 -18.3733655297165 0.742379 -24.7493 0 0 Q2 -12.3285261175605 0.741369 -16.6294 0 0 Q3 -14.5529109634028 0.741071 -19.6377 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.9914725479775 R-squared 0.983017813392995 Adjusted R-squared 0.981685877188525 F-TEST (value) 738.036709335835 F-TEST (DF numerator) 4 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.96066508658799 Sum Squared Residuals 196.054586670019 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 81.71 82.6351423879086 -0.925142387908629 2 87.703 88.9471603603273 -1.24416036032729 3 90.09 87.6343258965565 2.45567410344347 4 100.639 102.045789386879 -1.40678938687924 5 83.042 85.2283460610435 -2.18634606104345 6 89.956 91.5089312616662 -1.55293126166622 7 89.561 90.2432459555888 -0.682245955588831 8 105.38 105.346230425414 0.0337695745859021 9 86.554 87.8372661200758 -1.28326612007576 10 93.131 93.5992105860716 -0.468210586071625 11 92.812 92.6950021556433 0.116997844356707 12 102.195 107.122182031864 -4.92718203186379 13 88.925 88.843114817534 0.0818851824660004 14 94.184 95.8623701553527 -1.67837015535268 15 94.196 93.7480000107949 0.447999989205114 16 108.932 108.049448799833 0.882551200166897 17 91.134 90.3204550919258 0.813544908074221 18 97.149 97.1668301848688 -0.0178301848688226 19 96.415 95.0367436544132 1.37825634558677 20 112.432 110.281175597319 2.15082440268143 21 92.47 93.1179717817315 -0.647971781731503 22 98.61410515 100.451554837506 -1.83744968750586 23 97.80117197 99.3116006186108 -1.51042864861077 24 111.8560178 114.351719544845 -2.49570174484485 25 95.63981455 96.2769653471863 -0.637150797186286 26 104.1120262 104.726411801703 -0.614385601703362 27 104.0148224 103.004951304590 1.00987109540978 28 118.1743476 117.620727811584 0.553619788415862 29 102.033431 100.284643751121 1.74878724887855 30 109.3138852 106.738109196620 2.57577600338016 31 108.1523649 104.906633998222 3.24573090177780 32 121.30381 119.490977733421 1.81283226657945 33 103.8725146 102.657818021687 1.21469657831305 34 112.7185207 108.812672135127 3.90584856487253 35 109.0381253 107.468404899561 1.56972040043885 36 122.4434864 122.115614178351 0.327872221649359 37 106.6325686 104.685231802501 1.94733679749878 38 113.8153852 112.285993418538 1.52939178146207 39 111.1071252 110.910293411176 0.196831788823947 40 130.039536 126.076143424592 3.96339257540756 41 109.6121057 108.787208521823 0.824897178176908 42 116.8592117 116.545133996838 0.314077703162339 43 113.8982545 115.876671354876 -1.9784168548761 44 128.9375926 130.256702073403 -1.31910947340324 45 111.8120023 112.354828120620 -0.542825820620266 46 119.9689463 119.782709491781 0.186236808218633 47 117.018539 118.296994783135 -1.27845578313498 48 132.4743387 132.614159958071 -0.139821258070968 49 116.3369106 115.199493968119 1.13741663188065 50 124.6405636 122.171600148245 2.46896345175532 51 121.025249 120.497288808825 0.527960191175139 52 137.2054829 137.171911868429 0.0335710315713785 53 120.0187687 121.564630256722 -1.54586155672225 54 127.0443429 130.611299375355 -3.56695647535519 55 124.349043 129.848538418007 -5.49949541800691 56 143.6114438 143.081272965996 0.530170834004248 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.305157613555767 0.610315227111534 0.694842386444233 9 0.191719941010000 0.383439882020001 0.80828005899 10 0.125717890437361 0.251435780874722 0.874282109562639 11 0.0666892610062383 0.133378522012477 0.933310738993762 12 0.315264280731397 0.630528561462794 0.684735719268603 13 0.327069617066943 0.654139234133887 0.672930382933057 14 0.263838828709626 0.527677657419251 0.736161171290374 15 0.182930657380934 0.365861314761869 0.817069342619066 16 0.284627898779412 0.569255797558824 0.715372101220588 17 0.264382403211655 0.52876480642331 0.735617596788345 18 0.215487218955776 0.430974437911552 0.784512781044224 19 0.156262590379952 0.312525180759905 0.843737409620048 20 0.204045780688484 0.408091561376969 0.795954219311516 21 0.168580431141209 0.337160862282417 0.831419568858791 22 0.206186361317141 0.412372722634282 0.793813638682859 23 0.265878764058872 0.531757528117745 0.734121235941128 24 0.437482965073149 0.874965930146298 0.562517034926851 25 0.465172222380253 0.930344444760507 0.534827777619747 26 0.55579562519426 0.88840874961148 0.44420437480574 27 0.48420326057337 0.96840652114674 0.51579673942663 28 0.521748807249711 0.956502385500578 0.478251192750289 29 0.508033750613352 0.983932498773297 0.491966249386648 30 0.542289328312775 0.91542134337445 0.457710671687225 31 0.5618438196614 0.8763123606772 0.4381561803386 32 0.520414824525526 0.959170350948947 0.479585175474474 33 0.45426705348261 0.90853410696522 0.545732946517389 34 0.50738349486717 0.98523301026566 0.49261650513283 35 0.447533996849481 0.895067993698962 0.552466003150519 36 0.482149363452546 0.964298726905091 0.517850636547454 37 0.394217845416259 0.788435690832518 0.605782154583741 38 0.311589265189982 0.623178530379964 0.688410734810018 39 0.279627966912255 0.559255933824509 0.720372033087745 40 0.335851734189755 0.67170346837951 0.664148265810245 41 0.260162209758528 0.520324419517057 0.739837790241471 42 0.207856014285557 0.415712028571113 0.792143985714443 43 0.259805901221165 0.519611802442329 0.740194098778835 44 0.38384153092534 0.76768306185068 0.61615846907466 45 0.370118610670573 0.740237221341146 0.629881389329427 46 0.294966373006054 0.589932746012108 0.705033626993946 47 0.235357997746692 0.470715995493385 0.764642002253308 48 0.43077420652749 0.86154841305498 0.56922579347251 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/10m9ae1293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/10m9ae1293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/1qzu51293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/1qzu51293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/2qzu51293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/2qzu51293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/318t81293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/318t81293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/418t81293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/418t81293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/518t81293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/518t81293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/6uzbt1293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/6uzbt1293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/7uzbt1293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/7uzbt1293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/8m9ae1293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/8m9ae1293197354.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/9m9ae1293197354.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293197288hovdo497khghpy3/9m9ae1293197354.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = No 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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