SHW WS7 - Lineaire Trend

*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: Wed, 18 Nov 2009 07:41:08 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx.htm/, Retrieved Wed, 18 Nov 2009 15:42:38 +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/2009/Nov/18/t1258555346gkzdesxfd7ffgnx.htm/}, year = {2009}, } @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 = {2009}, 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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8.6 1.59 8.5 1.26 8.3 1.13 7.8 1.92 7.8 2.61 8 2.26 8.6 2.41 8.9 2.26 8.9 2.03 8.6 2.86 8.3 2.55 8.3 2.27 8.3 2.26 8.4 2.57 8.5 3.07 8.4 2.76 8.6 2.51 8.5 2.87 8.5 3.14 8.5 3.11 8.5 3.16 8.5 2.47 8.5 2.57 8.5 2.89 8.5 2.63 8.5 2.38 8.5 1.69 8.5 1.96 8.6 2.19 8.4 1.87 8.1 1.6 8 1.63 8 1.22 8 1.21 8 1.49 7.9 1.64 7.8 1.66 7.8 1.77 7.9 1.82 8.1 1.78 8 1.28 7.6 1.29 7.3 1.37 7 1.12 6.8 1.51 7 2.24 7.1 2.94 7.2 3.09 7.1 3.46 6.9 3.64 6.7 4.39 6.7 4.15 6.6 5.21 6.9 5.8 7.3 5.91 7.5 5.39 7.3 5.46 7.1 4.72 6.9 3.14 7.1 2.63

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 8.98296836521457 -0.0409587337748366X[t] -0.0776609807441892M1[t] -0.087485820924576M2[t] -0.093542457597681M3[t] -0.139681011738336M4[t] -0.0795938383452158M5[t] -0.0872069069017645M6[t] + 0.0255896118794349M7[t] + 0.0680645297493756M8[t] + 0.0170109275557401M9[t] -0.0119947379491530M10[t] -0.0686187279361657M11[t] -0.0300113248845106t + 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) 8.98296836521457 0.229032 39.2215 0 0 X -0.0409587337748366 0.051288 -0.7986 0.42862 0.21431 M1 -0.0776609807441892 0.265648 -0.2923 0.771337 0.385668 M2 -0.087485820924576 0.265209 -0.3299 0.742992 0.371496 M3 -0.093542457597681 0.264958 -0.353 0.725667 0.362833 M4 -0.139681011738336 0.264781 -0.5275 0.600358 0.300179 M5 -0.0795938383452158 0.265289 -0.3 0.765508 0.382754 M6 -0.0872069069017645 0.265171 -0.3289 0.743746 0.371873 M7 0.0255896118794349 0.26515 0.0965 0.923535 0.461767 M8 0.0680645297493756 0.264049 0.2578 0.797732 0.398866 M9 0.0170109275557401 0.263744 0.0645 0.948853 0.474427 M10 -0.0119947379491530 0.263628 -0.0455 0.963907 0.481953 M11 -0.0686187279361657 0.263256 -0.2607 0.795523 0.397761 t -0.0300113248845106 0.003565 -8.4192 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.828915594493535 R-squared 0.68710106279457 Adjusted R-squared 0.598673102279992 F-TEST (value) 7.77017878503821 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 7.69727117599928e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.416179568730165 Sum Squared Residuals 7.9674499377076 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.6 8.8101716728839 -0.210171672883898 2 8.5 8.78385188996468 -0.283851889964681 3 8.3 8.7531085637978 -0.453108563797793 4 7.8 8.6446012850905 -0.844601285090507 5 7.8 8.64641560729448 -0.84641560729448 6 8 8.62312677067461 -0.623126770674613 7 8.6 8.69976815450508 -0.0997681545050762 8 8.9 8.71837555755673 0.181624442443269 9 8.9 8.6467311392468 0.253268860753202 10 8.6 8.55371839982428 0.0462816001757201 11 8.3 8.47978029242296 -0.179780292422955 12 8.3 8.52985614093157 -0.229856140931564 13 8.3 8.42259342264061 -0.122593422640613 14 8.4 8.37006005010552 0.029939949894482 15 8.5 8.31351272166048 0.186487278339517 16 8.4 8.25006005010552 0.149939949894483 17 8.6 8.29037558205784 0.309624417942164 18 8.5 8.23800604445784 0.261993955542164 19 8.5 8.30973238023532 0.190267619764682 20 8.5 8.323424735234 0.176575264766006 21 8.5 8.2403118714671 0.259688128532894 22 8.5 8.20955640738234 0.290443592617661 23 8.5 8.11882521913333 0.381174780866667 24 8.5 8.14432582737704 0.355674172622960 25 8.5 8.0473027925298 0.452697207470202 26 8.5 8.0177063109086 0.482293689091391 27 8.5 8.00989987565563 0.490100124344369 28 8.5 7.92269113851126 0.577308861488741 29 8.6 7.94334647825166 0.656653521748343 30 8.4 7.91882887961855 0.481171120381455 31 8.1 8.01267293163444 0.08732706836556 32 8 8.02390776260662 -0.023907762606625 33 8 7.95963591637616 0.0403640836238379 34 8 7.9010285133245 0.0989714866754936 35 8 7.80292475299603 0.197075247003971 36 7.9 7.83538834598146 0.0646116540185419 37 7.8 7.72689686567726 0.0731031343227378 38 7.8 7.68255523989713 0.117444760102867 39 7.9 7.64443934165078 0.255560658349225 40 8.1 7.5699278119766 0.530072188023397 41 8 7.62048302737263 0.379516972627369 42 7.6 7.58244904659382 0.0175509534061760 43 7.3 7.66195754178853 -0.361957541788525 44 7 7.68466081821767 -0.684660818217665 45 6.8 7.58762198496733 -0.787621984967333 46 7 7.4987051189223 -0.498705118922298 47 7.1 7.38339869040839 -0.283398690408389 48 7.2 7.41586228339382 -0.215862283393819 49 7.1 7.29303524626843 -0.193035246268429 50 6.9 7.24582650912406 -0.345826509124061 51 6.7 7.17903949723532 -0.479039497235318 52 6.7 7.11271971431611 -0.412719714316113 53 6.6 7.0993793050234 -0.499379305023397 54 6.9 7.03758925865518 -0.137589258655183 55 7.3 7.11586899183664 0.18413100816336 56 7.5 7.14963112638499 0.350368873615015 57 7.3 7.0656990879426 0.234300912057399 58 7.1 7.03699156054658 0.0630084394534236 59 6.9 7.0150710450393 -0.115071045039294 60 7.1 7.07456740231612 0.0254325976838834 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.603296900080496 0.793406199839008 0.396703099919504 18 0.455999350398963 0.911998700797926 0.544000649601037 19 0.411535281988348 0.823070563976697 0.588464718011652 20 0.470628504209653 0.941257008419306 0.529371495790347 21 0.435587302123784 0.871174604247568 0.564412697876216 22 0.402319277824179 0.804638555648359 0.597680722175821 23 0.296864372683274 0.593728745366549 0.703135627316726 24 0.222369067246097 0.444738134492193 0.777630932753903 25 0.152171513052545 0.304343026105091 0.847828486947455 26 0.100026114252869 0.200052228505738 0.899973885747131 27 0.0658265656903587 0.131653131380717 0.934173434309641 28 0.041798921319287 0.083597842638574 0.958201078680713 29 0.0277156991785588 0.0554313983571176 0.972284300821441 30 0.0175699543428389 0.0351399086856778 0.98243004565716 31 0.034513911924794 0.069027823849588 0.965486088075206 32 0.0592624147243254 0.118524829448651 0.940737585275675 33 0.0590989627782359 0.118197925556472 0.940901037221764 34 0.0406556134083379 0.0813112268166757 0.959344386591662 35 0.0250051172669281 0.0500102345338561 0.974994882733072 36 0.0150116848559668 0.0300233697119337 0.984988315144033 37 0.0120162761147347 0.0240325522294694 0.987983723885265 38 0.0106057777731387 0.0212115555462775 0.989394222226861 39 0.0133472802525592 0.0266945605051184 0.98665271974744 40 0.0393736544864906 0.0787473089729811 0.96062634551351 41 0.338470893363276 0.676941786726551 0.661529106636724 42 0.863998336836892 0.272003326326215 0.136001663163108 43 0.975943302931 0.0481133941379985 0.0240566970689992 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 6 0.222222222222222 NOK 10% type I error level 12 0.444444444444444 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/10o9ol1258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/10o9ol1258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/10q441258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/10q441258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/2jom91258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/2jom91258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/3n0k51258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/3n0k51258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/48ltf1258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/48ltf1258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/5inh21258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/5inh21258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/67yt51258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/67yt51258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/7venm1258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/7venm1258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/89coh1258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/89coh1258555264.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/9tw3w1258555264.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258555346gkzdesxfd7ffgnx/9tw3w1258555264.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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