workshop 7/module1

*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: Tue, 24 Nov 2009 11:24:19 -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/24/t12590871317jkygbwgjhk4zew.htm/, Retrieved Tue, 24 Nov 2009 19:25:43 +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/24/t12590871317jkygbwgjhk4zew.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:

» Textbox « » Textfile « » CSV «

114.08 136.49 112.95 142.62 135.31 141.71 134.31 149.51 133.03 147.39 140.11 131.96 124.69 136.38 131.68 127.34 150.95 133.85 137.26 125.14 130.51 141.25 143.15 149.32 118.01 120.92 122.56 134.85 147.97 131.93 135.74 134.22 151.62 143.07 154.82 145.37 145.59 134.32 147.12 126.31 175.86 162.21 140.66 124.09 152.69 153.91 154.38 154.34 132.45 138.70 136.44 150.98 153.24 146.39 154.11 178.30 155.93 168.23 142.53 162.52 148.73 158.86 147.73 152.17 166.79 171.01 144.30 171.49 156.07 189.62 161.70 177.46 152.10 179.98 140.45 156.96 155.56 167.89 174.53 194.78 167.16 192.78 159.48 165.06 173.22 196.60 176.13 151.64 180.31 187.02 185.84 210.99 169.43 219.08 195.25 235.68 174.99 241.44 156.42 187.46 182.08 229.57 182.00 208.44 153.28 215.09 136.72 217.00 130.19 171.08 132.04 178.41 143.89 196.34 133.38 172.11 127.98 154.93 150.45 182.26 133.55 181.74

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation InvoerEU[t] = + 82.0823843016541 + 0.405853219646718InvoerAM[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) 82.0823843016541 10.050437 8.167 0 0 InvoerAM 0.405853219646718 0.059794 6.7875 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.662171795257425 R-squared 0.438471486434441 Adjusted R-squared 0.428954054001127 F-TEST (value) 46.0703545317146 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 6.18692874709836e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.0076440268092 Sum Squared Residuals 11576.6313797264 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114.08 137.477290251235 -23.3972902512352 2 112.95 139.965170487669 -27.0151704876691 3 135.31 139.595844057791 -4.2858440577906 4 134.31 142.761499171035 -8.451499171035 5 133.03 141.901090345384 -8.87109034538395 6 140.11 135.638775166235 4.47122483376492 7 124.69 137.432646397074 -12.7426463970736 8 131.68 133.763733291467 -2.08373329146725 9 150.95 136.405837751367 14.5441622486326 10 137.26 132.870856208244 4.38914379175551 11 130.51 139.409151576753 -8.89915157675312 12 143.15 142.684387059302 0.465612940697883 13 118.01 131.158155621335 -13.1481556213353 14 122.56 136.811690971014 -14.2516909710141 15 147.97 135.626599569646 12.3434004303543 16 135.74 136.556003442637 -0.816003442636669 17 151.62 140.14780443651 11.4721955634899 18 154.82 141.081266841698 13.7387331583024 19 145.59 136.596588764601 8.99341123539866 20 147.12 133.345704475231 13.7742955247689 21 175.86 147.915835060548 27.9441649394517 22 140.66 132.444710327615 8.21528967238457 23 152.69 144.547253337481 8.14274666251944 24 154.38 144.721770221929 9.65822977807134 25 132.45 138.374225866654 -5.92422586665398 26 136.44 143.358103403916 -6.91810340391568 27 153.24 141.495237125737 11.7447628742628 28 154.11 154.446013364664 -0.336013364664014 29 155.93 150.359071442822 5.57092855717844 30 142.53 148.041649558639 -5.51164955863881 31 148.73 146.556226774732 2.17377322526817 32 147.73 143.841068735295 3.88893126470472 33 166.79 151.487343393439 15.3026566065606 34 144.3 151.68215293887 -7.38215293886986 35 156.07 159.040271811065 -2.97027181106488 36 161.7 154.105096660161 7.5949033398392 37 152.1 155.127846773671 -3.02784677367051 38 140.45 145.785105657403 -5.33510565740307 39 155.56 150.221081348142 5.33891865185832 40 174.53 161.134474424442 13.3955255755581 41 167.16 160.322767985149 6.83723201485149 42 159.48 149.072516736541 10.4074832634585 43 173.22 161.873127284199 11.3468727158010 44 176.13 143.625966528883 32.5040334711175 45 180.31 157.985053439983 22.3249465600166 46 185.84 167.713355114915 18.1266448850848 47 169.43 170.996707661857 -1.56670766185720 48 195.25 177.733871107993 17.5161288920073 49 174.99 180.071585653158 -5.08158565315781 50 156.42 158.163628856628 -1.74362885662798 51 182.08 175.254107935951 6.82589206404874 52 182 166.678429404816 15.3215705951839 53 153.28 169.377353315467 -16.0973533154668 54 136.72 170.152532964992 -33.432532964992 55 130.19 151.515753118815 -21.3257531188147 56 132.04 154.490657218825 -22.4506572188252 57 143.89 161.767605447091 -17.8776054470908 58 133.38 151.933781935051 -18.5537819350508 59 127.98 144.961223621520 -16.9812236215202 60 150.45 156.053192114465 -5.60319211446504 61 133.55 155.842148440249 -22.2921484402487 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.326189404113174 0.652378808226349 0.673810595886826 6 0.53395555111065 0.9320888977787 0.46604444888935 7 0.401683345183835 0.80336669036767 0.598316654816165 8 0.287483874154723 0.574967748309445 0.712516125845277 9 0.444946976899485 0.889893953798969 0.555053023100515 10 0.334427756784060 0.668855513568119 0.66557224321594 11 0.245667223357378 0.491334446714756 0.754332776642622 12 0.245455456284912 0.490910912569823 0.754544543715088 13 0.255882170926344 0.511764341852688 0.744117829073656 14 0.228607012201552 0.457214024403105 0.771392987798448 15 0.270652801046952 0.541305602093904 0.729347198953048 16 0.207173549307149 0.414347098614298 0.792826450692851 17 0.247299305905333 0.494598611810667 0.752700694094667 18 0.293979928809691 0.587959857619383 0.706020071190309 19 0.264975352601441 0.529950705202882 0.735024647398559 20 0.260498326825826 0.520996653651652 0.739501673174174 21 0.489071672935862 0.978143345871723 0.510928327064138 22 0.446792937682433 0.893585875364867 0.553207062317567 23 0.384135820350593 0.768271640701187 0.615864179649407 24 0.33085228468771 0.66170456937542 0.66914771531229 25 0.275546628188883 0.551093256377766 0.724453371811117 26 0.237520477304389 0.475040954608778 0.762479522695611 27 0.212467741885758 0.424935483771517 0.787532258114242 28 0.168750033336141 0.337500066672281 0.83124996666386 29 0.127837377876334 0.255674755752669 0.872162622123666 30 0.101138416100487 0.202276832200974 0.898861583899513 31 0.0716534128556518 0.143306825711304 0.928346587144348 32 0.0502357330508296 0.100471466101659 0.94976426694917 33 0.0485425242379713 0.0970850484759425 0.951457475762029 34 0.0396526468854478 0.0793052937708957 0.960347353114552 35 0.0280188135182369 0.0560376270364737 0.971981186481763 36 0.0197707850220263 0.0395415700440527 0.980229214977974 37 0.0129625866340042 0.0259251732680084 0.987037413365996 38 0.00838577949259833 0.0167715589851967 0.991614220507402 39 0.00528297407998074 0.0105659481599615 0.99471702592002 40 0.00441798654885528 0.00883597309771056 0.995582013451145 41 0.00273056966232526 0.00546113932465051 0.997269430337675 42 0.00219312796157651 0.00438625592315302 0.997806872038423 43 0.00163618991097458 0.00327237982194916 0.998363810089025 44 0.0550037723720441 0.110007544744088 0.944996227627956 45 0.181538892118653 0.363077784237305 0.818461107881347 46 0.273131858208746 0.546263716417493 0.726868141791254 47 0.226360009961055 0.45272001992211 0.773639990038945 48 0.282069578535098 0.564139157070195 0.717930421464902 49 0.240464625684858 0.480929251369715 0.759535374315143 50 0.224293787068309 0.448587574136619 0.77570621293169 51 0.236474793661368 0.472949587322736 0.763525206338632 52 0.8951951853279 0.209609629344198 0.104804814672099 53 0.901283299328306 0.197433401343387 0.0987167006716937 54 0.943303300619555 0.113393398760890 0.0566966993804448 55 0.896730496419142 0.206539007161715 0.103269503580857 56 0.835865026720133 0.328269946559734 0.164134973279867 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0769230769230769 NOK 5% type I error level 8 0.153846153846154 NOK 10% type I error level 11 0.211538461538462 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/10gusl1259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/10gusl1259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/1z7661259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/1z7661259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/2uzh51259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/2uzh51259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/39yrk1259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/39yrk1259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/4dpm01259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/4dpm01259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/5tc4d1259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/5tc4d1259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/6vmlp1259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/6vmlp1259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/7e0zj1259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/7e0zj1259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/8al1s1259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/8al1s1259087054.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/9ckz51259087054.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590871317jkygbwgjhk4zew/9ckz51259087054.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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