WS 7: Multiple Regression 2

*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, 20 Nov 2009 03:32:04 -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/20/t1258713153oaraz214qb980su.htm/, Retrieved Fri, 20 Nov 2009 11:32:45 +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/20/t1258713153oaraz214qb980su.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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79.8 109.87 83.4 95.74 113.6 123.06 112.9 123.39 104 120.28 109.9 115.33 99 110.4 106.3 114.49 128.9 132.03 111.1 123.16 102.9 118.82 130 128.32 87 112.24 87.5 104.53 117.6 132.57 103.4 122.52 110.8 131.8 112.6 124.55 102.5 120.96 112.4 122.6 135.6 145.52 105.1 118.57 127.7 134.25 137 136.7 91 121.37 90.5 111.63 122.4 134.42 123.3 137.65 124.3 137.86 120 119.77 118.1 130.69 119 128.28 142.7 147.45 123.6 128.42 129.6 136.9 151.6 143.95 110.4 135.64 99.2 122.48 130.5 136.83 136.2 153.04 129.7 142.71 128 123.46 121.6 144.37 135.8 146.15 143.8 147.61 147.5 158.51 136.2 147.4 156.6 165.05 123.3 154.64 104.5 126.2 139.8 157.36 136.5 154.15 112.1 123.21 118.5 113.07 94.4 110.45 102.3 113.57 111.4 122.44 99.2 114.93 87.8 111.85 115.8 126.04

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 Investgoed[t] = + 2.26371392062023 + 0.970890252831042Uitvoer[t] -27.0259952474602M1[t] -18.0960455070252M2[t] -10.3481032400426M3[t] -13.9322023492286M4[t] -13.4373301649736M5[t] -0.228784107182265M6[t] -14.9263279733971M7[t] -8.48247154905135M8[t] -4.74716796666329M9[t] -9.9347654845262M10[t] -11.4879879092140M11[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) 2.26371392062023 7.552077 0.2997 0.765692 0.382846 Uitvoer 0.970890252831042 0.051592 18.8186 0 0 M1 -27.0259952474602 3.190163 -8.4717 0 0 M2 -18.0960455070252 3.432269 -5.2723 3e-06 2e-06 M3 -10.3481032400426 3.120221 -3.3165 0.001764 0.000882 M4 -13.9322023492286 3.117429 -4.4691 4.9e-05 2.5e-05 M5 -13.4373301649736 3.149149 -4.267 9.5e-05 4.8e-05 M6 -0.228784107182265 3.295157 -0.0694 0.944942 0.472471 M7 -14.9263279733971 3.232022 -4.6183 3e-05 1.5e-05 M8 -8.48247154905135 3.210537 -2.6421 0.011156 0.005578 M9 -4.74716796666329 3.116377 -1.5233 0.134385 0.067193 M10 -9.9347654845262 3.169961 -3.134 0.002968 0.001484 M11 -11.4879879092140 3.159798 -3.6357 0.000686 0.000343 Multiple Linear Regression - Regression Statistics Multiple R 0.969120209849607 R-squared 0.939193981138947 Adjusted R-squared 0.923669040153146 F-TEST (value) 60.4958165057075 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.92674642846338 Sum Squared Residuals 1140.82302740770 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 79.8 81.9094307517059 -2.10943075170587 2 83.4 77.1207012196388 6.27929878036116 3 113.6 111.393365193966 2.20663480603439 4 112.9 108.129659868214 4.77034013178617 5 104 105.605063366164 -1.60506336616429 6 109.9 114.007702672442 -4.10770267244194 7 99 94.52366985977 4.47633014022993 8 106.3 104.938467418195 1.36153258180521 9 128.9 125.703186035239 3.19681396476068 10 111.1 111.903791974765 -0.80379197476507 11 102.9 106.136905852791 -3.23690585279057 12 130 126.848351163899 3.15164883610054 13 87 84.210440650916 2.78955934908395 14 87.5 85.6548265420237 1.84517345797628 15 117.6 120.626531498389 -3.0265314983888 16 103.4 107.284985348251 -3.8849853482508 17 110.8 116.789719078778 -5.9897190787779 18 112.6 122.959310803544 -10.3593108035442 19 102.5 104.776270929666 -2.27627092966585 20 112.4 112.812387368655 -0.412387368654536 21 135.6 138.80049554593 -3.20049554593010 22 105.1 107.447405714271 -2.34740571427059 23 127.7 121.117742453974 6.58225754602643 24 137 134.984411482624 2.01558851737642 25 91 93.0746686592635 -2.07466865926346 26 90.5 92.5481473371241 -2.04814733712411 27 122.4 122.422678466126 -0.0226784661262075 28 123.3 121.974554873584 1.32544512641552 29 124.3 122.673314010934 1.62668598906599 30 120 118.318455395012 1.68154460498822 31 118.1 114.223033089712 3.87696691028810 32 119 118.327044004735 0.672955995265134 33 142.7 140.674313733894 2.02568626610601 34 123.6 117.010674704656 6.58932529534365 35 129.6 123.690601623976 5.90939837602416 36 151.6 142.023365815649 9.57663418435136 37 110.4 106.929272567162 3.47072743283759 38 99.2 103.082306580341 -3.88230658034092 39 130.5 124.762523975449 5.73747602455095 40 136.2 136.916555864654 -0.716555864654212 41 129.7 127.382131737165 2.31786826283543 42 128 121.901040427958 6.09895957204168 43 121.6 127.504811748441 -5.90481174844057 44 135.8 135.676852822826 0.123147177174423 45 143.8 140.829656174347 2.97034382565304 46 147.5 146.224762412342 1.27523758765761 47 136.2 133.884949278702 2.31505072129821 48 156.6 162.509150150384 -5.90915015038364 49 123.3 125.376187370952 -2.07618737095221 50 104.5 106.694018320872 -2.1940183208724 51 139.8 144.694900866070 -4.89490086607033 52 136.5 137.994244045297 -1.49424404529667 53 112.1 108.449771806959 3.65022819304077 54 118.5 111.813490701044 6.68650929895621 55 94.4 94.5722143724116 -0.172214372411607 56 102.3 104.045248385590 -1.74524838559023 57 111.4 116.392348510590 -4.99234851058963 58 99.2 103.913365193966 -4.7133651939656 59 87.8 99.3698007905582 -11.5698007905582 60 115.8 124.634721387445 -8.83472138744469 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.42567927537233 0.85135855074466 0.57432072462767 17 0.278205141820166 0.556410283640333 0.721794858179833 18 0.250701133668846 0.501402267337691 0.749298866331154 19 0.154307549099868 0.308615098199737 0.845692450900132 20 0.0920035425870273 0.184007085174055 0.907996457412973 21 0.0497721142270282 0.0995442284540565 0.950227885772972 22 0.0338840099723498 0.0677680199446996 0.96611599002765 23 0.282027984585377 0.564055969170755 0.717972015414623 24 0.207292483307993 0.414584966615985 0.792707516692007 25 0.139321462106380 0.278642924212760 0.86067853789362 26 0.104406598454236 0.208813196908472 0.895593401545764 27 0.0672309883276781 0.134461976655356 0.932769011672322 28 0.0505026578288477 0.101005315657695 0.949497342171152 29 0.0557484422301361 0.111496884460272 0.944251557769864 30 0.0815684313546397 0.163136862709279 0.91843156864536 31 0.0723223421864274 0.144644684372855 0.927677657813573 32 0.0446921870903954 0.0893843741807908 0.955307812909605 33 0.0282804214236456 0.0565608428472912 0.971719578576354 34 0.0489514056610633 0.0979028113221265 0.951048594338937 35 0.0625016057923279 0.125003211584656 0.937498394207672 36 0.347875399433127 0.695750798866253 0.652124600566873 37 0.340546494780574 0.681092989561147 0.659453505219426 38 0.298796901821419 0.597593803642839 0.70120309817858 39 0.5741596042603 0.8516807914794 0.4258403957397 40 0.458789312192327 0.917578624384655 0.541210687807673 41 0.38508529423509 0.77017058847018 0.61491470576491 42 0.353211078201432 0.706422156402865 0.646788921798568 43 0.634170856292149 0.731658287415702 0.365829143707851 44 0.540025137082665 0.91994972583467 0.459974862917335 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 5 0.172413793103448 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/10rxqy1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/10rxqy1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/1b9dm1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/1b9dm1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/2z7ao1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/2z7ao1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/32vsl1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/32vsl1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/49axs1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/49axs1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/53vh11258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/53vh11258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/6povh1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/6povh1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/7qqzi1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/7qqzi1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/8ccgq1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/8ccgq1258713120.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/93e1c1258713120.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713153oaraz214qb980su/93e1c1258713120.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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