*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 12:04:16 -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/t1258571184ogwv2uk93mys3wp.htm/, Retrieved Wed, 18 Nov 2009 20:06:36 +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/t1258571184ogwv2uk93mys3wp.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 «

90398 562000 90269 561000 90390 555000 88219 544000 87032 537000 87175 543000 92603 594000 93571 611000 94118 613000 92159 611000 89528 594000 89955 595000 89587 591000 89488 589000 88521 584000 86587 573000 85159 567000 84915 569000 91378 621000 92729 629000 92194 628000 89664 612000 86285 595000 86858 597000 87184 593000 86629 590000 85220 580000 84816 574000 84831 573000 84957 573000 90951 620000 92134 626000 91790 620000 86625 588000 83324 566000 82719 557000 83614 561000 81640 549000 78665 532000 77828 526000 75728 511000 72187 499000 79357 555000 81329 565000 77304 542000 75576 527000 72932 510000 74291 514000 74988 517000 73302 508000 70483 493000 69848 490000 66466 469000 67610 478000 75091 528000 76207 534000 73454 518000 72008 506000 71362 502000 74250 516000

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 25551.4147450011 + 0.113859995860027X[t] -203.055106917519t + 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) 25551.4147450011 2931.892536 8.715 0 0 X 0.113859995860027 0.004793 23.7558 0 0 t -203.055106917519 11.518337 -17.6289 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.98906748563434 R-squared 0.978254491139036 Adjusted R-squared 0.977491490828125 F-TEST (value) 1282.11545545903 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1154.56767677021 Sum Squared Residuals 75982511.653826 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 90398 89337.6773114187 1060.32268858134 2 90269 89020.762208641 1248.23779135899 3 90390 88134.5471265633 2255.45287343668 4 88219 86679.0320651855 1539.96793481449 5 87032 85678.9569872478 1353.04301275220 6 87175 86159.0618554905 1015.93814450955 7 92603 91762.8665374343 840.133462565718 8 93571 93495.4313601372 75.5686398627849 9 94118 93520.0962449397 597.90375506025 10 92159 93089.3211463022 -930.321146302176 11 89528 90950.6461097642 -1422.64610976420 12 89955 90861.4509987067 -906.450998706712 13 89587 90202.955908349 -615.955908349086 14 89488 89772.1808097115 -284.180809711513 15 88521 88999.8257234939 -478.825723493861 16 86587 87544.310662116 -957.31066211605 17 85159 86658.0955800384 -1499.09558003837 18 84915 86682.760464841 -1767.76046484090 19 91378 92400.4251426448 -1022.42514264477 20 92729 93108.2500026075 -379.250002607462 21 92194 92791.33489983 -597.334899829916 22 89664 90766.519859152 -1102.51985915197 23 86285 88627.844822614 -2342.844822614 24 86858 88652.5097074165 -1794.50970741653 25 87184 87994.0146170589 -810.014617058907 26 86629 87449.3795225613 -820.379522561307 27 85220 86107.7244570435 -887.724457043521 28 84816 85221.5093749658 -405.509374965843 29 84831 84904.5942721883 -73.5942721882968 30 84957 84701.5391652708 255.460834729223 31 90951 89849.9038637745 1101.09613622549 32 92134 90330.0087320171 1803.99126798285 33 91790 89443.7936499395 2346.20635006053 34 86625 85597.218675501 1027.78132449890 35 83324 82889.243659663 434.756340337006 36 82719 81661.4485900052 1057.55140999477 37 83614 81913.8334665278 1700.16653347218 38 81640 80344.45840929 1295.54159071002 39 78665 78205.783372752 459.216627247989 40 77828 77319.5682906743 508.431709325668 41 75728 75408.6132458564 319.386754143586 42 72187 73839.2381886186 -1652.23818861858 43 79357 80012.3428498625 -655.342849862546 44 81329 80947.8877015453 381.112298454708 45 77304 78126.0526898472 -822.052689847161 46 75576 76215.0976450292 -639.097645029243 47 72932 74076.4226084913 -1144.42260849127 48 74291 74328.8074850139 -37.8074850138576 49 74988 74467.3323656764 520.667634323583 50 73302 73239.5372960187 62.4627039813413 51 70483 71328.5822512007 -845.582251200741 52 69848 70783.9471567031 -935.94715670314 53 66466 68189.832136725 -1723.83213672506 54 67610 69011.5169925478 -1401.51699254778 55 75091 74501.4616786316 589.538321368405 56 76207 74981.5665468742 1225.43345312577 57 73454 72956.7515061963 497.248493803711 58 72008 71387.3764489585 620.62355104155 59 71362 70728.8813586008 633.118641399175 60 74250 72119.8661937237 2130.13380627632 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.253429613923606 0.506859227847213 0.746570386076394 7 0.150411363603450 0.300822727206901 0.84958863639655 8 0.093348098894058 0.186696197788116 0.906651901105942 9 0.0634180723614153 0.126836144722831 0.936581927638585 10 0.132994583950587 0.265989167901174 0.867005416049413 11 0.220148956592069 0.440297913184137 0.779851043407931 12 0.146700086312588 0.293400172625175 0.853299913687412 13 0.104718849919712 0.209437699839425 0.895281150080288 14 0.103176280403056 0.206352560806113 0.896823719596944 15 0.0826053076397339 0.165210615279468 0.917394692360266 16 0.0579255037226126 0.115851007445225 0.942074496277387 17 0.0449966375076766 0.0899932750153532 0.955003362492323 18 0.0312666925991815 0.062533385198363 0.968733307400819 19 0.0431092656558182 0.0862185313116365 0.956890734344182 20 0.111840763230410 0.223681526460819 0.88815923676959 21 0.145277300697779 0.290554601395559 0.85472269930222 22 0.135180436980454 0.270360873960907 0.864819563019546 23 0.198372224770816 0.396744449541631 0.801627775229184 24 0.256158561628388 0.512317123256776 0.743841438371612 25 0.348222975226650 0.696445950453301 0.65177702477335 26 0.43495756209783 0.86991512419566 0.56504243790217 27 0.489771999295715 0.97954399859143 0.510228000704285 28 0.542274651377182 0.915450697245636 0.457725348622818 29 0.593932770784587 0.812134458430825 0.406067229215413 30 0.64503903600892 0.70992192798216 0.35496096399108 31 0.880546586747892 0.238906826504217 0.119453413252108 32 0.96627679717118 0.0674464056576402 0.0337232028288201 33 0.987532584584885 0.0249348308302305 0.0124674154151153 34 0.984485644438085 0.0310287111238298 0.0155143555619149 35 0.976256481737923 0.0474870365241541 0.0237435182620771 36 0.968762493192642 0.0624750136147157 0.0312375068073579 37 0.975627981030735 0.0487440379385307 0.0243720189692653 38 0.981900972412218 0.0361980551755636 0.0180990275877818 39 0.98357038914111 0.0328592217177815 0.0164296108588908 40 0.992185795332206 0.015628409335588 0.007814204667794 41 0.99971095290244 0.000578094195119388 0.000289047097559694 42 0.99975004033579 0.000499919328418039 0.000249959664209020 43 0.999612387053812 0.000775225892375739 0.000387612946187870 44 0.999043585170943 0.0019128296581143 0.00095641482905715 45 0.999352139846214 0.00129572030757224 0.000647860153786121 46 0.998954564081901 0.00209087183619796 0.00104543591809898 47 0.998702371146427 0.00259525770714534 0.00129762885357267 48 0.996638396885367 0.00672320622926566 0.00336160311463283 49 0.996335050791384 0.00732989841723175 0.00366494920861587 50 0.997719251114724 0.00456149777055281 0.00228074888527640 51 0.996716936765447 0.0065661264691068 0.0032830632345534 52 0.99517557686294 0.00964884627411816 0.00482442313705908 53 0.988532998931182 0.0229340021376351 0.0114670010688175 54 0.992054387427707 0.0158912251445861 0.00794561257229305 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.244897959183673 NOK 5% type I error level 21 0.428571428571429 NOK 10% type I error level 26 0.530612244897959 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/10z54t1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/10z54t1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/1v3ig1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/1v3ig1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/2gfqa1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/2gfqa1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/366in1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/366in1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/4af2u1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/4af2u1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/529xz1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/529xz1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/6g3xu1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/6g3xu1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/7xin91258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/7xin91258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/82all1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/82all1258571048.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/963sf1258571048.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258571184ogwv2uk93mys3wp/963sf1258571048.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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