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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, 20 Nov 2009 05:59:36 -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/t12587220059x3pke4lac6x6s5.htm/, Retrieved Fri, 20 Nov 2009 14:00:17 +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/t12587220059x3pke4lac6x6s5.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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89.1 72.7 82.6 79.7 102.7 115.8 91.8 87.8 94.1 99.2 103.1 111.4 93.2 102.3 91 94.4 94.3 118.5 99.4 112.1 115.7 136.5 116.8 139.8 99.8 104.5 96 123.3 115.9 156.6 109.1 136.2 117.3 147.5 109.8 143.8 112.8 135.8 110.7 121.6 100 128 113.3 129.7 122.4 136.2 112.5 130.5 104.2 99.2 92.5 110.4 117.2 151.6 109.3 129.6 106.1 123.6 118.8 142.7 105.3 119 106 118.1 102 120 112.9 124.3 116.5 123.3 114.8 122.4 100.5 90.5 85.4 91 114.6 137 109.9 127.7 100.7 105.1 115.5 135.6 100.7 112.4 99 102.5 102.3 112.6 108.8 110.8 105.9 103.4 113.2 117.6 95.7 87.5 80.9 87 113.9 130 98.1 102.9 102.8 111.1 104.7 128.9 95.9 106.3 94.6 99 101.6 109.9 103.9 104 110.3 112.9 114.1 113.6

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation TotaleIndustrieleProductie[t] = + 60.5723257968456 + 0.409406491194934Investeringsgoederen[t] -1.73997398875970M1[t] -15.2224134744869M2[t] -6.2587520538734M3[t] -6.80666085283433M4[t] -6.50781928966874M5[t] -6.61544127689258M6[t] -8.39735230028105M7[t] -6.49855556195852M8[t] -11.1638483388052M9[t] -2.95344127395409M10[t] + 0.902654510456986M11[t] + 0.0728314508847354t + 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) 60.5723257968456 4.259408 14.2208 0 0 Investeringsgoederen 0.409406491194934 0.030486 13.4294 0 0 M1 -1.73997398875970 2.199106 -0.7912 0.432877 0.216439 M2 -15.2224134744869 2.093141 -7.2725 0 0 M3 -6.2587520538734 1.945574 -3.2169 0.002374 0.001187 M4 -6.80666085283433 1.927506 -3.5313 0.000953 0.000476 M5 -6.50781928966874 1.922936 -3.3843 0.001468 0.000734 M6 -6.61544127689258 1.91523 -3.4541 0.001197 0.000598 M7 -8.39735230028105 1.928115 -4.3552 7.4e-05 3.7e-05 M8 -6.49855556195852 1.980525 -3.2812 0.001976 0.000988 M9 -11.1638483388052 1.913423 -5.8345 1e-06 0 M10 -2.95344127395409 1.918586 -1.5394 0.130562 0.065281 M11 0.902654510456986 1.900459 0.475 0.637058 0.318529 t 0.0728314508847354 0.023235 3.1345 0.002996 0.001498 Multiple Linear Regression - Regression Statistics Multiple R 0.96132580989825 R-squared 0.924147312776527 Adjusted R-squared 0.902710683778589 F-TEST (value) 43.1106641284607 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.00233118796081 Sum Squared Residuals 414.643657861300 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 89.1 88.6690351688427 0.430964831157306 2 82.6 78.1252725723645 4.4747274276355 3 102.7 101.941339776000 0.758660224000175 4 91.8 90.0028806744654 1.79711932553455 5 94.1 95.041787688138 -0.941787688138045 6 103.1 100.001756344377 3.09824365562285 7 93.2 94.5670777019995 -1.36707770199949 8 91 93.3043946107668 -2.30439461076679 9 94.3 98.5786297226028 -4.27862972260281 10 99.4 104.241666694691 -4.84166669469101 11 115.7 118.160112315143 -2.46011231514323 12 116.8 118.681330676514 -1.88133067651427 13 99.8 102.562138999458 -2.76213899945812 14 96 96.8493729990804 -0.849372999080426 15 115.9 119.51910202737 -3.61910202736996 16 109.1 110.692132258917 -1.59213225891710 17 117.3 115.690098623470 1.60990137652981 18 109.8 114.140504069710 -4.34050406970983 19 112.8 109.156172567647 3.64382743235337 20 110.7 105.314228581886 5.38577141811418 21 100 103.341968799572 -3.3419687995715 22 113.3 112.321198350339 0.97880164966131 23 122.4 118.911267778402 3.48873222159844 24 112.5 115.747827719018 -3.2478277190182 25 104.2 101.266262006742 2.93373799325821 26 92.5 92.4420066732826 0.057993326717399 27 117.2 118.346046982012 -1.14604698201212 28 109.3 108.864026827647 0.435973172352637 29 106.1 106.779260894528 -0.679260894528087 30 118.8 114.564134340012 4.23586565998778 31 105.3 103.152120926189 2.14787907381145 32 106 104.755283273320 1.24471672667962 33 102 100.940694280629 1.05930571937115 34 112.9 110.984380708503 1.91561929149714 35 116.5 114.503901452604 1.99609854739626 36 114.8 113.305612550956 1.49438744904394 37 100.5 98.5784029439627 1.92159705603731 38 85.4 85.3734981547177 0.0265018452823059 39 114.6 113.242689621183 1.35731037881709 40 109.9 108.960131904994 0.939868095006193 41 100.7 100.079218218039 0.620781781961381 42 115.5 112.531325663145 2.96867433685499 43 100.7 101.324015494919 -0.624015494918803 44 99 99.2425194212962 -0.242519421296228 45 102.3 98.7850636564032 3.51493634359684 46 108.8 106.331370487988 2.46862951201191 47 105.9 107.230689688441 -1.33068968844137 48 113.2 112.214438803837 0.985561196162808 49 95.7 98.2241608809947 -2.52416088099471 50 80.9 84.6098496005548 -3.70984960055478 51 113.9 111.250821593435 2.64917840656482 52 98.1 99.6808283339763 -1.58082833397627 53 102.8 103.409634575825 -0.609634575825055 54 104.7 110.662279582756 -5.96227958275578 55 95.9 99.7006133092465 -3.80061330924653 56 94.6 98.6835741127308 -4.08357411273079 57 101.6 98.5536435407937 3.04635645920633 58 103.9 104.421383758479 -0.521383758479346 59 110.3 111.99402876541 -1.69402876541008 60 114.1 111.450790249674 2.64920975032571 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.412306790361790 0.824613580723579 0.58769320963821 18 0.338570421617947 0.677140843235893 0.661429578382053 19 0.775642259353388 0.448715481293223 0.224357740646612 20 0.912071158511812 0.175857682976376 0.0879288414881879 21 0.934596857912147 0.130806284175706 0.0654031420878532 22 0.920353554883096 0.159292890233809 0.0796464451169044 23 0.876955446835663 0.246089106328675 0.123044553164337 24 0.96755062418153 0.0648987516369419 0.0324493758184709 25 0.94581066266743 0.10837867466514 0.05418933733257 26 0.940003945835165 0.119992108329671 0.0599960541648355 27 0.953282500358692 0.0934349992826159 0.0467174996413080 28 0.92915395441313 0.141692091173741 0.0708460455868707 29 0.93004055612148 0.139918887757042 0.0699594438785208 30 0.933929296349473 0.132141407301054 0.0660707036505271 31 0.908426024987212 0.183147950025576 0.0915739750127881 32 0.865316700957472 0.269366598085056 0.134683299042528 33 0.902857181618135 0.19428563676373 0.097142818381865 34 0.877816724801924 0.244366550396152 0.122183275198076 35 0.817051194411165 0.36589761117767 0.182948805588835 36 0.821796760967626 0.356406478064748 0.178203239032374 37 0.769996402982864 0.460007194034273 0.230003597017136 38 0.730905789635586 0.538188420728827 0.269094210364414 39 0.72228300267172 0.55543399465656 0.27771699732828 40 0.679939430071733 0.640121139856533 0.320060569928267 41 0.557715247915976 0.884569504168049 0.442284752084024 42 0.830621784338344 0.338756431323312 0.169378215661656 43 0.730866058853745 0.538267882292511 0.269133941146255 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 2 0.0740740740740741 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/10t5wu1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/10t5wu1258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/1wyjx1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/1wyjx1258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/2ob6k1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/2ob6k1258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/3j7c41258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/3j7c41258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/4cl571258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/4cl571258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/5qglz1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/5qglz1258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/6lan61258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/6lan61258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/7pd8v1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/7pd8v1258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/8t88m1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/8t88m1258721972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/9kn7e1258721972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587220059x3pke4lac6x6s5/9kn7e1258721972.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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