*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 09:55:24 -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/t1258736179yyrq2g0u19at775.htm/, Retrieved Fri, 20 Nov 2009 17:56:31 +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/t1258736179yyrq2g0u19at775.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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8 5560 8.1 3922 7.7 3759 7.5 4138 7.6 4634 7.8 3996 7.8 4308 7.8 4143 7.5 4429 7.5 5219 7.1 4929 7.5 5755 7.5 5592 7.6 4163 7.7 4962 7.7 5208 7.9 4755 8.1 4491 8.2 5732 8.2 5731 8.2 5040 7.9 6102 7.3 4904 6.9 5369 6.7 5578 6.7 4619 6.9 4731 7 5011 7.1 5299 7.2 4146 7.1 4625 6.9 4736 7 4219 6.8 5116 6.4 4205 6.7 4121 6.6 5103 6.4 4300 6.3 4578 6.2 3809 6.5 5526 6.8 4247 6.8 3830 6.4 4394 6.1 4826 5.8 4409 6.1 4569 7.2 4106 7.3 4794 6.9 3914 6.1 3793 5.8 4405 6.2 4022 7.1 4100 7.7 4788 7.9 3163 7.7 3585 7.4 3903 7.5 4178 8 3863 8.1 4187

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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WerklM[t] = + 6.52362952102874 + 0.000158604824453189Bouwv[t] + 0.0284956355211745M1[t] -0.0471686646110958M2[t] -0.275876137837123M3[t] -0.39960341957532M4[t] -0.232418826118232M5[t] + 0.210864635565684M6[t] + 0.257811253422546M7[t] + 0.213211850240498M8[t] + 0.0753688758530607M9[t] -0.228691681107129M10[t] -0.366391706061917M11[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) 6.52362952102874 0.795352 8.2022 0 0 Bouwv 0.000158604824453189 0.000158 1.0046 0.320133 0.160066 M1 0.0284956355211745 0.425141 0.067 0.946839 0.47342 M2 -0.0471686646110958 0.442524 -0.1066 0.915558 0.457779 M3 -0.275876137837123 0.438748 -0.6288 0.532474 0.266237 M4 -0.39960341957532 0.437016 -0.9144 0.365082 0.182541 M5 -0.232418826118232 0.437736 -0.531 0.597897 0.298948 M6 0.210864635565684 0.442207 0.4768 0.635636 0.317818 M7 0.257811253422546 0.43655 0.5906 0.557582 0.278791 M8 0.213211850240498 0.437794 0.487 0.628465 0.314233 M9 0.0753688758530607 0.437962 0.1721 0.86409 0.432045 M10 -0.228691681107129 0.439227 -0.5207 0.604991 0.302496 M11 -0.366391706061917 0.436755 -0.8389 0.405686 0.202843 Multiple Linear Regression - Regression Statistics Multiple R 0.359465052427854 R-squared 0.129215123916960 Adjusted R-squared -0.0884810951038002 F-TEST (value) 0.593557042461255 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.836476353996469 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.690237336376786 Sum Squared Residuals 22.868523865369 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.43396798050962 0.566032019490377 2 8.1 7.09850897792305 1.00149102207695 3 7.7 6.84394891831115 0.856051081688851 4 7.5 6.78033286504071 0.71966713495929 5 7.6 7.02618545142658 0.57381454857342 6 7.8 7.36827903510936 0.431720964890638 7 7.8 7.46471035819562 0.335289641804381 8 7.8 7.3939411589788 0.406058841021206 9 7.5 7.30145916438497 0.198540835615031 10 7.5 7.1226964187428 0.377303581257201 11 7.1 6.93900099469659 0.160999005303413 12 7.5 7.43640028575684 0.063599714243164 13 7.5 7.43904333489214 0.0609566651078591 14 7.6 7.13673274061626 0.463267259383735 15 7.7 7.03475052212834 0.665249477871665 16 7.7 6.95004002720562 0.749959972794378 17 7.9 7.04537663518542 0.854623364814584 18 8.1 7.44678842321369 0.65321157678631 19 8.2 7.69056362821696 0.509436371783041 20 8.2 7.64580562021046 0.554194379789542 21 8.2 7.39836671212587 0.801633287874132 22 7.9 7.26274447873496 0.637255521265037 23 7.3 6.93503587408526 0.364964125914743 24 6.9 7.3751788235179 -0.475178823517905 25 6.7 7.4368228673498 -0.736822867349796 26 6.7 7.20905654056692 -0.509056540566918 27 6.9 6.99811280767965 -0.098112807679648 28 7 6.91879487678834 0.081205123211656 29 7.1 7.13165765968795 -0.0316576596879509 30 7.2 7.39206975877734 -0.192069758777340 31 7.1 7.51498808754728 -0.414988087547280 32 6.9 7.48799381987954 -0.587993819879535 33 7 7.2681521512498 -0.268152151249799 34 6.8 7.10636012182412 -0.306360121824120 35 6.4 6.82417110179248 -0.424171101792477 36 6.7 7.17724000260033 -0.477240002600326 37 6.6 7.36148557573453 -0.761485575734532 38 6.4 7.15846160156635 -0.758461601566351 39 6.3 6.97384626953831 -0.67384626953831 40 6.2 6.72815187779561 -0.528151877795611 41 6.5 7.16766095483882 -0.667660954838824 42 6.8 7.40808884604711 -0.608088846047113 43 6.8 7.388897252107 -0.588897252106994 44 6.4 7.43375096991655 -1.03375096991654 45 6.1 7.36442527969289 -1.26442527969289 46 5.8 6.99422651093572 -1.19422651093572 47 6.1 6.88190325789344 -0.781903257893439 48 7.2 7.17486093023353 0.0251390697664716 49 7.3 7.3124766849785 -0.0124766849784967 50 6.9 7.09724013932742 -0.19724013932742 51 6.1 6.84934148234256 -0.749341482342558 52 5.8 6.82268035316971 -1.02268035316971 53 6.2 6.92911929886123 -0.729119298861229 54 7.1 7.38477393685249 -0.284773936852494 55 7.7 7.54084067393315 0.159159326066852 56 7.9 7.23850843101467 0.661491568985331 57 7.7 7.16759669254648 0.532403307453522 58 7.4 6.9139724697624 0.486027530237598 59 7.5 6.81988877153224 0.680111228467759 60 8 7.1363199578914 0.863680042108597 61 8.1 7.21620355653541 0.883796443464588 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0941201440765923 0.188240288153185 0.905879855923408 17 0.0483429637253793 0.0966859274507587 0.95165703627462 18 0.0233947956617210 0.0467895913234419 0.976605204338279 19 0.0101214543368396 0.0202429086736793 0.98987854566316 20 0.00418759659172501 0.00837519318345002 0.995812403408275 21 0.00652389819525539 0.0130477963905108 0.993476101804745 22 0.00500999305297843 0.0100199861059569 0.994990006947022 23 0.00272217759595647 0.00544435519191293 0.997277822404044 24 0.00236870385774549 0.00473740771549099 0.997631296142254 25 0.0232206805166318 0.0464413610332636 0.976779319483368 26 0.102686043943336 0.205372087886673 0.897313956056664 27 0.140065089775731 0.280130179551462 0.859934910224269 28 0.200974093774471 0.401948187548942 0.799025906225529 29 0.245533950679169 0.491067901358339 0.75446604932083 30 0.234516145656401 0.469032291312803 0.765483854343599 31 0.234526839099737 0.469053678199473 0.765473160900263 32 0.266258277293215 0.532516554586431 0.733741722706785 33 0.236897104672323 0.473794209344646 0.763102895327677 34 0.251662274186424 0.503324548372848 0.748337725813576 35 0.221264117824420 0.442528235648839 0.77873588217558 36 0.198706226622532 0.397412453245065 0.801293773377468 37 0.19510662028544 0.39021324057088 0.80489337971456 38 0.195413364514495 0.39082672902899 0.804586635485505 39 0.216643900312282 0.433287800624564 0.783356099687718 40 0.172570831920131 0.345141663840261 0.82742916807987 41 0.329573970950378 0.659147941900756 0.670426029049622 42 0.257887808077945 0.515775616155891 0.742112191922055 43 0.792647533725612 0.414704932548776 0.207352466274388 44 0.734202475732465 0.53159504853507 0.265797524267535 45 0.807894496466596 0.384211007066809 0.192105503533404 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.1 NOK 5% type I error level 8 0.266666666666667 NOK 10% type I error level 9 0.3 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/103j4s1258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/103j4s1258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/15zy91258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/15zy91258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/2gqau1258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/2gqau1258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/3trru1258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/3trru1258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/485731258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/485731258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/54tji1258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/54tji1258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/6jjfr1258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/6jjfr1258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/7ur6k1258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/7ur6k1258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/8oe431258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/8oe431258736114.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/912b71258736114.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736179yyrq2g0u19at775/912b71258736114.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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