Workshop 7

*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, 27 Nov 2009 15:09:45 -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/27/t1259359855vcdlnvsvfpksm4g.htm/, Retrieved Fri, 27 Nov 2009 23:11:07 +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/27/t1259359855vcdlnvsvfpksm4g.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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132.92 138.04 136.51 131.02 126.51 0 129.61 132.92 138.04 136.51 131.02 0 122.96 129.61 132.92 138.04 136.51 0 124.04 122.96 129.61 132.92 138.04 0 121.29 124.04 122.96 129.61 132.92 0 124.56 121.29 124.04 122.96 129.61 0 118.53 124.56 121.29 124.04 122.96 0 113.14 118.53 124.56 121.29 124.04 0 114.15 113.14 118.53 124.56 121.29 0 122.17 114.15 113.14 118.53 124.56 0 129.23 122.17 114.15 113.14 118.53 0 131.19 129.23 122.17 114.15 113.14 0 129.12 131.19 129.23 122.17 114.15 0 128.28 129.12 131.19 129.23 122.17 0 126.83 128.28 129.12 131.19 129.23 0 138.13 126.83 128.28 129.12 131.19 0 140.52 138.13 126.83 128.28 129.12 0 146.83 140.52 138.13 126.83 128.28 0 135.14 146.83 140.52 138.13 126.83 0 131.84 135.14 146.83 140.52 138.13 0 125.7 131.84 135.14 146.83 140.52 0 128.98 125.7 131.84 135.14 146.83 0 133.25 128.98 125.7 131.84 135.14 0 136.76 133.25 128.98 125.7 131.84 0 133.24 136.76 133.25 128.98 125.7 0 128.54 133.24 136.76 133.25 128.98 0 121.08 128.54 133.24 136.76 133.25 0 120 etc...

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 Y(t)[t] = + 22.2014493267714 + 1.47305280975136`Y(t-1)`[t] -0.609958903931009`Y(t-2)`[t] -0.311570948737318`Y(t-3)`[t] + 0.266930877605454`Y(t-4)`[t] + 1.67219763575734X[t] -2.25507346474391M1[t] + 0.6140703509069M2[t] -2.28209966022831M3[t] + 6.09052972606484M4[t] -0.631935182812322M5[t] + 4.54801419086582M6[t] -6.35229584801417M7[t] + 1.40352294946333M8[t] + 0.884830647112344M9[t] + 4.65001216934383M10[t] + 0.985345882304379M11[t] + 0.0152649517802632t + 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) 22.2014493267714 11.532871 1.9251 0.061732 0.030866 `Y(t-1)` 1.47305280975136 0.158174 9.3129 0 0 `Y(t-2)` -0.609958903931009 0.280548 -2.1742 0.035988 0.017994 `Y(t-3)` -0.311570948737318 0.286298 -1.0883 0.283328 0.141664 `Y(t-4)` 0.266930877605454 0.163244 1.6352 0.110272 0.055136 X 1.67219763575734 1.598698 1.046 0.302181 0.15109 M1 -2.25507346474391 2.113838 -1.0668 0.292787 0.146393 M2 0.6140703509069 2.264139 0.2712 0.787693 0.393846 M3 -2.28209966022831 2.440303 -0.9352 0.355606 0.177803 M4 6.09052972606484 2.251047 2.7056 0.01015 0.005075 M5 -0.631935182812322 2.482619 -0.2545 0.800448 0.400224 M6 4.54801419086582 1.940873 2.3433 0.024449 0.012225 M7 -6.35229584801417 2.327005 -2.7298 0.00955 0.004775 M8 1.40352294946333 2.332968 0.6016 0.55101 0.275505 M9 0.884830647112344 2.975698 0.2974 0.767817 0.383909 M10 4.65001216934383 2.139782 2.1731 0.036073 0.018036 M11 0.985345882304379 2.324637 0.4239 0.67405 0.337025 t 0.0152649517802632 0.03359 0.4544 0.652091 0.326046 Multiple Linear Regression - Regression Statistics Multiple R 0.965438204984186 R-squared 0.932070927643088 Adjusted R-squared 0.901681605799206 F-TEST (value) 30.6710012296883 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.77038156067033 Sum Squared Residuals 291.650531684682 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 132.92 132.983760318567 -0.0637603185665924 2 129.61 126.886235326489 2.72376467351121 3 122.96 123.241262021469 -0.281262021469456 4 124.04 125.855966646979 -1.81596664697931 5 121.29 124.460504182536 -3.17050418253571 6 124.56 126.134473269161 -1.57447326916149 7 118.53 119.632110895046 -1.10211089504641 8 113.14 117.671226042491 -4.53122604249057 9 114.15 111.153199322278 2.99680067772204 10 122.17 122.460744416983 -0.290744416982594 11 129.23 130.078942344692 -0.848942344692285 12 131.19 132.863299752968 -1.67329975296797 13 129.12 126.975166062872 2.14483393712770 14 128.28 125.555930802724 2.72406919727643 15 126.83 123.974129250684 2.85587074931597 16 138.13 131.906598877913 6.22340112208706 17 140.52 142.438508762002 -1.91850876200246 18 146.83 144.489339626827 2.34066037317325 19 135.14 137.533654495603 -2.39365449560344 20 131.84 126.507574564522 5.33242543547753 21 125.7 126.945444639670 -1.24544463967037 22 128.98 129.020809473211 -0.0408094732107547 23 133.25 131.855931195698 1.39406880430221 24 136.76 136.207294287067 0.552705712932555 25 133.24 133.872468316190 -0.632468316189744 26 128.54 128.975900767936 -0.435900767935761 27 121.08 121.364883661894 -0.284883661893834 28 120.23 123.664268007648 -3.43426800764833 29 119.08 120.780053355482 -1.70005335548232 30 125.75 125.869466170903 -0.119466170902747 31 126.89 123.784667023855 3.10533297614469 32 126.6 129.298020432093 -2.69802043209308 33 121.89 125.286905878889 -3.39690587888891 34 123.44 123.731399773180 -0.291399773179585 35 126.46 125.632793506154 0.827206493845888 36 129.49 129.555984974033 -0.0659849740332081 37 127.78 128.19727118068 -0.417271180680044 38 125.29 126.187382759627 -0.897382759627055 39 119.02 120.543677205408 -1.52367720540766 40 119.96 122.555914978614 -2.59591497861357 41 122.86 121.377186851981 1.48281314801903 42 131.89 131.559784919369 0.330215080631395 43 132.73 130.240992588524 2.48900741147555 44 135.01 133.088871069087 1.92112893091275 45 136.71 135.064450159163 1.64554984083724 46 142.73 142.107046336627 0.622953663372934 47 144.43 145.802332953456 -1.37233295345582 48 144.93 143.743420985931 1.18657901406863 49 138.75 139.781334121691 -1.03133412169131 50 130.22 134.334550343225 -4.11455034322483 51 122.19 122.956047860545 -0.766047860545026 52 128.4 126.777251488846 1.62274851115415 53 140.43 135.123746847999 5.30625315200145 54 153.5 154.476936013740 -0.976936013740409 55 149.33 151.428574996970 -2.09857499697038 56 142.97 142.994307891807 -0.0243078918066325 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.957840773850388 0.0843184522992245 0.0421592261496122 22 0.986115019874089 0.0277699602518228 0.0138849801259114 23 0.972503532706071 0.0549929345878572 0.0274964672939286 24 0.962207716395179 0.0755845672096422 0.0377922836048211 25 0.960933915942092 0.0781321681158157 0.0390660840579078 26 0.989374312215197 0.0212513755696055 0.0106256877848027 27 0.991796395572788 0.0164072088544238 0.0082036044272119 28 0.994947544198604 0.0101049116027912 0.00505245580139561 29 0.988049198797882 0.0239016024042354 0.0119508012021177 30 0.989021977097648 0.0219560458047032 0.0109780229023516 31 0.996249017070732 0.00750196585853604 0.00375098292926802 32 0.989360876457439 0.0212782470851227 0.0106391235425614 33 0.983653617108173 0.0326927657836544 0.0163463828918272 34 0.955235626064056 0.0895287478718877 0.0447643739359439 35 0.877423559271052 0.245152881457897 0.122576440728948 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0666666666666667 NOK 5% type I error level 9 0.6 NOK 10% type I error level 14 0.933333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/10q3pt1259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/10q3pt1259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/1fm2d1259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/1fm2d1259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/2mkzg1259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/2mkzg1259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/3lqov1259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/3lqov1259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/46gg51259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/46gg51259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/5tih31259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/5tih31259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/636a61259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/636a61259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/7ojd01259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/7ojd01259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/8g6zg1259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/8g6zg1259359780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/9y3le1259359780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259359855vcdlnvsvfpksm4g/9y3le1259359780.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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