*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: Thu, 19 Nov 2009 06:31:28 -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/19/t1258637530wmksbslgt3yxnbb.htm/, Retrieved Thu, 19 Nov 2009 14:32:23 +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/19/t1258637530wmksbslgt3yxnbb.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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110.5 55 110.8 48.7 104.2 70.3 88.9 94.8 89.8 58.5 90 62.4 93.9 56.7 91.3 65.1 87.8 114.4 99.7 50.7 73.5 44.5 79.2 72 96.9 61.2 95.2 68.4 95.6 78.7 89.7 64.1 92.8 64.6 88 71.9 101.1 71 92.7 76.4 95.8 117.3 103.8 66.1 81.8 57.3 87.1 75 105.9 63.8 108.1 62.2 102.6 75.4 93.7 58 103.5 62.1 100.6 99.2 113.3 70.7 102.4 73.3 102.1 111.2 106.9 68.9 87.3 57.6 93.1 72.9 109.1 75.9 120.3 79.4 104.9 96.9 92.6 75.2 109.8 60.3 111.4 88.9 117.9 90.5 121.6 79.9 117.8 116.3 124.2 95.2 106.8 81.5 102.7 89.1 116.8 76 113.6 100.5 96.1 83.9 85 75.1 83.2 69.5 84.9 95.1 83 90.1 79.6 78.4 83.2 113.8 83.8 73.6 82.8 56.5 71.4 97.7

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 The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation prod[t] = + 68.0312982366911 + 0.229514405745129`inv `[t] + 24.5735355099470M1[t] + 25.0803868545787M2[t] + 14.0488543217235M3[t] + 5.09316380538653M4[t] + 13.3292942013657M5[t] + 7.78424888359052M6[t] + 16.411509807828M7[t] + 12.3623368066073M8[t] + 3.00635086491699M9[t] + 19.3761303959791M10[t] + 4.75718490958852M11[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) 68.0312982366911 11.617085 5.8561 0 0 `inv ` 0.229514405745129 0.129095 1.7779 0.081896 0.040948 M1 24.5735355099470 7.288152 3.3717 0.001502 0.000751 M2 25.0803868545787 7.133824 3.5157 0.000983 0.000491 M3 14.0488543217235 7.027721 1.9991 0.051403 0.025701 M4 5.09316380538653 7.10123 0.7172 0.476786 0.238393 M5 13.3292942013657 7.415723 1.7974 0.078693 0.039346 M6 7.78424888359052 7.033145 1.1068 0.274015 0.137008 M7 16.411509807828 7.063913 2.3233 0.024541 0.01227 M8 12.3623368066073 7.080958 1.7459 0.08737 0.043685 M9 3.00635086491699 8.235492 0.365 0.716713 0.358357 M10 19.3761303959791 7.155684 2.7078 0.009415 0.004708 M11 4.75718490958852 7.573057 0.6282 0.532932 0.266466 Multiple Linear Regression - Regression Statistics Multiple R 0.614117486163206 R-squared 0.377140286811415 Adjusted R-squared 0.218112274933479 F-TEST (value) 2.37153368364369 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0175319062285035 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.1116343779799 Sum Squared Residuals 5803.01567184553 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.5 105.228126062621 5.27187393737922 2 110.8 104.289036651058 6.51096334894232 3 104.2 98.2150152822973 5.9849847177027 4 88.9 94.882427706716 -5.98242770671593 5 89.8 94.787185174147 -4.98718517414693 6 90 90.1372460387778 -0.137246038777776 7 93.9 97.456274850268 -3.55627485026802 8 91.3 95.3350228573064 -4.03502285730637 9 87.8 97.294097118851 -9.49409711885096 10 99.7 99.0438090039484 0.656190996051607 11 73.5 83.001874201938 -9.50187420193796 12 79.2 84.5563354503405 -5.35633545034049 13 96.9 106.651115378240 -9.75111537824013 14 95.2 108.810470444237 -13.6104704442368 15 95.6 100.142936290556 -4.5429362905564 16 89.7 87.8363354503405 1.86366454965950 17 92.8 96.1872230491922 -3.38722304919221 18 88 92.3176328933565 -4.31763289335650 19 101.1 100.738330852423 0.361669147576616 20 92.7 97.9285356422263 -5.22853564222632 21 95.8 97.9596888955118 -2.15968889551185 22 103.8 102.578330852423 1.22166914757662 23 81.8 85.9396585954756 -4.13965859547562 24 87.1 85.2448786675759 1.85512133242411 25 105.9 107.247852833177 -1.34785283317747 26 108.1 107.387481128617 0.712518871383038 27 102.6 99.3855387515975 3.21446124840253 28 93.7 86.4362975752952 7.2637024247048 29 103.5 95.6134370348294 7.88656296517062 30 100.6 98.5833761701985 2.01662382980146 31 113.3 100.669476530700 12.6305234693002 32 102.4 97.2170409844164 5.18295901558358 33 102.1 96.5596510204666 5.54034897953343 34 106.9 103.220971188510 3.67902881149027 35 87.3 86.0085129171992 1.29148708280084 36 93.1 84.7628984155111 8.33710158448889 37 109.1 110.024977142694 -0.92497714269355 38 120.3 111.335128907433 8.96487109256682 39 104.9 104.320098475118 0.579901524882256 40 92.6 90.3839453541114 2.21605464588856 41 109.8 95.2003111044882 14.5996888955119 42 111.4 96.2193777910237 15.1806222089763 43 117.9 105.213861764453 12.6861382355466 44 121.6 98.7318360623343 22.8681639376657 45 117.8 97.7301744897667 20.0698255102333 46 124.2 109.257200059607 14.9427999403934 47 106.8 91.4939072145077 15.3060927854922 48 102.7 88.4810317885822 14.2189682114178 49 116.8 110.047928583268 6.75207141673194 50 113.6 116.177882868655 -2.57788286865542 51 96.1 101.336411200431 -5.23641120043107 52 85 90.360993913537 -5.36099391353692 53 83.2 97.3118436373433 -14.1118436373433 54 84.9 97.6423671066435 -12.7423671066435 55 83 105.122056002155 -22.1220560021553 56 79.6 98.3875644537166 -18.7875644537166 57 83.2 97.156388475404 -13.9563884754039 58 83.8 104.299688895512 -20.4996888955119 59 82.8 85.7560470708795 -2.95604707087951 60 71.4 90.4548556779903 -19.0548556779903 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.209887016147859 0.419774032295718 0.79011298385214 17 0.107378880919970 0.214757761839941 0.89262111908003 18 0.0461704898151589 0.0923409796303177 0.95382951018484 19 0.0363449999768493 0.0726899999536987 0.96365500002315 20 0.0165218069675441 0.0330436139350881 0.983478193032456 21 0.0101325058766649 0.0202650117533298 0.989867494123335 22 0.00505279747581958 0.0101055949516392 0.99494720252418 23 0.00352581484248761 0.00705162968497521 0.996474185157512 24 0.00196505396621746 0.00393010793243492 0.998034946033783 25 0.000777192321977265 0.00155438464395453 0.999222807678023 26 0.000357633152275751 0.000715266304551501 0.999642366847724 27 0.000130279214427562 0.000260558428855124 0.999869720785572 28 4.75730427334324e-05 9.51460854668649e-05 0.999952426957267 29 6.29326012836504e-05 0.000125865202567301 0.999937067398716 30 6.05353059205804e-05 0.000121070611841161 0.99993946469408 31 0.000129685727751855 0.000259371455503709 0.999870314272248 32 8.56086493629671e-05 0.000171217298725934 0.999914391350637 33 5.77543215715624e-05 0.000115508643143125 0.999942245678428 34 2.32062683285219e-05 4.64125366570438e-05 0.999976793731671 35 1.20972345965863e-05 2.41944691931725e-05 0.999987902765403 36 8.61494758849676e-06 1.72298951769935e-05 0.999991385052412 37 2.91531545926922e-06 5.83063091853844e-06 0.99999708468454 38 3.27450558586208e-06 6.54901117172416e-06 0.999996725494414 39 9.21611265676386e-07 1.84322253135277e-06 0.999999078388734 40 2.56925726715466e-07 5.13851453430933e-07 0.999999743074273 41 1.10792036203647e-06 2.21584072407293e-06 0.999998892079638 42 5.2799082870685e-06 1.0559816574137e-05 0.999994720091713 43 1.43417887654176e-05 2.86835775308352e-05 0.999985658211235 44 0.000573856094067586 0.00114771218813517 0.999426143905932 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 22 0.758620689655172 NOK 5% type I error level 25 0.862068965517241 NOK 10% type I error level 27 0.93103448275862 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/10q9e11258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/10q9e11258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/19zj41258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/19zj41258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/2gkm41258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/2gkm41258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/3ag0g1258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/3ag0g1258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/4cp4j1258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/4cp4j1258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/50xb01258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/50xb01258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/6vk1w1258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/6vk1w1258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/7fdt11258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/7fdt11258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/8pwhg1258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/8pwhg1258637484.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/9myjl1258637484.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258637530wmksbslgt3yxnbb/9myjl1258637484.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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