*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 06:55:26 -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/t12587253580g6zusr971c5fr0.htm/, Retrieved Fri, 20 Nov 2009 14:56:10 +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/t12587253580g6zusr971c5fr0.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:

shwws7

Dataseries X:

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6539 2605 6699 2682 6962 2755 6981 2760 7024 2735 6940 2659 6774 2654 6671 2670 6965 2785 6969 2845 6822 2723 6878 2746 6691 2767 6837 2940 7018 2977 7167 2993 7076 2892 7171 2824 7093 2771 6971 2686 7142 2738 7047 2723 6999 2731 6650 2632 6475 2606 6437 2605 6639 2646 6422 2627 6272 2535 6232 2456 6003 2404 5673 2319 6050 2519 5977 2504 5796 2382 5752 2394 5609 2381 5839 2501 6069 2532 6006 2515 5809 2429 5797 2389 5502 2261 5568 2272 5864 2439 5764 2373 5615 2327 5615 2364 5681 2388 5915 2553 6334 2663 6494 2694 6620 2679 6578 2611 6495 2580 6538 2627 6737 2732 6651 2707 6530 2633 6563 2683

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 Voeding-Mannen[t] = -1163.16083712531 + 2.90769983505941`Landbouw-Mannen`[t] -50.7291223751464M1[t] -214.871464759489M2[t] -125.681135126959M3[t] -125.385774599149M4[t] + 6.32547487764123M5[t] + 182.215203958574M6[t] + 168.449455084770M7[t] + 135.077291917911M8[t] + 30.8732529973186M9[t] -3.65280901495669M10[t] + 74.1754192412731M11[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) -1163.16083712531 348.945854 -3.3334 0.00168 0.00084 `Landbouw-Mannen` 2.90769983505941 0.133016 21.8598 0 0 M1 -50.7291223751464 104.561894 -0.4852 0.629818 0.314909 M2 -214.871464759489 105.264338 -2.0413 0.046862 0.023431 M3 -125.681135126959 106.451279 -1.1806 0.243684 0.121842 M4 -125.385774599149 106.532305 -1.177 0.245132 0.122566 M5 6.32547487764123 105.230571 0.0601 0.952322 0.476161 M6 182.215203958574 104.593079 1.7421 0.088026 0.044013 M7 168.449455084770 104.619468 1.6101 0.114069 0.057035 M8 135.077291917911 104.747325 1.2896 0.203516 0.101758 M9 30.8732529973186 105.06848 0.2938 0.770174 0.385087 M10 -3.65280901495669 104.919016 -0.0348 0.972374 0.486187 M11 74.1754192412731 104.546139 0.7095 0.48152 0.24076 Multiple Linear Regression - Regression Statistics Multiple R 0.957534943039322 R-squared 0.916873167141318 Adjusted R-squared 0.89564929492208 F-TEST (value) 43.2000889220505 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 165.299129084041 Sum Squared Residuals 1284218.6975693 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6539 6360.66811082931 178.331889170690 2 6699 6420.41865574453 278.581344255468 3 6962 6721.8710733364 240.1289266636 4 6981 6736.70493303951 244.295066960493 5 7024 6795.72368663981 228.276313360188 6 6940 6750.62822825623 189.37177174377 7 6774 6722.32398020713 51.6760197928712 8 6671 6735.47501440122 -64.47501440122 9 6965 6965.65645651246 -0.656456512459602 10 6969 7105.59238460375 -136.592384603749 11 6822 6828.68123298273 -6.6812329827309 12 6878 6821.38290994782 56.6170900521758 13 6691 6831.71548410893 -140.715484108925 14 6837 7170.60521318986 -333.60521318986 15 7018 7367.38043671959 -349.380436719588 16 7167 7414.19899460835 -247.198994608349 17 7076 7252.23256074414 -176.232560744139 18 7171 7230.39870104103 -59.3987010410319 19 7093 7062.52486090908 30.4751390909204 20 6971 6781.99821176217 189.001788237830 21 7142 6828.99456426467 313.005435735333 22 7047 6750.8530047265 296.146995273499 23 6999 6851.94283166321 147.057168336794 24 6650 6489.90512875105 160.094871248948 25 6475 6363.57581066436 111.424189335639 26 6437 6196.52576844496 240.474231555042 27 6639 6404.93179131492 234.068208685075 28 6422 6349.9808549766 72.0191450233943 29 6272 6214.18371962793 57.8162803720695 30 6232 6160.36516173917 71.6348382608299 31 6003 5995.39902144228 7.60097855772302 32 5673 5714.87237229537 -41.872372295368 33 6050 6192.20830038666 -142.208300386657 34 5977 6114.06674084849 -137.066740848491 35 5796 5837.15558922747 -41.155589227473 36 5752 5797.87256800691 -45.8725680069128 37 5609 5709.34334777599 -100.343347775994 38 5839 5894.12498559878 -55.1249855987801 39 6069 6073.45401011815 -4.45401011815229 40 6006 6024.31847344995 -18.3184734499521 41 5809 5905.96753711163 -96.9675371116334 42 5797 5965.54927279019 -168.549272790190 43 5502 5579.59794502878 -77.5979450287818 44 5568 5578.21048004758 -10.2104800475758 45 5864 5959.5923135819 -95.5923135819047 46 5764 5733.15806245571 30.8419375442915 47 5615 5677.2320982992 -62.2320982992057 48 5615 5710.64157295513 -95.6415729551306 49 5681 5729.69724662141 -48.69724662141 50 5915 6045.32537702187 -130.325377021869 51 6334 6454.36268851093 -120.362688510935 52 6494 6544.79674392559 -50.796743925586 53 6620 6632.89249587649 -12.8924958764851 54 6578 6611.05863617338 -33.0586361733783 55 6495 6507.15419241273 -12.1541924127327 56 6538 6610.44392149367 -72.4439214936654 57 6737 6811.54836525431 -74.548365254311 58 6651 6704.32980736555 -53.3298073655505 59 6530 6566.98824782738 -36.9882478273842 60 6563 6638.19782033908 -75.1978203390814 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0476702023119076 0.0953404046238152 0.952329797688092 17 0.0191887345156717 0.0383774690313433 0.980811265484328 18 0.0361302910120639 0.0722605820241278 0.963869708987936 19 0.157563264761412 0.315126529522824 0.842436735238588 20 0.35899677750614 0.71799355501228 0.64100322249386 21 0.54608000328305 0.9078399934339 0.45391999671695 22 0.661286140539382 0.677427718921235 0.338713859460618 23 0.67384968763406 0.652300624731881 0.326150312365941 24 0.721877450015828 0.556245099968344 0.278122549984172 25 0.706250361830669 0.587499276338663 0.293749638169331 26 0.908085505790842 0.183828988418317 0.0919144942091583 27 0.989020471179607 0.0219590576407859 0.0109795288203930 28 0.998659389749117 0.00268122050176562 0.00134061025088281 29 0.999794398575635 0.000411202848730928 0.000205601424365464 30 0.99998671145026 2.65770994784890e-05 1.32885497392445e-05 31 0.999990100535414 1.97989291726649e-05 9.89946458633246e-06 32 0.999990336904955 1.93261900893075e-05 9.66309504465374e-06 33 0.999992477822636 1.50443547286259e-05 7.52217736431297e-06 34 0.999997007883762 5.9842324767305e-06 2.99211623836525e-06 35 0.99999057055022 1.88588995606006e-05 9.4294497803003e-06 36 0.999974645172133 5.0709655734324e-05 2.5354827867162e-05 37 0.999930501015286 0.000138997969427331 6.94989847136657e-05 38 0.999836100631809 0.000327798736382624 0.000163899368191312 39 0.99979826869726 0.000403462605481196 0.000201731302740598 40 0.999284082323416 0.00143183535316707 0.000715917676583535 41 0.998222149286572 0.00355570142685617 0.00177785071342809 42 0.998891919672255 0.00221616065549037 0.00110808032774518 43 0.997570944731968 0.00485811053606496 0.00242905526803248 44 0.990143907362887 0.0197121852742256 0.0098560926371128 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.551724137931034 NOK 5% type I error level 19 0.655172413793103 NOK 10% type I error level 21 0.724137931034483 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/102gpx1258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/102gpx1258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/17uq81258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/17uq81258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/2uizp1258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/2uizp1258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/33ngk1258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/33ngk1258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/4v2971258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/4v2971258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/5w12z1258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/5w12z1258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/6df1o1258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/6df1o1258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/7qzk71258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/7qzk71258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/8f0351258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/8f0351258725322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/9y1to1258725322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587253580g6zusr971c5fr0/9y1to1258725322.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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