model 3

*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 05:33:44 -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/t12587205359vm8jlb6c7adny9.htm/, Retrieved Fri, 20 Nov 2009 13:35:47 +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/t12587205359vm8jlb6c7adny9.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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98.71 153.4 98.54 145 98.2 137.7 96.92 148.3 99.06 152.2 99.65 169.4 99.82 168.6 99.99 161.1 100.33 174.1 99.31 179 101.1 190.6 101.1 190 100.93 181.6 100.85 174.8 100.93 180.5 99.6 196.8 101.88 193.8 101.81 197 102.38 216.3 102.74 221.4 102.82 217.9 101.72 229.7 103.47 227.4 102.98 204.2 102.68 196.6 102.9 198.8 103.03 207.5 101.29 190.7 103.69 201.6 103.68 210.5 104.2 223.5 104.08 223.8 104.16 231.2 103.05 244 104.66 234.7 104.46 250.2 104.95 265.7 105.85 287.6 106.23 283.3 104.86 295.4 107.44 312.3 108.23 333.8 108.45 347.7 109.39 383.2 110.15 407.1 109.13 413.6 110.28 362.7 110.17 321.9 109.99 239.4 109.26 191 109.11 159.7 107.06 163.4 109.53 157.6 108.92 166.2 109.24 176.7 109.12 198.3 109 226.2 107.23 216.2 109.49 235.9 109.04 226.9

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] = + 95.9882710311442 + 0.0112456275625973X[t] + 0.355621378278568M1[t] + 0.281404269153141M2[t] + 0.174446779390013M3[t] -1.62886313825417M4[t] + 0.502574320639195M5[t] + 0.315918698325612M6[t] + 0.359135015305834M7[t] + 0.290375545247334M8[t] + 0.172803055667315M9[t] -1.28073177452812M10[t] + 0.310383374592550M11[t] + 0.191057566869932t + 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) 95.9882710311442 0.540531 177.5813 0 0 X 0.0112456275625973 0.001895 5.9349 0 0 M1 0.355621378278568 0.510941 0.696 0.489923 0.244961 M2 0.281404269153141 0.511393 0.5503 0.584797 0.292398 M3 0.174446779390013 0.512072 0.3407 0.734904 0.367452 M4 -1.62886313825417 0.510748 -3.1892 0.002569 0.001284 M5 0.502574320639195 0.509681 0.9861 0.329263 0.164632 M6 0.315918698325612 0.507863 0.6221 0.536977 0.268488 M7 0.359135015305834 0.506874 0.7085 0.482192 0.241096 M8 0.290375545247334 0.506595 0.5732 0.569307 0.284654 M9 0.172803055667315 0.507326 0.3406 0.734944 0.367472 M10 -1.28073177452812 0.507632 -2.523 0.015158 0.007579 M11 0.310383374592550 0.506564 0.6127 0.543077 0.271539 t 0.191057566869932 0.006825 27.993 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.983059435402643 R-squared 0.966405853534163 Adjusted R-squared 0.956911855619905 F-TEST (value) 101.791243505837 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.799905919004726 Sum Squared Residuals 29.4330760459046 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.71 98.260029244395 0.449970755604924 2 98.54 98.2824064306138 0.257593569386186 3 98.2 98.2844134265137 -0.0844134265136707 4 96.92 96.791364727903 0.128635272097050 5 99.06 99.1577177011604 -0.0977177011603769 6 99.65 99.3555444397934 0.2944555602066 7 99.82 99.5808218215935 0.23917817840651 8 99.99 99.6187777116854 0.371222288314563 9 100.33 99.838455947289 0.49154405271089 10 99.31 98.6310822590203 0.678917740979667 11 101.1 100.543704254737 0.556295745262929 12 101.1 100.417631070477 0.682368929523104 13 100.93 100.869846744100 0.0601532559004348 14 100.85 100.910216934418 -0.0602169344184278 15 100.93 101.058417088632 -0.12841708863202 16 99.6 99.6294684671281 -0.0294684671281111 17 101.88 101.918226610204 -0.0382266102036215 18 101.81 101.958614562960 -0.148614562960275 19 102.38 102.409929058769 -0.0299290587685652 20 102.74 102.589579856149 0.150420143850757 21 102.82 102.62370523697 0.196294763029932 22 101.72 101.493926378883 0.226073621116796 23 103.47 103.250234151480 0.219765848520165 24 102.98 102.870009784305 0.109990215695044 25 102.68 103.331221959978 -0.651221959977714 26 102.9 103.47280279836 -0.572802798359934 27 103.03 103.654739835261 -0.624739835261341 28 101.29 101.853560941435 -0.563560941435444 29 103.69 104.298633307631 -0.608633307631066 30 103.68 104.403121337495 -0.723121337494523 31 104.2 104.783588379658 -0.583588379658447 32 104.08 104.909260164739 -0.829260164738662 33 104.16 105.065962885992 -0.905962885991797 34 103.05 103.947429655468 -0.897429655467536 35 104.66 105.625018035126 -0.965018035125986 36 104.46 105.679999454624 -1.21999945462363 37 104.95 106.400985626992 -1.45098562699238 38 105.85 106.764105328358 -0.914105328357773 39 106.23 106.799849206945 -0.569849206945402 40 104.86 105.323668949679 -0.463668949678577 41 107.44 107.836215081250 -0.396215081249775 42 108.23 108.082398018402 0.14760198159804 43 108.45 108.472986125372 -0.0229861253722201 44 109.39 108.994504000656 0.395495999344144 45 110.15 109.336759576692 0.81324042330816 46 109.13 108.147378892523 0.982621107476772 47 110.28 109.357149165578 0.922850834422376 48 110.17 108.779001753301 1.39099824669896 49 109.99 108.397916424535 1.59208357546473 50 109.26 107.97046850825 1.28953149174995 51 109.11 107.702580442648 1.40741955735243 52 107.06 106.131936913855 0.928063086145082 53 109.53 108.389207299755 1.14079270024484 54 108.92 108.490321641350 0.429678358650158 55 109.24 108.842674614607 0.397325385392722 56 109.12 109.207878266771 -0.0878782667708028 57 109 109.595116353057 -0.595116353057185 58 107.23 108.220182814106 -0.9901828141057 59 109.49 110.223894393079 -0.733894393079484 60 109.04 110.003357937293 -0.963357937293478 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00185688852709852 0.00371377705419704 0.998143111472901 18 0.000202733529975828 0.000405467059951656 0.999797266470024 19 6.0573593497185e-05 0.00012114718699437 0.999939426406503 20 1.57929101545725e-05 3.15858203091449e-05 0.999984207089845 21 2.27212665418015e-06 4.54425330836029e-06 0.999997727873346 22 1.21136027346300e-06 2.42272054692599e-06 0.999998788639727 23 2.13266540452214e-07 4.26533080904428e-07 0.99999978673346 24 5.89006119327157e-08 1.17801223865431e-07 0.999999941099388 25 1.67515974243679e-08 3.35031948487358e-08 0.999999983248403 26 1.99193396655019e-09 3.98386793310039e-09 0.999999998008066 27 2.34764861818634e-10 4.69529723637268e-10 0.999999999765235 28 8.40896352235791e-11 1.68179270447158e-10 0.99999999991591 29 1.49233513051788e-11 2.98467026103577e-11 0.999999999985077 30 2.13557463945499e-12 4.27114927890998e-12 0.999999999997864 31 2.24078089118563e-13 4.48156178237126e-13 0.999999999999776 32 1.08855820067812e-12 2.17711640135623e-12 0.999999999998911 33 1.21436964402142e-11 2.42873928804284e-11 0.999999999987856 34 1.19128820982817e-10 2.38257641965634e-10 0.99999999988087 35 1.53409912999757e-10 3.06819825999514e-10 0.99999999984659 36 1.27231370971582e-08 2.54462741943165e-08 0.999999987276863 37 1.15842401551478e-08 2.31684803102957e-08 0.99999998841576 38 1.03033910712324e-08 2.06067821424647e-08 0.99999998969661 39 1.00064023707592e-07 2.00128047415185e-07 0.999999899935976 40 7.9178416342053e-07 1.58356832684106e-06 0.999999208215837 41 0.00010228634717868 0.00020457269435736 0.999897713652821 42 0.000662115238995922 0.00132423047799184 0.999337884761004 43 0.0478180989087926 0.0956361978175853 0.952181901091207 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 26 0.962962962962963 NOK 5% type I error level 26 0.962962962962963 NOK 10% type I error level 27 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/10bp9s1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/10bp9s1258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/1fd671258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/1fd671258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/2vajw1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/2vajw1258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/3prv91258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/3prv91258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/4sjup1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/4sjup1258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/52dka1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/52dka1258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/6gja41258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/6gja41258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/7oait1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/7oait1258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/8w9mg1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/8w9mg1258720420.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/96cvp1258720420.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587205359vm8jlb6c7adny9/96cvp1258720420.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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