Model 2

*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 05:55:31 -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/t1258722124ftm023dpnrhledg.htm/, Retrieved Fri, 20 Nov 2009 14:02:16 +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/t1258722124ftm023dpnrhledg.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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108.5 98.71 112.3 98.54 116.6 98.2 115.5 96.92 120.1 99.06 132.9 99.65 128.1 99.82 129.3 99.99 132.5 100.33 131 99.31 124.9 101.1 120.8 101.1 122 100.93 122.1 100.85 127.4 100.93 135.2 99.6 137.3 101.88 135 101.81 136 102.38 138.4 102.74 134.7 102.82 138.4 101.72 133.9 103.47 133.6 102.98 141.2 102.68 151.8 102.9 155.4 103.03 156.6 101.29 161.6 103.69 160.7 103.68 156 104.2 159.5 104.08 168.7 104.16 169.9 103.05 169.9 104.66 185.9 104.46 190.8 104.95 195.8 105.85 211.9 106.23 227.1 104.86 251.3 107.44 256.7 108.23 251.9 108.45 251.2 109.39 270.3 110.15 267.2 109.13 243 110.28 229.9 110.17 187.2 109.99 178.2 109.26 175.2 109.11 192.4 107.06 187 109.53 184 108.92 194.1 109.24 212.7 109.12 217.5 109 200.5 107.23 205.9 109.49 196.5 109.04 206.3 109.02

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -918.27639098955 + 10.3421732921795X[t] -1.90632391481678M1[t] + 0.108298714811628M2[t] + 5.16145524896801M3[t] + 29.293192545015M4[t] + 10.8408731493808M5[t] + 11.81365323506M6[t] + 7.45047084987543M7[t] + 9.90629621999926M8[t] + 14.0682807093823M9[t] + 23.1802573531665M10[t] -0.405543323044851M11[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) -918.27639098955 82.359041 -11.1497 0 0 X 10.3421732921795 0.774498 13.3534 0 0 M1 -1.90632391481678 13.586385 -0.1403 0.889001 0.444501 M2 0.108298714811628 14.249395 0.0076 0.993967 0.496984 M3 5.16145524896801 14.24766 0.3623 0.718743 0.359371 M4 29.293192545015 14.431433 2.0298 0.047938 0.023969 M5 10.8408731493808 14.190929 0.7639 0.448648 0.224324 M6 11.81365323506 14.184155 0.8329 0.409039 0.20452 M7 7.45047084987543 14.170264 0.5258 0.60146 0.30073 M8 9.90629621999926 14.163921 0.6994 0.487675 0.243838 M9 14.0682807093823 14.160328 0.9935 0.325449 0.162724 M10 23.1802573531665 14.204123 1.6319 0.109237 0.054618 M11 -0.405543323044851 14.160242 -0.0286 0.977271 0.488635 Multiple Linear Regression - Regression Statistics Multiple R 0.893265188168467 R-squared 0.797922696393646 Adjusted R-squared 0.747403370492058 F-TEST (value) 15.7944050549684 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 8.69304628281498e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 22.3872153977978 Sum Squared Residuals 24056.995836835 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 108.5 100.693210766676 7.80678923332389 2 112.3 100.949663936633 11.3503360633669 3 116.6 102.486481551448 14.1135184485515 4 115.5 113.380237033506 2.11976296649430 5 120.1 117.060168483136 3.03983151686429 6 132.9 124.134830811201 8.76516918879918 7 128.1 121.529817885687 6.57018211431334 8 129.3 125.743812715481 3.55618728451900 9 132.5 133.422136124205 -0.922136124205162 10 131 131.985096009966 -0.985096009966213 11 124.9 126.911785526756 -2.01178552675617 12 120.8 127.317328849801 -6.51732884980103 13 122 123.652835475314 -1.65283547531385 14 122.1 124.840084241568 -2.74008424156777 15 127.4 130.720614639099 -3.32061463909864 16 135.2 141.097261456547 -5.89726145654676 17 137.3 146.225097167082 -8.9250971670819 18 135 146.473925122309 -11.4739251223086 19 136 148.005781513666 -12.0057815136663 20 138.4 154.184789268975 -15.7847892689747 21 134.7 159.174147621732 -24.4741476217321 22 138.4 156.909733644119 -18.5097336441189 23 133.9 151.422736229222 -17.5227362292217 24 133.6 146.760614639099 -13.1606146390987 25 141.2 141.751638736628 -0.55163873662805 26 151.8 146.041539490536 5.75846050946409 27 155.4 152.439178552676 2.9608214473244 28 156.6 158.575534320330 -1.97553432033029 29 161.6 164.944430825927 -3.34443082592689 30 160.7 165.813789178684 -5.11378917868437 31 156 166.828536905433 -10.8285369054331 32 159.5 168.043301480495 -8.54330148049534 33 168.7 173.032659833253 -4.33265983325275 34 169.9 170.664824122718 -0.76482412271762 35 169.9 163.729922446915 6.17007755308467 36 185.9 162.067031111524 23.8329688884757 37 190.8 165.228372109876 25.5716278901245 38 195.8 176.550950702465 19.2490492975346 39 211.9 185.53413308765 26.3658669123499 40 227.1 195.497092973411 31.6029070265889 41 251.3 203.7275806716 47.5724193283999 42 256.7 212.870677658101 43.8293223418988 43 251.9 210.782773397196 41.1172266028039 44 251.2 222.960241661969 28.2397583380313 45 270.3 234.982277853408 35.3177221465918 46 267.2 233.545237739169 33.6547622608308 47 243 221.852936348964 21.1470636510357 48 229.9 221.120840609869 8.77915939013054 49 187.2 217.352925502460 -30.1529255024603 50 178.2 211.817761628798 -33.6177616287978 51 175.2 215.319592169127 -40.1195921691272 52 192.4 218.249874216206 -25.8498742162061 53 187 225.342722852255 -38.3427228522554 54 184 220.006777229705 -36.0067772297050 55 194.1 218.953090298018 -24.8530902980179 56 212.7 220.16785487308 -7.46785487308026 57 217.5 223.088778567402 -5.58877856740171 58 200.5 213.895108484028 -13.3951084840281 59 205.9 213.682619448142 -7.78261944814245 60 196.5 209.434184789707 -12.9341847897066 61 206.3 207.321017409046 -1.02101740904615 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00185641552691344 0.00371283105382688 0.998143584473087 17 0.000180318462387388 0.000360636924774777 0.999819681537613 18 0.000150765398518817 0.000301530797037634 0.99984923460148 19 2.64464834021305e-05 5.2892966804261e-05 0.999973553516598 20 4.06840388595918e-06 8.13680777191835e-06 0.999995931596114 21 2.02377698747084e-06 4.04755397494169e-06 0.999997976223012 22 3.08705348193656e-07 6.17410696387312e-07 0.999999691294652 23 4.16837259290224e-08 8.33674518580449e-08 0.999999958316274 24 8.97461230218056e-09 1.79492246043611e-08 0.999999991025388 25 3.35877216383567e-08 6.71754432767134e-08 0.999999966412278 26 2.09094509567139e-07 4.18189019134278e-07 0.99999979090549 27 1.53477869209684e-07 3.06955738419368e-07 0.99999984652213 28 8.56829075858595e-08 1.71365815171719e-07 0.999999914317092 29 4.11781909114705e-08 8.2356381822941e-08 0.99999995882181 30 1.11496889292279e-08 2.22993778584558e-08 0.999999988850311 31 2.78039505371385e-09 5.56079010742769e-09 0.999999997219605 32 9.90702611598446e-10 1.98140522319689e-09 0.999999999009297 33 2.02092387577511e-09 4.04184775155021e-09 0.999999997979076 34 3.59866706434258e-09 7.19733412868516e-09 0.999999996401333 35 1.90463266338430e-08 3.80926532676861e-08 0.999999980953673 36 9.89511362508275e-07 1.97902272501655e-06 0.999999010488637 37 2.88159849590332e-06 5.76319699180665e-06 0.999997118401504 38 1.57821656654409e-06 3.15643313308818e-06 0.999998421783433 39 1.38550378502142e-06 2.77100757004283e-06 0.999998614496215 40 2.79154236492112e-06 5.58308472984223e-06 0.999997208457635 41 0.000136976191993031 0.000273952383986063 0.999863023808007 42 0.00498037159787968 0.00996074319575936 0.99501962840212 43 0.162388843489480 0.324777686978961 0.83761115651052 44 0.183578027150105 0.367156054300211 0.816421972849895 45 0.145465500556288 0.290931001112576 0.854534499443712 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.9 NOK 5% type I error level 27 0.9 NOK 10% type I error level 27 0.9 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/10c5kz1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/10c5kz1258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/111xp1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/111xp1258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/2tehz1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/2tehz1258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/3nju91258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/3nju91258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/4lzrg1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/4lzrg1258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/5tsz51258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/5tsz51258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/69fxr1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/69fxr1258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/7w27f1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/7w27f1258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/83ep81258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/83ep81258721724.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/9pxsn1258721724.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258722124ftm023dpnrhledg/9pxsn1258721724.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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