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 04:59:59 -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/t1258718477199vs857ecg115t.htm/, Retrieved Fri, 20 Nov 2009 13:01:29 +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/t1258718477199vs857ecg115t.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] = + 97.1336370267663 + 0.0352680312321223X[t] -0.994110622434554M1[t] -0.687493175700802M2[t] -0.466465397677703M3[t] -2.20315379946010M4[t] + 0.00931861749677947M5[t] -0.271665593540829M6[t] -0.305962182715962M7[t] -0.447910526269306M8[t] -0.704493275398668M9[t] -2.09188703780570M10[t] -0.159814522917262M11[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) 97.1336370267663 2.264442 42.8952 0 0 X 0.0352680312321223 0.007098 4.969 9e-06 5e-06 M1 -0.994110622434554 2.137059 -0.4652 0.643953 0.321976 M2 -0.687493175700802 2.143613 -0.3207 0.749847 0.374923 M3 -0.466465397677703 2.149238 -0.217 0.829119 0.414559 M4 -2.20315379946010 2.144095 -1.0275 0.309423 0.154712 M5 0.00931861749677947 2.140064 0.0044 0.996544 0.498272 M6 -0.271665593540829 2.131882 -0.1274 0.899144 0.449572 M7 -0.305962182715962 2.127209 -0.1438 0.886248 0.443124 M8 -0.447910526269306 2.125494 -0.2107 0.834007 0.417004 M9 -0.704493275398668 2.127379 -0.3312 0.741999 0.371 M10 -2.09188703780570 2.129257 -0.9824 0.330909 0.165455 M11 -0.159814522917262 2.12708 -0.0751 0.940428 0.470214 Multiple Linear Regression - Regression Statistics Multiple R 0.627797045839455 R-squared 0.394129130764746 Adjusted R-squared 0.239438696066384 F-TEST (value) 2.54785715440825 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0111036875022026 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.36067973745561 Sum Squared Residuals 530.825909994002 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 98.71 101.549642395339 -2.83964239533927 2 98.54 101.560008379723 -3.02000837972326 3 98.2 101.523579529752 -3.32357952975187 4 96.92 100.16073225903 -3.24073225902997 5 99.06 102.510749997792 -3.45074999779212 6 99.65 102.836375923947 -3.18637592394701 7 99.82 102.773864909786 -2.95386490978619 8 99.99 102.367406331992 -2.37740633199193 9 100.33 102.569307988880 -2.23930798888015 10 99.31 101.354727579511 -2.04472757951052 11 101.1 103.695909256692 -2.59590925669158 12 101.1 103.834562960870 -2.73456296086957 13 100.93 102.544200876085 -1.61420087608518 14 100.85 102.610995710441 -1.76099571044051 15 100.93 103.033051266487 -2.1030512664867 16 99.6 101.871231773788 -2.27123177378791 17 101.88 103.977900097048 -2.09790009704842 18 101.81 103.809773585954 -1.99977358595359 19 102.38 104.456149999558 -2.07614999955843 20 102.74 104.494068615289 -1.75406861528891 21 102.82 104.114047756847 -1.29404775684712 22 101.72 103.142816762979 -1.42281676297912 23 103.47 104.993772806034 -1.52377280603368 24 102.98 104.335369004366 -1.3553690043657 25 102.68 103.073221344567 -0.393221344567015 26 102.9 103.457428460011 -0.557428460011436 27 103.03 103.985288109754 -0.955288109754006 28 101.29 101.656096783272 -0.366096783271949 29 103.69 104.252990740659 -0.562990740658968 30 103.68 104.285892007587 -0.60589200758724 31 104.2 104.710079824430 -0.510079824429701 32 104.08 104.578711890246 -0.498711890245997 33 104.16 104.583112572234 -0.423112572234342 34 103.05 103.647149609598 -0.597149609598473 35 104.66 105.251229434028 -0.591229434028176 36 104.46 105.957698441043 -1.49769844104334 37 104.95 105.510242302707 -0.56024230270667 38 105.85 106.589229633424 -0.73922963342391 39 106.23 106.658604877149 -0.428604877148875 40 104.86 105.348659653275 -0.488659653275163 41 107.44 108.157161798055 -0.71716179805491 42 108.23 108.634440258508 -0.404440258507925 43 108.45 109.090369303459 -0.640369303459294 44 109.39 110.200436068646 -0.810436068646293 45 110.15 110.786759265965 -0.636759265964651 46 109.13 109.628607706566 -0.498607706566422 47 110.28 109.765537431740 0.51446256826017 48 110.17 108.486416280387 1.6835837196135 49 109.99 104.582693081302 5.40730691869814 50 109.26 103.182337816401 6.07766218359912 51 109.11 102.299476216859 6.81052378314144 52 107.06 100.693279530635 6.36672046936499 53 109.53 102.701197366446 6.82880263355442 54 108.92 102.723518224004 6.19648177599577 55 109.24 103.059535962766 6.18046403723362 56 109.12 103.679377093827 5.44062290617313 57 109 104.406772416074 4.59322758392627 58 107.23 102.666698341345 4.56330165865453 59 109.49 105.293551071507 4.19644892849327 60 109.04 105.135953313335 3.90404668666512 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0010699946216479 0.0021399892432958 0.998930005378352 17 9.03722580906049e-05 0.000180744516181210 0.99990962774191 18 1.13370937557175e-05 2.26741875114349e-05 0.999988662906244 19 2.75200155473553e-06 5.50400310947105e-06 0.999997247998445 20 2.52730321115333e-06 5.05460642230665e-06 0.999997472696789 21 3.15259649148898e-07 6.30519298297797e-07 0.99999968474035 22 7.1782861872708e-08 1.43565723745416e-07 0.999999928217138 23 1.08193775816879e-08 2.16387551633758e-08 0.999999989180622 24 1.37238712068433e-08 2.74477424136866e-08 0.99999998627613 25 2.54795327754182e-08 5.09590655508363e-08 0.999999974520467 26 1.45513626366300e-08 2.91027252732601e-08 0.999999985448637 27 4.56497269100838e-09 9.12994538201676e-09 0.999999995435027 28 6.42818989219183e-08 1.28563797843837e-07 0.999999935718101 29 1.03680137988326e-07 2.07360275976653e-07 0.999999896319862 30 1.17627070202785e-07 2.3525414040557e-07 0.99999988237293 31 1.1307243890761e-07 2.2614487781522e-07 0.999999886927561 32 9.79050966051753e-08 1.95810193210351e-07 0.999999902094903 33 1.24347037638512e-07 2.48694075277024e-07 0.999999875652962 34 2.46677310881236e-07 4.93354621762471e-07 0.99999975332269 35 1.56085044927271e-06 3.12170089854542e-06 0.99999843914955 36 6.95325746222679e-05 0.000139065149244536 0.999930467425378 37 0.00424843733960981 0.00849687467921962 0.99575156266039 38 0.0366396932670628 0.0732793865341255 0.963360306732937 39 0.0972195695501208 0.194439139100242 0.90278043044988 40 0.197830534359731 0.395661068719462 0.802169465640269 41 0.513430724690708 0.973138550618585 0.486569275309292 42 0.581647190148437 0.836705619703126 0.418352809851563 43 0.861814319871053 0.276371360257894 0.138185680128947 44 0.961649199654906 0.076701600690188 0.038350800345094 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 22 0.758620689655172 NOK 10% type I error level 24 0.827586206896552 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/10y0f31258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/10y0f31258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/1tahm1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/1tahm1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/2zlfj1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/2zlfj1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/3hvzf1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/3hvzf1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/42idt1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/42idt1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/55qim1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/55qim1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/63hth1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/63hth1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/75gik1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/75gik1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/88hyx1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/88hyx1258718394.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/967iv1258718394.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258718477199vs857ecg115t/967iv1258718394.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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