Model 4

*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 17:35:27 -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/21/t1258763780kpaniovcu4bg4cl.htm/, Retrieved Sat, 21 Nov 2009 01:36:32 +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/21/t1258763780kpaniovcu4bg4cl.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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117.33 102.7 111.1 107.47 103.86 104.08 119.04 103.1 117.33 111.1 107.47 103.86 123.68 100 119.04 117.33 111.1 107.47 125.9 107.2 123.68 119.04 117.33 111.1 124.54 107 125.9 123.68 119.04 117.33 119.39 119 124.54 125.9 123.68 119.04 118.8 110.4 119.39 124.54 125.9 123.68 114.81 101.7 118.8 119.39 124.54 125.9 117.9 102.4 114.81 118.8 119.39 124.54 120.53 98.8 117.9 114.81 118.8 119.39 125.15 105.6 120.53 117.9 114.81 118.8 126.49 104.4 125.15 120.53 117.9 114.81 131.85 106.3 126.49 125.15 120.53 117.9 127.4 107.2 131.85 126.49 125.15 120.53 131.08 108.5 127.4 131.85 126.49 125.15 122.37 106.9 131.08 127.4 131.85 126.49 124.34 114.2 122.37 131.08 127.4 131.85 119.61 125.9 124.34 122.37 131.08 127.4 119.97 110.6 119.61 124.34 122.37 131.08 116.46 110.5 119.97 119.61 124.34 122.37 117.03 106.7 116.46 119.97 119.61 124.34 120.96 104.7 117.03 116.46 119.97 119.61 124.71 107.4 120.96 117.03 116.46 119.97 127.08 109.8 124.71 120.96 117.03 116.46 131.91 103.4 127.08 124.71 120.96 117.03 137.6 etc...

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] = + 10.8562216284785 + 0.264895487886645X[t] + 0.741179769060709Y1[t] + 0.169868749827528Y2[t] -0.362243934241926Y3[t] + 0.152135238199125Y4[t] + 3.65823272078654M1[t] -0.440463601056253M2[t] + 1.91915658338744M3[t] -0.108805188726336M4[t] -3.88665101019026M5[t] -10.5879834879979M6[t] -2.76413230559056M7[t] -5.59498712522036M8[t] -3.68967115962482M9[t] + 2.44620732778347M10[t] + 0.210606343946619M11[t] -0.0384352271612887t + 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) 10.8562216284785 17.667674 0.6145 0.542568 0.271284 X 0.264895487886645 0.113558 2.3327 0.025061 0.01253 Y1 0.741179769060709 0.150447 4.9265 1.7e-05 8e-06 Y2 0.169868749827528 0.18357 0.9254 0.360617 0.180308 Y3 -0.362243934241926 0.186675 -1.9405 0.059764 0.029882 Y4 0.152135238199125 0.150634 1.01 0.318898 0.159449 M1 3.65823272078654 2.01009 1.8199 0.076653 0.038327 M2 -0.440463601056253 2.124443 -0.2073 0.836858 0.418429 M3 1.91915658338744 2.102695 0.9127 0.367148 0.183574 M4 -0.108805188726336 2.226024 -0.0489 0.961272 0.480636 M5 -3.88665101019026 2.480063 -1.5672 0.125368 0.062684 M6 -10.5879834879979 2.742863 -3.8602 0.000427 0.000213 M7 -2.76413230559056 2.771267 -0.9974 0.324867 0.162434 M8 -5.59498712522036 2.634212 -2.124 0.04024 0.02012 M9 -3.68967115962482 2.691172 -1.371 0.17841 0.089205 M10 2.44620732778347 2.518923 0.9711 0.337624 0.168812 M11 0.210606343946619 2.209132 0.0953 0.92455 0.462275 t -0.0384352271612887 0.055337 -0.6946 0.491555 0.245778 Multiple Linear Regression - Regression Statistics Multiple R 0.942325551080884 R-squared 0.88797744421989 Adjusted R-squared 0.837862090318263 F-TEST (value) 17.7186705288546 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 4.54636328584002e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.89784985699467 Sum Squared Residuals 319.106284159994 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 117.33 120.49323319607 -3.16323319606985 2 119.04 120.355063010325 -1.31506301032531 3 123.68 123.415034400279 0.264965599720899 4 125.9 125.280905808770 0.619094191229748 5 124.54 124.173621155509 0.366378844490938 6 119.39 118.561042846312 0.82895715368763 7 118.8 119.921986266532 -1.12198626653208 8 114.81 114.266377329141 0.543622670859355 9 117.9 114.919807405540 2.9801925944598 10 120.53 121.106323528458 -0.576323528458282 11 125.15 124.463367371774 0.68663262822615 12 126.49 126.041103203087 0.448896796913206 13 131.85 131.459572977420 0.390427022579776 14 127.4 130.487743754714 -3.08774375471440 15 131.08 130.982997301601 0.0970026983993924 16 122.37 128.726626866768 -6.35662686676795 17 124.34 123.440954474686 0.899045525314365 18 119.61 117.770971824046 1.83902817595413 19 119.97 122.047350287949 -2.07735028794931 20 116.46 116.576197747376 -0.116197747376233 21 117.03 116.909207620593 0.120792379407362 22 120.96 121.453085568527 -0.493085568527386 23 124.71 124.230173749574 0.479826250425689 24 127.08 127.323415941597 -0.243415941597359 25 131.91 130.304584601478 1.60541539852207 26 137.69 132.409225568751 5.2807744312486 27 142.46 139.414436928022 3.04556307197832 28 144.32 139.231222320246 5.08877767975448 29 138.06 138.549443583948 -0.489443583947765 30 124.45 129.021343900775 -4.57134390077473 31 126.71 121.734403875404 4.97559612459558 32 121.83 121.467613261448 0.362386738552028 33 122.51 122.304413686542 0.205586313458251 34 125.48 124.445960441232 1.03403955876774 35 127.77 131.024069579365 -3.25406957936475 36 128.03 129.709992832872 -1.67999283287222 37 132.84 132.011949773232 0.828050226768081 38 133.41 135.159262801068 -1.74926280106808 39 139.99 136.060344819205 3.92965518079453 40 138.53 137.609261860232 0.920738139767675 41 136.12 137.40018428656 -1.28018428655992 42 124.75 125.243245461533 -0.493245461532842 43 122.88 125.510073376508 -2.63007337650847 44 121.46 121.086820958911 0.373179041088917 45 118.4 121.706571287325 -3.30657128732542 46 122.45 122.414630461782 0.0353695382179322 47 128.94 126.852389299287 2.08761070071291 48 133.25 131.775488022444 1.47451197755637 49 137.94 137.6006594518 0.339340548199918 50 140.04 139.168704865141 0.871295134859192 51 130.74 138.077186550893 -7.33718655089314 52 131.55 131.821983143984 -0.271983143983962 53 129.47 128.965796499298 0.50420350070238 54 125.45 123.053395967334 2.39660403266581 55 127.87 127.016186193606 0.85381380639428 56 124.68 125.842990703124 -1.16299070312407 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.458946940826829 0.917893881653657 0.541053059173171 22 0.334110369004507 0.668220738009013 0.665889630995493 23 0.274381933430208 0.548763866860417 0.725618066569792 24 0.195004848024754 0.390009696049508 0.804995151975246 25 0.333029250758380 0.666058501516761 0.66697074924162 26 0.529275254621496 0.941449490757007 0.470724745378504 27 0.404970472409353 0.809940944818705 0.595029527590647 28 0.370499973091057 0.740999946182113 0.629500026908943 29 0.366240776896876 0.732481553793753 0.633759223103124 30 0.456731931591266 0.913463863182532 0.543268068408734 31 0.683557924465705 0.63288415106859 0.316442075534295 32 0.640357167746587 0.719285664506826 0.359642832253413 33 0.670134267767784 0.659731464464432 0.329865732232216 34 0.532865311711831 0.934269376576338 0.467134688288169 35 0.612026678695517 0.775946642608966 0.387973321304483 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/10ggv11258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/10ggv11258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/17yid1258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/17yid1258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/2frl71258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/2frl71258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/38mn71258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/38mn71258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/4rayv1258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/4rayv1258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/5c4gx1258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/5c4gx1258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/6fhyz1258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/6fhyz1258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/7zgs91258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/7zgs91258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/87tzm1258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/87tzm1258763723.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/9ifog1258763723.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258763780kpaniovcu4bg4cl/9ifog1258763723.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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