multiple regression with linear trend

*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 11:38:36 -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/t1258742613hnrmv8a7ua2u8zk.htm/, Retrieved Fri, 20 Nov 2009 19:43:45 +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/t1258742613hnrmv8a7ua2u8zk.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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462 1919 455 1911 461 1870 461 2263 463 1802 462 1863 456 1989 455 2197 456 2409 472 2502 472 2593 471 2598 465 2053 459 2213 465 2238 468 2359 467 2151 463 2474 460 3079 462 2312 461 2565 476 1972 476 2484 471 2202 453 2151 443 1976 442 2012 444 2114 438 1772 427 1957 424 2070 416 1990 406 2182 431 2008 434 1916 418 2397 412 2114 404 1778 409 1641 412 2186 406 1773 398 1785 397 2217 385 2153 390 1895 413 2475 413 1793 401 2308 397 2051 397 1898 409 2142 419 1874 424 1560 428 1808 430 1575 424 1525 433 1997 456 1753 459 1623 446 2251 441 1890

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 wkl[t] = + 477.993760890492 + 0.000337047508224897bvg[t] -8.15082618449848M1[t] -20.0515922400293M2[t] -13.4216469414095M3[t] -8.84333732104988M4[t] -8.88767330186228M5[t] -11.9050494733973M6[t] -13.1368512782844M7[t] -17.0475856182171M8[t] -15.2677929888212M9[t] + 6.19349772806338M10[t] + 8.45229429338714M11[t] -1.03850630532863t + 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) 477.993760890492 31.666012 15.0949 0 0 bvg 0.000337047508224897 0.011353 0.0297 0.976442 0.488221 M1 -8.15082618449848 12.732764 -0.6401 0.525186 0.262593 M2 -20.0515922400293 13.754834 -1.4578 0.15155 0.075775 M3 -13.4216469414095 13.595873 -0.9872 0.328607 0.164303 M4 -8.84333732104988 12.950613 -0.6829 0.498053 0.249027 M5 -8.88767330186228 14.304897 -0.6213 0.537402 0.268701 M6 -11.9050494733973 13.475594 -0.8835 0.381489 0.190744 M7 -13.1368512782844 12.801573 -1.0262 0.310055 0.155027 M8 -17.0475856182171 13.184982 -1.293 0.202346 0.101173 M9 -15.2677929888212 12.714327 -1.2008 0.235833 0.117916 M10 6.19349772806338 12.827693 0.4828 0.631463 0.315732 M11 8.45229429338714 12.956477 0.6524 0.517347 0.258673 t -1.03850630532863 0.172032 -6.0367 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.741731690526168 R-squared 0.550165900730807 Adjusted R-squared 0.425743703060604 F-TEST (value) 4.42176646155290 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 7.76836363377553e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.8705683838929 Sum Squared Residuals 18557.4559312513 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 462 469.451222568948 -7.45122256894819 2 455 456.509253828024 -1.50925382802365 3 461 462.086873873477 -1.08687387347749 4 461 465.759136859241 -4.75913685924094 5 463 464.520915671808 -1.52091567180823 6 462 460.485593092946 1.51440690705374 7 456 458.257752968767 -2.25775296876687 8 455 453.378618205216 1.62138179478368 9 456 454.191358601027 1.8086413989728 10 472 474.645488430848 -2.64548843084813 11 472 475.896450014092 -3.89645001409176 12 471 466.407334652917 4.59266534708293 13 465 457.034311271107 7.9656887288926 14 459 444.148966511564 14.8510334884360 15 465 449.748831692561 15.2511683074393 16 468 453.329417756087 14.6705822439131 17 467 452.176469588235 14.8235304117649 18 463 448.229453456528 14.7705465434719 19 460 446.163059088788 13.8369409112116 20 462 440.955303004719 21.0446969952814 21 461 441.781862348367 19.2181376516333 22 476 462.004777587545 13.9952224124546 23 476 463.397636171752 12.6023638282484 24 471 453.811788175716 17.1882118242836 25 453 444.60526626297 8.39473373703014 26 443 431.607010588171 11.3929894118289 27 442 437.210583291758 4.78941670824174 28 444 440.784765452628 3.21523454737176 29 438 439.586652918674 -1.58665291867430 30 427 435.593124230832 -8.5931242308322 31 424 433.360902489046 -9.36090248904592 32 416 428.384698043127 -12.3846980431266 33 406 429.190697488773 -23.190697488773 34 431 449.554835633898 -18.5548356338979 35 434 450.744117523136 -16.7441175231363 36 418 441.415436775877 -23.4154367758767 37 412 432.130719841222 -20.1307198412219 38 404 419.078199517599 -15.0781995175990 39 409 424.623463002263 -15.6234630022632 40 412 428.346957209277 -16.3469572092768 41 406 427.124914302239 -21.1249143022389 42 398 423.073076395474 -25.0730763954739 43 397 420.948372808811 -23.9483728088114 44 385 415.977561123024 -30.9775611230237 45 390 416.631889189969 -26.6318891899689 46 413 437.250161156295 -24.2501611562953 47 413 438.240585015681 -25.2405850156811 48 401 428.923363883701 -27.9233638837011 49 397 419.64741018426 -22.6474101842602 50 397 406.656569554642 -9.65656955464235 51 409 412.33024813994 -3.33024813994032 52 419 415.779722722767 3.22027727723292 53 424 414.591047519043 9.40895248095656 54 428 410.618752824219 17.3812471757805 55 430 408.269912644587 21.7300873554126 56 424 403.303819623915 20.6961803760852 57 433 404.204192371864 28.7958076281358 58 456 424.544737191413 31.4552628085867 59 459 425.721211275339 33.2787887246608 60 446 416.442076511789 29.5579234882113 61 441 407.131069871492 33.8689301285076 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000140542605976049 0.000281085211952099 0.999859457394024 18 6.48173427370901e-06 1.29634685474180e-05 0.999993518265726 19 1.61445512218299e-06 3.22891024436599e-06 0.999998385544878 20 2.20281459475342e-07 4.40562918950683e-07 0.99999977971854 21 1.73875548978238e-08 3.47751097956477e-08 0.999999982612445 22 1.79774390630517e-09 3.59548781261034e-09 0.999999998202256 23 2.33694707305671e-10 4.67389414611341e-10 0.999999999766305 24 2.56239171090847e-10 5.12478342181695e-10 0.99999999974376 25 1.28150305251415e-06 2.56300610502830e-06 0.999998718496947 26 1.74606830111757e-05 3.49213660223515e-05 0.999982539316989 27 0.000209810673788679 0.000419621347577358 0.999790189326211 28 0.000480106583197835 0.00096021316639567 0.999519893416802 29 0.00168792518371605 0.0033758503674321 0.998312074816284 30 0.00788951582167657 0.0157790316433531 0.992110484178323 31 0.0118890966004893 0.0237781932009785 0.98811090339951 32 0.0427406864877315 0.085481372975463 0.957259313512268 33 0.107495216589976 0.214990433179951 0.892504783410024 34 0.110411558583316 0.220823117166631 0.889588441416684 35 0.126462765741363 0.252925531482725 0.873537234258637 36 0.280944330542163 0.561888661084326 0.719055669457837 37 0.459069373584128 0.918138747168255 0.540930626415872 38 0.607602022651323 0.784795954697353 0.392397977348677 39 0.726063589978342 0.547872820043315 0.273936410021658 40 0.929509464952723 0.140981070094554 0.0704905350472768 41 0.990095563521964 0.0198088729560722 0.00990443647803609 42 0.998149949780456 0.00370010043908806 0.00185005021954403 43 0.999359456908612 0.00128108618277606 0.000640543091388029 44 0.999160920919032 0.00167815816193583 0.000839079080967915 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.571428571428571 NOK 5% type I error level 19 0.678571428571429 NOK 10% type I error level 20 0.714285714285714 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/10noey1258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/10noey1258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/1fnum1258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/1fnum1258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/2kctz1258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/2kctz1258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/3y0ue1258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/3y0ue1258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/4taf91258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/4taf91258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/5he641258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/5he641258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/6q0201258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/6q0201258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/72jbv1258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/72jbv1258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/8bd201258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/8bd201258742311.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/97vbm1258742311.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258742613hnrmv8a7ua2u8zk/97vbm1258742311.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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