WS71

*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: Sun, 22 Nov 2009 10:52:42 -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/22/t1258912427el3dot5ptn2809u.htm/, Retrieved Sun, 22 Nov 2009 18:53:59 +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/22/t1258912427el3dot5ptn2809u.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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124 104.89 118.63 105.15 121.86 105.24 119.97 105.57 125.03 105.62 130.09 106.17 126.65 106.27 121.7 106.41 119.24 106.94 122.63 107.16 116.66 107.32 114.12 107.32 113.11 107.35 112.61 107.55 113.4 107.87 115.18 108.37 121.01 108.38 119.44 107.92 116.68 108.03 117.07 108.14 117.41 108.3 119.58 108.64 120.92 108.66 117.09 109.04 116.77 109.03 119.39 109.03 122.49 109.54 124.08 109.75 118.29 109.83 112.94 109.65 113.79 109.82 114.43 109.95 118.7 110.12 120.36 110.15 118.27 110.21 118.34 109.99 117.82 110.14 117.65 110.14 118.18 110.81 121.02 110.97 124.78 110.99 131.16 109.73 130.14 109.81 131.75 110.02 134.73 110.18 135.35 110.21 140.32 110.25 136.35 110.36 131.6 110.51 128.9 110.6 133.89 110.95 138.25 111.18 146.23 111.19 144.76 111.69 149.3 111.7 156.8 111.83 159.08 111.77 165.12 111.73 163.14 112.01 153.43 111.86 151.01 112.04

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 AKB[t] = -324.26371948999 + 4.13127744296266AKW[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) -324.26371948999 80.04428 -4.0511 0.000151 7.5e-05 AKW 4.13127744296266 0.732799 5.6377 1e-06 0 Multiple Linear Regression - Regression Statistics Multiple R 0.591692965394288 R-squared 0.350100565297086 Adjusted R-squared 0.339085320641105 F-TEST (value) 31.7832763802456 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 5.11889629128959e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.9986129589431 Sum Squared Residuals 7137.19973421724 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 124 109.065971502364 14.9340284976363 2 118.63 110.140103637534 8.48989636246626 3 121.86 110.511918607400 11.3480813925997 4 119.97 111.875240163578 8.094759836422 5 125.03 112.081804035726 12.9481959642738 6 130.09 114.354006629356 15.7359933706444 7 126.65 114.767134373652 11.8828656263481 8 121.7 115.345513215667 6.35448678433336 9 119.24 117.535090260437 1.70490973956313 10 122.63 118.443971297889 4.18602870211135 11 116.66 119.104975688763 -2.44497568876266 12 114.12 119.104975688763 -4.98497568876265 13 113.11 119.228914012052 -6.11891401205154 14 112.61 120.055169500644 -7.44516950064408 15 113.4 121.377178282392 -7.97717828239216 16 115.18 123.442817003873 -8.26281700387348 17 121.01 123.484129778303 -2.47412977830308 18 119.44 121.583742154540 -2.14374215454029 19 116.68 122.038182673266 -5.35818267326617 20 117.07 122.492623191992 -5.42262319199207 21 117.41 123.153627582866 -5.74362758286608 22 119.58 124.558261913473 -4.9782619134734 23 120.92 124.640887462333 -3.72088746233263 24 117.09 126.210772890658 -9.12077289065848 25 116.77 126.169460116229 -9.39946011622884 26 119.39 126.169460116229 -6.77946011622883 27 122.49 128.276411612140 -5.78641161213982 28 124.08 129.143979875162 -5.06397987516195 29 118.29 129.474482070599 -11.1844820705989 30 112.94 128.730852130866 -15.7908521308657 31 113.79 129.433169296169 -15.6431692961693 32 114.43 129.970235363754 -15.5402353637545 33 118.7 130.672552529058 -11.9725525290581 34 120.36 130.796490852347 -10.4364908523470 35 118.27 131.044367498925 -12.7743674989247 36 118.34 130.135486461473 -11.7954864614730 37 117.82 130.755178077917 -12.9351780779174 38 117.65 130.755178077917 -13.1051780779174 39 118.18 133.523133964702 -15.3431339647024 40 121.02 134.184138355576 -13.1641383555764 41 124.78 134.266763904436 -9.48676390443562 42 131.16 129.061354326303 2.09864567369729 43 130.14 129.391856521740 0.748143478260274 44 131.75 130.259424784762 1.49057521523816 45 134.73 130.920429175636 3.80957082436407 46 135.35 131.044367498925 4.30563250107525 47 140.32 131.209618596643 9.11038140335672 48 136.35 131.664059115369 4.68594088463083 49 131.6 132.283750731814 -0.683750731813591 50 128.9 132.655565701680 -3.75556570168018 51 133.89 134.101512806717 -0.211512806717161 52 138.25 135.051706618599 3.19829338140142 53 146.23 135.093019393028 11.1369806069718 54 144.76 137.158658114510 7.6013418854905 55 149.3 137.199970888939 12.1000291110609 56 156.8 137.737036956524 19.0629630434757 57 159.08 137.489160309946 21.5908396900535 58 165.12 137.323909212228 27.796090787772 59 163.14 138.480666896258 24.6593331037424 60 153.43 137.860975279813 15.5690247201869 61 151.01 138.604605219546 12.4053947804535 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0323339785756892 0.0646679571513784 0.96766602142431 6 0.0236954584670988 0.0473909169341976 0.976304541532901 7 0.00889567976834302 0.0177913595366860 0.991104320231657 8 0.00981027051666282 0.0196205410333256 0.990189729483337 9 0.0120230257912594 0.0240460515825188 0.98797697420874 10 0.00578872533893445 0.0115774506778689 0.994211274661066 11 0.00593198106838157 0.0118639621367631 0.994068018931618 12 0.00675233231647629 0.0135046646329526 0.993247667683524 13 0.00638278107031331 0.0127655621406266 0.993617218929687 14 0.00462255500040877 0.00924511000081755 0.99537744499959 15 0.00233458770653452 0.00466917541306904 0.997665412293466 16 0.00105744744955318 0.00211489489910635 0.998942552550447 17 0.00121312197266043 0.00242624394532085 0.99878687802734 18 0.0008447059884739 0.0016894119769478 0.999155294011526 19 0.000465882656815422 0.000931765313630844 0.999534117343185 20 0.000271509540841615 0.00054301908168323 0.999728490459158 21 0.000167518844114229 0.000335037688228459 0.999832481155886 22 0.000143348536198879 0.000286697072397758 0.999856651463801 23 0.000184662154561057 0.000369324309122115 0.999815337845439 24 9.3777977312736e-05 0.000187555954625472 0.999906222022687 25 4.73326140050232e-05 9.46652280100464e-05 0.999952667385995 26 3.86491551491283e-05 7.72983102982566e-05 0.99996135084485 27 5.34763852142829e-05 0.000106952770428566 0.999946523614786 28 8.4209768578289e-05 0.000168419537156578 0.999915790231422 29 3.75747516576545e-05 7.5149503315309e-05 0.999962425248342 30 2.55594798317794e-05 5.11189596635588e-05 0.999974440520168 31 1.50378912988112e-05 3.00757825976223e-05 0.999984962108701 32 8.86278972851534e-06 1.77255794570307e-05 0.999991137210271 33 5.13918003001576e-06 1.02783600600315e-05 0.99999486081997 34 3.46075795142004e-06 6.92151590284007e-06 0.999996539242049 35 2.31419103828117e-06 4.62838207656234e-06 0.999997685808962 36 1.21055476832721e-06 2.42110953665443e-06 0.999998789445232 37 8.73210616359912e-07 1.74642123271982e-06 0.999999126789384 38 7.64552701127978e-07 1.52910540225596e-06 0.999999235447299 39 7.12726315410073e-06 1.42545263082015e-05 0.999992872736846 40 0.000215604261537430 0.000431208523074860 0.999784395738463 41 0.00823396316973124 0.0164679263394625 0.991766036830269 42 0.0298946977921442 0.0597893955842885 0.970105302207856 43 0.0500632400519775 0.100126480103955 0.949936759948023 44 0.0786038972623704 0.157207794524741 0.92139610273763 45 0.137369164906446 0.274738329812892 0.862630835093554 46 0.202162763381586 0.404325526763173 0.797837236618414 47 0.493014636884608 0.986029273769217 0.506985363115392 48 0.633995788574519 0.732008422850962 0.366004211425481 49 0.592721192648012 0.814557614703975 0.407278807351988 50 0.500917994940876 0.998164010118248 0.499082005059124 51 0.449876930701290 0.899753861402579 0.550123069298710 52 0.46746825821804 0.93493651643608 0.53253174178196 53 0.451867503628547 0.903735007257094 0.548132496371453 54 0.615350559575316 0.769298880849368 0.384649440424684 55 0.768881675796778 0.462236648406444 0.231118324203222 56 0.688000177228602 0.623999645542795 0.311999822771398 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.519230769230769 NOK 5% type I error level 36 0.692307692307692 NOK 10% type I error level 38 0.730769230769231 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/10095y1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/10095y1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/1s8751258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/1s8751258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/2enj21258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/2enj21258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/3lmyk1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/3lmyk1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/4t83l1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/4t83l1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/532eg1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/532eg1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/62q0u1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/62q0u1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/7k0de1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/7k0de1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/8ausy1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/8ausy1258912357.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/9e8yq1258912357.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258912427el3dot5ptn2809u/9e8yq1258912357.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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