Workshop 7 review

*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, 26 Nov 2010 09:37:04 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8.htm/, Retrieved Fri, 26 Nov 2010 10:35:23 +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/2010/Nov/26/t1290764120j23m7itvv6845w8.htm/}, year = {2010}, } @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 = {2010}, 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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43071 990633 45552 1047696 36329 835567 37703 867169 50519 1161937 36798 846354 37056 852288 44927 1033321 37635 865605 62924 1447252 8170 187910 27438 631074 27429 630867 33666 774318 27733 637859 33228 764244 25699 591077 303936 6990528 30169 693887 35117 807691 34870 802010 56676 1303548 7054 162242 29722 683606 41629 957467 41117 945691 39341 904843 39486 908178 48138 1107174 45633 1049559 41756 960388 47221 1086083 50530 1162190 68184 1568232 8771 201733 37898 871654 41888 963424 40439 930097 40898 940654 38401 883223 52073 1197679 41547 955581 38529 886167 51321 1180383 41519 954937 69116 1589668 12657 291111 34801 800423 37967 873241 39401 906223 33425 768775 36222 833106 48428 1113844 40891 940493 36432 837936 50669 1165387 39556 909788 68906 1584838

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation Verkoopcijfers[t] = -2.16080094974641e-12 + 0.0434782608695652`Totaleuitstootkm/u`[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) -2.16080094974641e-12 0 -1.9893 0.051556 0.025778 `Totaleuitstootkm/u` 0.0434782608695652 0 52790364194483208 0 0 Multiple Linear Regression - Regression Statistics Multiple R 1 R-squared 1 Adjusted R-squared 1 F-TEST (value) 2.78682255178618e+33 F-TEST (DF numerator) 1 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.28579786031869e-12 Sum Squared Residuals 1.56462090512838e-21 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 43071 43071 2.2236603765632e-11 2 45552 45552 3.04672082062726e-11 3 36329 36329 4.26423361600248e-14 4 37703 37703 -2.13385244240005e-13 5 50519 50519 4.86497751854067e-13 6 36798 36798 -3.00659056435213e-13 7 37056 37056 -5.38808164607463e-13 8 44927 44927 -3.45455282489792e-13 9 37635 37635 -5.55874485369407e-13 10 62924 62924 -3.58401426601991e-12 11 8170 8170 -3.35495697743738e-13 12 27438 27438 -4.43717423682596e-12 13 27429 27429 -3.25679135312986e-12 14 33666 33666 -1.43703305610115e-12 15 27733 27733 -2.7549572778756e-12 16 33228 33228 -8.52619085953262e-13 17 25699 25699 -3.37009792118209e-12 18 303936 303936 -1.8404020615302e-12 19 30169 30169 -1.76324649894296e-12 20 35117 35117 3.74277331771232e-13 21 34870 34870 -6.13054784378416e-13 22 56676 56676 -5.41200710054708e-13 23 7054 7054 -1.2848435245694e-12 24 29722 29722 -1.93645495688464e-12 25 41629 41629 -2.60871677557611e-13 26 41117 41117 -8.459932444068e-13 27 39341 39341 -8.48630114417741e-13 28 39486 39486 -3.75031859962197e-13 29 48138 48138 -3.41651401952082e-13 30 45633 45633 -2.00131334769363e-13 31 41756 41756 -3.33293578783459e-13 32 47221 47221 -1.47454984751153e-13 33 50530 50530 2.28686915795456e-13 34 68184 68184 -2.95114455345813e-12 35 8771 8771 -1.83540998967634e-12 36 37898 37898 5.6460051058624e-14 37 41888 41888 -2.80651559699103e-13 38 40439 40439 -9.28915592800195e-13 39 40898 40898 -5.5356421472793e-13 40 38401 38401 -1.08371119844505e-12 41 52073 52073 8.52319786951532e-13 42 41547 41547 -2.75238036430598e-13 43 38529 38529 -1.17934840379857e-12 44 51321 51321 1.05158952856091e-12 45 41519 41519 -5.87106749390906e-13 46 69116 69116 -1.56000611288703e-12 47 12657 12657 -5.55082962443998e-12 48 34801 34801 3.55637114880694e-14 49 37967 37967 -5.92602534017711e-13 50 39401 39401 -3.19086172637381e-13 51 33425 33425 -6.28760309700135e-13 52 36222 36222 -3.53904781871073e-13 53 48428 48428 1.21931957432289e-13 54 40891 40891 -8.7356001233629e-13 55 36432 36432 -4.35175628351609e-13 56 50669 50669 8.43353351856457e-13 57 39556 39556 -1.04753179377113e-12 58 68906 68906 -2.44596156545993e-12 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.643624780601613 0.712750438796774 0.356375219398387 6 0.0338992937718442 0.0677985875436885 0.966100706228156 7 1.7533612454129e-05 3.50672249082579e-05 0.999982466387546 8 0.00188425307151425 0.00376850614302851 0.998115746928486 9 1.09350040670322e-09 2.18700081340644e-09 0.9999999989065 10 0.108947273846499 0.217894547692998 0.891052726153501 11 0.0131926087632318 0.0263852175264636 0.986807391236768 12 0.640230714861753 0.719538570276493 0.359769285138247 13 0.0165623124796977 0.0331246249593954 0.983437687520302 14 0.000155703003192506 0.000311406006385012 0.999844296996808 15 1.5621927153799e-08 3.1243854307598e-08 0.999999984378073 16 0.0359712122410309 0.0719424244820618 0.96402878775897 17 4.07618659292687e-07 8.15237318585373e-07 0.99999959238134 18 0.999999999999925 1.49815667557007e-13 7.49078337785034e-14 19 0.999999998649147 2.70170686905424e-09 1.35085343452712e-09 20 0.99999976857496 4.62850081089316e-07 2.31425040544658e-07 21 0.99984027651535 0.000319446969299784 0.000159723484649892 22 0.999928824362518 0.000142351274965028 7.11756374825139e-05 23 0.99992275696576 0.000154486068481291 7.72430342406453e-05 24 0.999977896146152 4.42077076953096e-05 2.21038538476548e-05 25 0.998877622457304 0.00224475508539126 0.00112237754269563 26 0.99999999952721 9.45578953534809e-10 4.72789476767404e-10 27 0.0137353599851616 0.0274707199703231 0.986264640014838 28 0.982592096713231 0.034815806573537 0.0174079032867685 29 0.99993999991973 0.000120000160541683 6.00000802708417e-05 30 0.99999999710013 5.7997410264901e-09 2.89987051324505e-09 31 0.999999999947782 1.04436260243385e-10 5.22181301216923e-11 32 0.999999999832595 3.34809765406716e-10 1.67404882703358e-10 33 0.99999814267845 3.71464310169238e-06 1.85732155084619e-06 34 9.21861805467312e-06 1.84372361093462e-05 0.999990781381945 35 0.999998797052568 2.40589486466529e-06 1.20294743233265e-06 36 0.999999999999991 1.73178949193526e-14 8.65894745967632e-15 37 0.999997586314448 4.8273711033365e-06 2.41368555166825e-06 38 0.99999955177301 8.96453980864269e-07 4.48226990432135e-07 39 1 7.86957129418019e-16 3.93478564709009e-16 40 0.999983751518737 3.24969625257086e-05 1.62484812628543e-05 41 0.449298240951677 0.898596481903354 0.550701759048323 42 0.998421425621758 0.0031571487564842 0.0015785743782421 43 0.00350382549957736 0.00700765099915471 0.996496174500423 44 0.000194035530749108 0.000388071061498216 0.999805964469251 45 0.218986451163151 0.437972902326303 0.781013548836849 46 0.99995955144503 8.08971099405522e-05 4.04485549702761e-05 47 0.999996342351122 7.3152977555781e-06 3.65764887778905e-06 48 0.999949653564421 0.000100692871157185 5.03464355785925e-05 49 0.999990002671323 1.99946573549931e-05 9.99732867749656e-06 50 0.99256846036367 0.0148630792726588 0.00743153963632939 51 0.997031985110687 0.00593602977862527 0.00296801488931263 52 0.985569555062374 0.0288608898752513 0.0144304449376257 53 0.0656283600968298 0.13125672019366 0.93437163990317 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 35 0.714285714285714 NOK 5% type I error level 41 0.836734693877551 NOK 10% type I error level 43 0.877551020408163 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/10qlvv1290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/10qlvv1290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/1j2yj1290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/1j2yj1290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/2utf41290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/2utf41290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/3utf41290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/3utf41290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/4utf41290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/4utf41290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/5utf41290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/5utf41290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/6nlwp1290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/6nlwp1290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/7fcwa1290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/7fcwa1290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/8fcwa1290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/8fcwa1290764213.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/9fcwa1290764213.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290764120j23m7itvv6845w8/9fcwa1290764213.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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