Regressiemodel - include monthly dummies

*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: Thu, 19 Nov 2009 10:01:55 -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/19/t1258650302jheuf7ly064jxul.htm/, Retrieved Thu, 19 Nov 2009 18:05:14 +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/19/t1258650302jheuf7ly064jxul.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:

Uitleg in Word document

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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96.96 89.1 93.11 83.3 95.62 97.7 98.30 100.9 96.38 108.3 100.82 113.2 99.06 105 94.03 104 102.07 109.8 99.31 98.6 98.64 93.5 101.82 98.2 99.14 88 97.63 85.3 100.06 96.8 101.32 98.8 101.49 110.3 105.43 111.6 105.09 111.2 99.48 106.9 108.53 117.6 104.34 97 106.10 97.3 107.35 98.4 103.00 87.6 104.50 87.4 105.17 94.7 104.84 101.5 106.18 110.4 108.86 108.4 107.77 109.7 102.74 105.2 112.63 111.1 106.26 96.2 108.86 97.3 111.38 98.9 106.85 91.7 107.86 90.9 107.94 98.8 111.38 111.5 111.29 119 113.72 115.3 111.88 116.3 109.87 113.6 113.72 115.1 111.71 109.7 114.81 97.6 112.05 100.8 111.54 94 110.87 87.2 110.87 102.9 115.48 111.3 111.63 106.6 116.24 108.9 113.56 108.3 106.01 100.5 110.45 104 107.77 89.9 108.61 86.8 108.19 91.2

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation BESTC[t] = + 53.3118683218126 + 0.562524427468587INDUSTR[t] -0.486068748183151M1[t] + 0.643760885364526M2[t] -4.60851661067863M3[t] -6.00042832052068M4[t] -10.3130778166284M5[t] -7.00809149601084M6[t] -7.7738077861042M7[t] -10.5359586105817M8[t] -6.56459247310959M9[t] -2.71876905342549M10[t] + 0.933573282405774M11[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) 53.3118683218126 16.87502 3.1592 0.002765 0.001383 INDUSTR 0.562524427468587 0.171301 3.2838 0.001938 0.000969 M1 -0.486068748183151 3.639218 -0.1336 0.894318 0.447159 M2 0.643760885364526 3.869804 0.1664 0.868591 0.434296 M3 -4.60851661067863 3.412023 -1.3507 0.183269 0.091634 M4 -6.00042832052068 3.63209 -1.6521 0.105189 0.052594 M5 -10.3130778166284 4.112553 -2.5077 0.015664 0.007832 M6 -7.00809149601084 4.166935 -1.6818 0.099235 0.049617 M7 -7.7738077861042 4.035715 -1.9263 0.060133 0.030066 M8 -10.5359586105817 3.710585 -2.8394 0.006657 0.003328 M9 -6.56459247310959 4.170877 -1.5739 0.122216 0.061108 M10 -2.71876905342549 3.41265 -0.7967 0.429647 0.214823 M11 0.933573282405774 3.44854 0.2707 0.787795 0.393898 Multiple Linear Regression - Regression Statistics Multiple R 0.56286069610102 R-squared 0.316812163215325 Adjusted R-squared 0.142381226163919 F-TEST (value) 1.81626131562865 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0728147785907012 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.39173682080917 Sum Squared Residuals 1366.32881940886 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.96 102.946726061081 -5.9867260610811 2 93.11 100.813914015311 -7.70391401531056 3 95.62 103.661988274815 -8.04198827481507 4 98.3 104.070154732873 -5.77015473287252 5 96.38 103.920186000032 -7.5401860000323 6 100.82 109.981542015246 -9.16154201524597 7 99.06 104.603125419910 -5.5431254199102 8 94.03 101.278450167964 -7.24845016796409 9 102.07 108.512457984754 -6.44245798475403 10 99.31 106.05800781679 -6.74800781678995 11 98.64 106.841475572531 -8.20147557253142 12 101.82 108.551767099228 -6.73176709922801 13 99.14 102.327949190865 -3.18794919086526 14 97.63 101.938962870248 -4.30896287024775 15 100.06 103.155716290093 -3.09571629009335 16 101.32 102.888853435188 -1.56885343518848 17 101.49 105.045234854969 -3.55523485496948 18 105.43 109.081502931296 -3.65150293129622 19 105.09 108.090776870215 -3.00077687021545 20 99.48 102.909771007623 -3.42977100762299 21 108.53 112.900148519009 -4.37014851900901 22 104.34 105.157968732840 -0.817968732840203 23 106.1 108.979068396912 -2.87906839691205 24 107.35 108.664271984722 -1.31427198472173 25 103 102.102939419878 0.897060580122177 26 104.5 103.120264167932 1.37973583206821 27 105.17 101.974414992409 3.19558500759068 28 104.84 104.407669389354 0.432330610646340 29 106.18 105.101487297716 1.07851270228367 30 108.86 107.281424763397 1.57857523660325 31 107.77 107.246990229013 0.523009770987429 32 102.74 101.953479480926 0.786520519073607 33 112.63 109.243739740463 3.38626025953680 34 106.26 104.707949190865 1.55205080913467 35 108.86 108.979068396912 -0.119068396912045 36 111.38 108.945534198456 2.43446580154398 37 106.85 104.409289572499 2.44071042750096 38 107.86 105.089099664072 2.77090033592816 39 107.94 104.280765145031 3.65923485496947 40 111.38 110.032913664040 1.34708633596046 41 111.29 109.939197373946 1.35080262605382 42 113.72 111.16284331293 2.55715668707 43 111.88 110.959651450305 0.920348549694754 44 109.87 106.678684671663 3.19131532833748 45 113.72 111.493837450338 2.22616254966246 46 111.71 112.302028961691 -0.592028961691276 47 114.81 109.147825725153 5.66217427484738 48 112.05 110.014330610646 2.03566938935367 49 111.54 105.703095755677 5.83690424432322 50 110.87 103.007759282438 7.86224071756194 51 110.87 106.587115297652 4.28288470234826 52 115.48 109.920408778546 5.55959122145418 53 111.63 102.963894473336 8.6661055266643 54 116.24 107.562686977131 8.67731302286895 55 113.56 106.459456030557 7.10054396944346 56 106.01 99.309614671824 6.70038532817598 57 110.45 105.249816305436 5.20018369456378 58 107.77 101.164045297813 6.60595470218676 59 108.61 103.072561908492 5.53743809150813 60 108.19 104.614096106948 3.57590389305211 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.422166437341422 0.844332874682844 0.577833562658578 17 0.464081372515983 0.928162745031967 0.535918627484017 18 0.520487944648759 0.959024110702481 0.479512055351241 19 0.497287614407888 0.994575228815777 0.502712385592112 20 0.50483742540742 0.99032514918516 0.49516257459258 21 0.456983759621668 0.913967519243337 0.543016240378332 22 0.564024419797397 0.871951160405205 0.435975580202603 23 0.623025739743727 0.753948520512545 0.376974260256273 24 0.665120866866675 0.66975826626665 0.334879133133325 25 0.75281645751648 0.494367084967039 0.247183542483519 26 0.8310576934753 0.3378846130494 0.1689423065247 27 0.923974538082534 0.152050923834932 0.076025461917466 28 0.943278779721542 0.113442440556917 0.0567212202784585 29 0.96307550906629 0.0738489818674179 0.0369244909337089 30 0.98704577675493 0.0259084464901413 0.0129542232450706 31 0.989769046034356 0.020461907931288 0.010230953965644 32 0.994630555697168 0.0107388886056639 0.00536944430283193 33 0.994288942542887 0.0114221149142253 0.00571105745711265 34 0.99300200657904 0.0139959868419201 0.00699799342096005 35 0.994060604180665 0.0118787916386701 0.00593939581933504 36 0.990009530158103 0.0199809396837944 0.00999046984189722 37 0.990414236008613 0.0191715279827733 0.00958576399138664 38 0.989596054003116 0.0208078919937676 0.0104039459968838 39 0.982581575771846 0.0348368484563071 0.0174184242281536 40 0.981285834854485 0.0374283302910302 0.0187141651455151 41 0.971934850741305 0.0561302985173909 0.0280651492586955 42 0.976133438930852 0.0477331221382952 0.0238665610691476 43 0.98321812228769 0.0335637554246196 0.0167818777123098 44 0.941674482385823 0.116651035228353 0.0583255176141767 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 13 0.448275862068966 NOK 10% type I error level 15 0.517241379310345 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/1069gw1258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/1069gw1258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/1wny31258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/1wny31258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/25w2u1258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/25w2u1258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/3de8u1258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/3de8u1258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/4gvai1258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/4gvai1258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/5svmn1258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/5svmn1258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/6a3i81258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/6a3i81258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/7uv431258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/7uv431258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/8vi7w1258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/8vi7w1258650109.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/9ehl01258650109.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650302jheuf7ly064jxul/9ehl01258650109.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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