Workshop 7

*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 09:57:31 -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/t1258736328pngmffjkhq0fch8.htm/, Retrieved Fri, 20 Nov 2009 17:59:00 +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/t1258736328pngmffjkhq0fch8.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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109 102.86 108.6 102.55 108.8 102.28 108.5 102.26 108.3 102.57 108.2 103.08 108 102.76 107.9 102.51 108 102.87 109.3 103.14 109.6 103.12 109 103.16 108.7 102.48 108.3 102.57 108.4 102.88 107.8 102.63 107.8 102.38 107.6 101.69 107.7 101.96 107.6 102.19 107.6 101.87 108.6 101.6 108.6 101.63 108.2 101.22 107.5 101.21 107.1 101.49 107 101.64 106.9 101.66 106.6 101.77 106.3 101.82 106.1 101.78 105.9 101.28 106 101.29 107.2 101.37 107.2 101.12 106.4 101.51 106.1 102.24 105.9 102.94 106.1 103.09 105.9 103.46 105.8 103.64 105.7 104.39 105.6 104.15 105.3 105.21 105.5 105.8 106.5 105.91 106.5 105.39 106.1 105.46 105.9 104.72 105.8 103.14 106.2 102.63 106.5 102.32 106.6 101.93 106.7 100.62 106.6 100.6 106.5 99.63 106.8 98.9 107.8 98.32 107.9 99.22 107.4 98.81

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkl[t] = + 134.166581923486 -0.248752759833069Infl[t] -0.0497111330965682t + 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) 134.166581923486 5.760827 23.2895 0 0 Infl -0.248752759833069 0.056039 -4.4389 4.2e-05 2.1e-05 t -0.0497111330965682 0.005107 -9.733 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.802844482462342 R-squared 0.644559263020225 Adjusted R-squared 0.632087658213917 F-TEST (value) 51.6821429984872 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 1.57429624891847e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.676541126903235 Sum Squared Residuals 26.0893500943154 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 109 108.530161913960 0.469838086039677 2 108.6 108.557564136412 0.0424358635880709 3 108.8 108.575016248470 0.224983751529717 4 108.5 108.530280170570 -0.0302801705703741 5 108.3 108.403455681926 -0.103455681925560 6 108.2 108.226880641314 -0.0268806413141198 7 108 108.256770391364 -0.256770391364135 8 107.9 108.269247448226 -0.369247448225828 9 108 108.129985321589 -0.129985321589361 10 109.3 108.013110943338 1.28688905666213 11 109.6 107.968374865438 1.63162513456204 12 109 107.908713621948 1.09128637805193 13 108.7 108.028154365538 0.671845634462018 14 108.3 107.956055484056 0.343944515943554 15 108.4 107.829230995412 0.570769004588383 16 107.8 107.841708052273 -0.0417080522733249 17 107.8 107.854185109135 -0.054185109135024 18 107.6 107.976113380323 -0.376113380323276 19 107.7 107.859239002072 -0.159239002071771 20 107.6 107.752314734214 -0.152314734213605 21 107.6 107.782204484264 -0.182204484263617 22 108.6 107.799656596322 0.80034340367802 23 108.6 107.742482880430 0.85751711956958 24 108.2 107.794760378865 0.4052396211346 25 107.5 107.747536773367 -0.247536773367167 26 107.1 107.628174867517 -0.528174867517344 27 107 107.541150820446 -0.541150820445809 28 106.9 107.486464632153 -0.586464632152574 29 106.6 107.409390695474 -0.80939069547438 30 106.3 107.347241924386 -1.04724192438616 31 106.1 107.307480901683 -1.20748090168291 32 105.9 107.382146148503 -1.48214614850287 33 106 107.329947487808 -1.32994748780797 34 107.2 107.260336133925 -0.060336133924756 35 107.2 107.272813190786 -0.0728131907864551 36 106.4 107.126088481355 -0.726088481354987 37 106.1 106.894787833580 -0.794787833580292 38 105.9 106.670949768601 -0.770949768600563 39 106.1 106.583925721529 -0.483925721529045 40 105.9 106.442176067294 -0.542176067294232 41 105.8 106.347689437428 -0.547689437427718 42 105.7 106.111413734456 -0.411413734456342 43 105.6 106.121403263720 -0.521403263719718 44 105.3 105.8080142052 -0.508014205200097 45 105.5 105.611538943802 -0.111538943802013 46 106.5 105.534465007124 0.965534992876192 47 106.5 105.614105309140 0.885894690859565 48 106.1 105.546981482856 0.553018517144441 49 105.9 105.681347392035 0.218652607964551 50 105.8 106.024665619475 -0.224665619475139 51 106.2 106.101818393893 0.0981816061065688 52 106.5 106.129220616345 0.370779383654882 53 106.6 106.176523059583 0.423476940416551 54 106.7 106.452678041868 0.247321958131807 55 106.6 106.407941963968 0.192058036031702 56 106.5 106.599521007910 -0.0995210079098005 57 106.8 106.731399389491 0.0686006105086267 58 107.8 106.825964857098 0.974035142902012 59 107.9 106.552376240152 1.34762375984835 60 107.4 106.604653738587 0.795346261413363 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0121872065289217 0.0243744130578433 0.987812793471078 7 0.00209941317038817 0.00419882634077634 0.997900586829612 8 0.000321487143166575 0.00064297428633315 0.999678512856833 9 0.000206676336357391 0.000413352672714782 0.999793323663643 10 0.129524551554590 0.259049103109180 0.87047544844541 11 0.352075061546939 0.704150123093877 0.647924938453061 12 0.322211087263191 0.644422174526383 0.677788912736809 13 0.290625591054494 0.581251182108987 0.709374408945507 14 0.235860876312316 0.471721752624632 0.764139123687684 15 0.212980192081456 0.425960384162912 0.787019807918544 16 0.204978726600891 0.409957453201782 0.795021273399109 17 0.158462895036011 0.316925790072022 0.841537104963989 18 0.114420165521761 0.228840331043522 0.885579834478239 19 0.080471944490366 0.160943888980732 0.919528055509634 20 0.0581401347341221 0.116280269468244 0.941859865265878 21 0.0397625801345289 0.0795251602690578 0.960237419865471 22 0.203504937650574 0.407009875301148 0.796495062349426 23 0.508462465661986 0.983075068676028 0.491537534338014 24 0.710144032266906 0.579711935466187 0.289855967733094 25 0.745975586771375 0.50804882645725 0.254024413228625 26 0.789219878762541 0.421560242474918 0.210780121237459 27 0.833818274826544 0.332363450346911 0.166181725173456 28 0.867352239928096 0.265295520143807 0.132647760071904 29 0.893442612668104 0.213114774663792 0.106557387331896 30 0.912173277332901 0.175653445334197 0.0878267226670987 31 0.922416753513679 0.155166492972643 0.0775832464863214 32 0.93761790151317 0.124764196973659 0.0623820984868295 33 0.939532235650709 0.120935528698583 0.0604677643492913 34 0.960436910721551 0.0791261785568972 0.0395630892784486 35 0.986311838119373 0.027376323761255 0.0136881618806275 36 0.98253686377852 0.0349262724429603 0.0174631362214801 37 0.976472258381064 0.0470554832378715 0.0235277416189357 38 0.966483135124708 0.0670337297505838 0.0335168648752919 39 0.96119056426088 0.077618871478241 0.0388094357391205 40 0.951874668130214 0.0962506637395725 0.0481253318697863 41 0.942153228006062 0.115693543987877 0.0578467719939383 42 0.931339830574971 0.137320338850058 0.0686601694250288 43 0.92655397805007 0.146892043899859 0.0734460219499297 44 0.887610049123875 0.224779901752251 0.112389950876125 45 0.839868632470385 0.320262735059229 0.160131367529615 46 0.915520486009123 0.168959027981754 0.0844795139908772 47 0.972769804475054 0.0544603910498913 0.0272301955249456 48 0.962019032478246 0.075961935043507 0.0379809675217535 49 0.930268187188443 0.139463625623114 0.0697318128115571 50 0.88820095803954 0.223598083920919 0.111799041960459 51 0.824307325852518 0.351385348294964 0.175692674147482 52 0.762570049636362 0.474859900727275 0.237429950363638 53 0.695377964942828 0.609244070114344 0.304622035057172 54 0.62145688300952 0.75708623398096 0.37854311699048 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0612244897959184 NOK 5% type I error level 7 0.142857142857143 NOK 10% type I error level 14 0.285714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/10bev91258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/10bev91258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/18vg71258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/18vg71258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/2jl7e1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/2jl7e1258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/33blk1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/33blk1258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/4tcb11258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/4tcb11258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/519wv1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/519wv1258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/6cg3u1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/6cg3u1258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/7io7w1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/7io7w1258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/8y1yi1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/8y1yi1258736244.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/9a2tx1258736244.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736328pngmffjkhq0fch8/9a2tx1258736244.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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