Model 1

*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 17:04:58 -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/21/t125876196325to6khiijcmh7w.htm/, Retrieved Sat, 21 Nov 2009 01:06:14 +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/21/t125876196325to6khiijcmh7w.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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104.08 99.2 103.86 93.6 107.47 104.2 111.1 95.3 117.33 102.7 119.04 103.1 123.68 100 125.9 107.2 124.54 107 119.39 119 118.8 110.4 114.81 101.7 117.9 102.4 120.53 98.8 125.15 105.6 126.49 104.4 131.85 106.3 127.4 107.2 131.08 108.5 122.37 106.9 124.34 114.2 119.61 125.9 119.97 110.6 116.46 110.5 117.03 106.7 120.96 104.7 124.71 107.4 127.08 109.8 131.91 103.4 137.69 114.8 142.46 114.3 144.32 109.6 138.06 118.3 124.45 127.3 126.71 112.3 121.83 114.9 122.51 108.2 125.48 105.4 127.77 122.1 128.03 113.5 132.84 110 133.41 125.3 139.99 114.3 138.53 115.6 136.12 127.1 124.75 123 122.88 122.2 121.46 126.4 118.4 112.7 122.45 105.8 128.94 120.9 133.25 116.3 137.94 115.7 140.04 127.9 130.74 108.3 131.55 121.1 129.47 128.6 125.45 123.1 127.87 127.7 124.68 126.6

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 75.6032622959898 + 0.445177295463275X[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) 75.6032622959898 12.450719 6.0722 0 0 X 0.445177295463275 0.110547 4.027 0.000166 8.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.467448271714007 R-squared 0.218507886728412 Adjusted R-squared 0.205033884775453 F-TEST (value) 16.2169997816005 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.000166153070633612 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.71995781382273 Sum Squared Residuals 3456.66942153775 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.08 119.764850005946 -15.6848500059461 2 103.86 117.271857151352 -13.4118571513522 3 107.47 121.990736483263 -14.5207364832629 4 111.1 118.028658553640 -6.92865855363978 5 117.33 121.322970540068 -3.99297054006802 6 119.04 121.501041458253 -2.46104145825331 7 123.68 120.120991842317 3.55900815768284 8 125.9 123.326268369653 2.57373163034725 9 124.54 123.23723291056 1.30276708943991 10 119.39 128.579360456119 -9.1893604561194 11 118.8 124.750835715135 -5.95083571513524 12 114.81 120.877793244605 -6.06779324460474 13 117.9 121.189417351429 -3.28941735142903 14 120.53 119.586779087761 0.943220912238762 15 125.15 122.613984696912 2.5360153030885 16 126.49 122.079771942356 4.41022805764441 17 131.85 122.925608803736 8.9243911962642 18 127.4 123.326268369653 4.07373163034725 19 131.08 123.904998853755 7.175001146245 20 122.37 123.192715181014 -0.822715181013765 21 124.34 126.442509437896 -2.10250943789567 22 119.61 131.651083794816 -12.041083794816 23 119.97 124.839871174228 -4.86987117422788 24 116.46 124.795353444682 -8.33535344468156 25 117.03 123.103679721921 -6.07367972192111 26 120.96 122.213325130995 -1.25332513099457 27 124.71 123.415303828745 1.29469617125459 28 127.08 124.483729337857 2.59627066214274 29 131.91 121.634594646892 10.2754053531077 30 137.69 126.709615815174 10.9803841848264 31 142.46 126.487027167442 15.972972832558 32 144.32 124.394693878765 19.9253061212354 33 138.06 128.267736349295 9.7922636507049 34 124.45 132.274332008465 -7.82433200846457 35 126.71 125.596672576515 1.11332742348454 36 121.83 126.75413354472 -4.92413354471997 37 122.51 123.771445665116 -1.26144566511602 38 125.48 122.524949237819 2.95505076218115 39 127.77 129.959410072056 -2.18941007205555 40 128.03 126.130885331071 1.89911466892862 41 132.84 124.57276479695 8.26723520305008 42 133.41 131.383977417538 2.02602258246197 43 139.99 126.487027167442 13.502972832558 44 138.53 127.065757651544 11.4642423484557 45 136.12 132.185296549372 3.93470345062809 46 124.75 130.360069637972 -5.61006963797249 47 122.88 130.003927801602 -7.12392780160188 48 121.46 131.873672442548 -10.4136724425476 49 118.4 125.774743494701 -7.37474349470076 50 122.45 122.703020156004 -0.25302015600416 51 128.94 129.425197317500 -0.485197317499621 52 133.25 127.377381758369 5.87261824163145 53 137.94 127.110275381091 10.8297246189094 54 140.04 132.541438385743 7.49856161425745 55 130.74 123.815963394662 6.92403660533766 56 131.55 129.514232776592 2.03576722340774 57 129.47 132.853062492567 -3.38306249256683 58 125.45 130.404587367519 -4.95458736751882 59 127.87 132.45240292665 -4.58240292664988 60 124.68 131.962707901640 -7.28270790164027 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.464074866588081 0.928149733176162 0.535925133411919 6 0.468068894637843 0.936137789275685 0.531931105362157 7 0.682704380847213 0.634591238305574 0.317295619152787 8 0.621670684760847 0.756658630478306 0.378329315239153 9 0.515705522080261 0.968588955839479 0.484294477919739 10 0.630770459484588 0.738459081030823 0.369229540515412 11 0.548452827016879 0.903094345966242 0.451547172983121 12 0.482067906371633 0.964135812743267 0.517932093628367 13 0.419127989484703 0.838255978969406 0.580872010515297 14 0.417372805040757 0.834745610081514 0.582627194959243 15 0.408035766449274 0.816071532898549 0.591964233550726 16 0.426859336100202 0.853718672200403 0.573140663899798 17 0.53623595541415 0.9275280891717 0.46376404458585 18 0.497932718845256 0.995865437690513 0.502067281154744 19 0.499699850456224 0.999399700912447 0.500300149543776 20 0.42856232177157 0.85712464354314 0.57143767822843 21 0.364396926429215 0.728793852858431 0.635603073570784 22 0.481772213274743 0.963544426549486 0.518227786725257 23 0.441439230610916 0.882878461221831 0.558560769389084 24 0.471023635481931 0.942047270963862 0.528976364518069 25 0.487314927989146 0.974629855978292 0.512685072010854 26 0.463535069625634 0.927070139251267 0.536464930374366 27 0.426825683341128 0.853651366682256 0.573174316658872 28 0.388108197218275 0.77621639443655 0.611891802781725 29 0.448969290205323 0.897938580410646 0.551030709794677 30 0.539298924438651 0.921402151122698 0.460701075561349 31 0.74840492672482 0.50319014655036 0.25159507327518 32 0.939632270777033 0.120735458445935 0.0603677292229675 33 0.94890637165001 0.102187256699979 0.0510936283499896 34 0.950427119597304 0.0991457608053923 0.0495728804026961 35 0.927455409753482 0.145089180493036 0.0725445902465181 36 0.921548216757754 0.156903566484492 0.0784517832422459 37 0.906459958019643 0.187080083960715 0.0935400419803574 38 0.87917969299735 0.2416406140053 0.12082030700265 39 0.837781370512129 0.324437258975741 0.162218629487871 40 0.785403192603212 0.429193614793577 0.214596807396788 41 0.749903784745344 0.500192430509312 0.250096215254656 42 0.689602656091884 0.620794687816231 0.310397343908116 43 0.784771636899865 0.430456726200271 0.215228363100135 44 0.84599277880156 0.308014442396879 0.154007221198439 45 0.834535539727775 0.330928920544451 0.165464460272225 46 0.795379276448852 0.409241447102296 0.204620723551148 47 0.776484344168368 0.447031311663263 0.223515655831632 48 0.815976145226342 0.368047709547316 0.184023854773658 49 0.881272454009385 0.237455091981231 0.118727545990615 50 0.906423478126046 0.187153043747909 0.0935765218739545 51 0.850043853887375 0.299912292225249 0.149956146112625 52 0.766616264371847 0.466767471256307 0.233383735628153 53 0.776501785254089 0.446996429491823 0.223498214745911 54 0.97467485963321 0.0506502807335799 0.0253251403667900 55 0.922347561632014 0.155304876735971 0.0776524383679855 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 0 0 OK 10% type I error level 2 0.0392156862745098 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/10dga81258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/10dga81258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/1gxwu1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/1gxwu1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/20e2r1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/20e2r1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/3kn7d1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/3kn7d1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/4dvgv1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/4dvgv1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/5fpyf1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/5fpyf1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/6hf3b1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/6hf3b1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/7qbat1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/7qbat1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/8vwuo1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/8vwuo1258761893.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/9yr0l1258761893.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t125876196325to6khiijcmh7w/9yr0l1258761893.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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