WS7 3

*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: Wed, 18 Nov 2009 13:27:09 -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/18/t12585760909hlt8inm0dvfetg.htm/, Retrieved Wed, 18 Nov 2009 21:28:22 +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/18/t12585760909hlt8inm0dvfetg.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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103.63 100.30 103.64 98.50 103.66 95.10 103.77 93.10 103.88 92.20 103.91 89.00 103.91 86.40 103.92 84.50 104.05 82.70 104.23 80.80 104.30 81.80 104.31 81.80 104.31 82.90 104.34 83.80 104.55 86.20 104.65 86.10 104.73 86.20 104.75 88.80 104.75 89.60 104.76 87.80 104.94 88.30 105.29 88.60 105.38 91.00 105.43 91.50 105.43 95.40 105.42 98.70 105.52 99.90 105.69 98.60 105.72 100.30 105.74 100.20 105.74 100.40 105.74 101.40 105.95 103.00 106.17 109.10 106.34 111.40 106.37 114.10 106.37 121.80 106.36 127.60 106.44 129.90 106.29 128.00 106.23 123.50 106.23 124.00 106.23 127.40 106.23 127.60 106.34 128.40 106.44 131.40 106.44 135.10 106.48 134.00 106.50 144.50 106.57 147.30 106.40 150.90 106.37 148.70 106.25 141.40 106.21 138.90 106.21 139.80 106.24 145.60 106.19 147.90 106.08 148.50 106.13 151.10 106.09 157.50

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] = + 105.003586206778 -0.0156423924424082X[t] + 0.0710663552202558t + 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) 105.003586206778 0.368333 285.078 0 0 X -0.0156423924424082 0.004946 -3.1625 0.002507 0.001254 t 0.0710663552202558 0.00677 10.4969 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.936807863449721 R-squared 0.877608973021232 Adjusted R-squared 0.873314551021977 F-TEST (value) 204.360208003190 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.344327799666353 Sum Squared Residuals 6.75801311651513 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.63 103.505720600025 0.124279399975263 2 103.64 103.604943261641 0.0350567383587078 3 103.66 103.729193751166 -0.0691937511657371 4 103.77 103.831544891271 -0.0615448912708096 5 103.88 103.916689399689 -0.0366893996892332 6 103.91 104.037811410725 -0.127811410725194 7 103.91 104.149547986296 -0.239547986295711 8 103.92 104.250334887157 -0.330334887156538 9 104.05 104.349557548773 -0.299557548773133 10 104.23 104.450344449634 -0.220344449633957 11 104.3 104.505768412412 -0.205768412411812 12 104.31 104.576834767632 -0.266834767632063 13 104.31 104.630694491166 -0.320694491165669 14 104.34 104.687682693188 -0.347682693187757 15 104.55 104.721207306546 -0.171207306546239 16 104.65 104.793837901011 -0.143837901010727 17 104.73 104.863340016987 -0.133340016986744 18 104.75 104.893736151857 -0.143736151856742 19 104.75 104.952288593123 -0.202288593123071 20 104.76 105.051511254740 -0.291511254739657 21 104.94 105.114756413739 -0.174756413738716 22 105.29 105.181130051226 0.108869948773760 23 105.38 105.214654664585 0.165345335415273 24 105.43 105.277899823584 0.152100176416232 25 105.43 105.287960848279 0.142039151721369 26 105.42 105.307407308439 0.112592691561055 27 105.52 105.359702792728 0.160297207271683 28 105.69 105.451104258124 0.238895741876298 29 105.72 105.495578546192 0.224421453808138 30 105.74 105.568209140656 0.171790859343637 31 105.74 105.636147017388 0.103852982611863 32 105.74 105.691570980166 0.0484290198340156 33 105.95 105.737609507478 0.212390492521621 34 106.17 105.7132572688 0.456742731200054 35 106.34 105.748346121403 0.591653878597339 36 106.37 105.777178017028 0.592821982971586 37 106.37 105.727797950442 0.642202049557874 38 106.36 105.708138429496 0.65186157050358 39 106.44 105.743227282099 0.696772717900862 40 106.29 105.84401418296 0.445985817040039 41 106.23 105.985471304171 0.244528695828944 42 106.23 106.04871646317 0.181283536829892 43 106.23 106.066598684086 0.163401315913825 44 106.23 106.134536560818 0.0954634391820503 45 106.34 106.193089002084 0.146910997915721 46 106.44 106.217228179977 0.222771820022684 47 106.44 106.230417683161 0.209582316839338 48 106.48 106.318690670068 0.161309329932440 49 106.5 106.225511904643 0.274488095357466 50 106.57 106.252779561024 0.317220438975947 51 106.4 106.267533303452 0.132466696548373 52 106.37 106.373012922045 -0.00301292204518214 53 106.25 106.558268742095 -0.308268742095022 54 106.21 106.668441078421 -0.458441078421305 55 106.21 106.725429280443 -0.515429280443393 56 106.24 106.705769759498 -0.46576975949768 57 106.19 106.740858612100 -0.550858612100394 58 106.08 106.802539531855 -0.722539531855205 59 106.13 106.832935666725 -0.702935666725202 60 106.09 106.803890710314 -0.713890710314037 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.00113988877592615 0.00227977755185230 0.998860111224074 7 0.000125957328532039 0.000251914657064078 0.999874042671468 8 5.44544174440981e-05 0.000108908834888196 0.999945545582556 9 5.87838651128865e-06 1.17567730225773e-05 0.999994121613489 10 5.86047109627254e-06 1.17209421925451e-05 0.999994139528904 11 1.51215955009008e-06 3.02431910018016e-06 0.99999848784045 12 9.18229848504944e-07 1.83645969700989e-06 0.999999081770152 13 8.36844773421364e-07 1.67368954684273e-06 0.999999163155227 14 4.17722398183503e-07 8.35444796367006e-07 0.999999582277602 15 2.14953081043253e-07 4.29906162086506e-07 0.999999785046919 16 9.78731653517093e-08 1.95746330703419e-07 0.999999902126835 17 3.813057217448e-08 7.626114434896e-08 0.999999961869428 18 1.87431158163651e-08 3.74862316327303e-08 0.999999981256884 19 3.66059616568236e-08 7.32119233136472e-08 0.999999963394038 20 1.89832247929252e-07 3.79664495858504e-07 0.999999810167752 21 2.68276435265952e-07 5.36552870531905e-07 0.999999731723565 22 2.82993414133958e-05 5.65986828267917e-05 0.999971700658587 23 0.000130171783090915 0.000260343566181829 0.99986982821691 24 0.000163922777206876 0.000327845554413752 0.999836077222793 25 0.000175134797625381 0.000350269595250763 0.999824865202375 26 0.000741105117930955 0.00148221023586191 0.999258894882069 27 0.0027772298573983 0.0055544597147966 0.997222770142602 28 0.00343114434258057 0.00686228868516114 0.99656885565742 29 0.00514686177592503 0.0102937235518501 0.994853138224075 30 0.00916790922786697 0.0183358184557339 0.990832090772133 31 0.0300763637118743 0.0601527274237487 0.969923636288126 32 0.221046112482050 0.442092224964099 0.77895388751795 33 0.346459931234167 0.692919862468334 0.653540068765833 34 0.362780865487373 0.725561730974747 0.637219134512627 35 0.3428976555403 0.6857953110806 0.6571023444597 36 0.285403442118429 0.570806884236858 0.714596557881571 37 0.241241013250152 0.482482026500304 0.758758986749848 38 0.285460240252972 0.570920480505944 0.714539759747028 39 0.266256236393942 0.532512472787884 0.733743763606058 40 0.518875218063544 0.962249563872912 0.481124781936456 41 0.775311097280885 0.44937780543823 0.224688902719115 42 0.916872687737844 0.166254624524313 0.0831273122621564 43 0.987974886779582 0.0240502264408362 0.0120251132204181 44 0.999740591548554 0.000518816902891025 0.000259408451445513 45 0.999971578883629 5.68422327425001e-05 2.84211163712500e-05 46 0.999963614403515 7.27711929706915e-05 3.63855964853457e-05 47 0.999964436955603 7.11260887945742e-05 3.55630443972871e-05 48 0.999895050862464 0.000209898275071002 0.000104949137535501 49 0.999656161911701 0.000687676176597417 0.000343838088298709 50 0.999957046110995 8.59077780098817e-05 4.29538890049409e-05 51 0.999811864512972 0.000376270974055294 0.000188135487027647 52 0.999274264630472 0.00145147073905679 0.000725735369528393 53 0.998121283222266 0.00375743355546726 0.00187871677773363 54 0.995469748138303 0.00906050372339396 0.00453025186169698 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 34 0.693877551020408 NOK 5% type I error level 37 0.755102040816326 NOK 10% type I error level 38 0.775510204081633 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/1019uw1258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/1019uw1258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/1t1l71258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/1t1l71258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/2ejka1258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/2ejka1258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/3npoa1258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/3npoa1258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/4vwq01258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/4vwq01258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/5dvc21258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/5dvc21258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/6obj61258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/6obj61258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/7x8rc1258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/7x8rc1258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/8k7tf1258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/8k7tf1258576024.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/9r92r1258576024.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585760909hlt8inm0dvfetg/9r92r1258576024.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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