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 06:52:24 -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/t1258725660t02bngldais95yd.htm/, Retrieved Fri, 20 Nov 2009 15:01:12 +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/t1258725660t02bngldais95yd.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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20,3 3016 20 2155 19,2 2172 21,8 2150 21,3 2533 21,5 2058 19,5 2160 19,5 2260 19,7 2498 18,7 2695 19,7 2799 20 2946 19,7 2930 19,2 2318 19,7 2540 22 2570 21,8 2669 22,8 2450 21 2842 25 3440 23,3 2678 25 2981 26,8 2260 25,3 2844 26,5 2546 27,8 2456 22 2295 22,3 2379 28 2479 25 2057 27,3 2280 25,8 2351 27,3 2276 23,5 2548 24,5 2311 18 2201 21,3 2725 21,8 2408 20,5 2139 22,3 1898 18,7 2537 22,3 2068 17,7 2063 19,7 2520 20,5 2434 18,5 2190 10 2794 14,2 2070 15,5 2615 16,5 2265 20,5 2139 15,7 2428 11,7 2137 7,5 1823 3,5 2063 4,5 1806 2,2 1758 5 2243 2,3 1993 6,1 1932 3,3 2465

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -0.914964807471034 + 0.00829634792425807X[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) -0.914964807471034 5.783698 -0.1582 0.874842 0.437421 X 0.00829634792425807 0.002399 3.458 0.001016 0.000508 Multiple Linear Regression - Regression Statistics Multiple R 0.410515307946277 R-squared 0.168522818058227 Adjusted R-squared 0.154429984465994 F-TEST (value) 11.9580506613730 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.00101605926118986 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.21530259080776 Sum Squared Residuals 2279.1691914228 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 20.3 24.1068205320913 -3.80682053209132 2 20 16.9636649693051 3.0363350306949 3 19.2 17.1047028840175 2.09529711598251 4 21.8 16.9221832296838 4.87781677031619 5 21.3 20.0996844846747 1.20031551532535 6 21.5 16.1589192206521 5.34108077934793 7 19.5 17.0051467089264 2.49485329107361 8 19.5 17.8347815013522 1.6652184986478 9 19.7 19.8093123073256 -0.109312307325620 10 18.7 21.4436928484045 -2.74369284840446 11 19.7 22.3065130325273 -2.6065130325273 12 20 23.5260761773932 -3.52607617739323 13 19.7 23.3933346106051 -3.69333461060510 14 19.2 18.3159696809592 0.884030319040832 15 19.7 20.1577589201445 -0.457758920144459 16 22 20.4066493578722 1.5933506421278 17 21.8 21.2279878023737 0.572012197626253 18 22.8 19.4110876069612 3.38891239303877 19 21 22.6632559932704 -1.66325599327039 20 25 27.6244720519767 -2.62447205197672 21 23.3 21.3026549336921 1.99734506630793 22 25 23.8164483547423 1.18355164525773 23 26.8 17.8347815013522 8.9652184986478 24 25.3 22.6798486891189 2.62015131088109 25 26.5 20.20753700769 6.29246299231 26 27.8 19.4608656945068 8.33913430549322 27 22 18.1251536787012 3.87484632129877 28 22.3 18.8220469043389 3.47795309566109 29 28 19.6516816967647 8.34831830323528 30 25 16.1506228727278 8.84937712727219 31 27.3 18.0007084598374 9.29929154016264 32 25.8 18.5897491624597 7.21025083754032 33 27.3 17.9675230681403 9.33247693185967 34 23.5 20.2241297035385 3.27587029646148 35 24.5 18.2578952454894 6.24210475451064 36 18 17.3452969738210 0.654703026179027 37 21.3 21.6925832861322 -0.392583286132199 38 21.8 19.0626409941424 2.73735900585761 39 20.5 16.8309234025170 3.66907659748303 40 22.3 14.8315035527708 7.46849644722922 41 18.7 20.1328698763717 -1.43286987637168 42 22.3 16.2418826998946 6.05811730010535 43 17.7 16.2004009602734 1.49959903972664 44 19.7 19.9918319616593 -0.291831961659297 45 20.5 19.2783460401731 1.22165395982690 46 18.5 17.2540371466541 1.24596285334587 47 10 22.265031292906 -12.265031292906 48 14.2 16.2584753957432 -2.05847539574317 49 15.5 20.7799850144638 -5.27998501446381 50 16.5 17.8762632409735 -1.37626324097349 51 20.5 16.8309234025170 3.66907659748303 52 15.7 19.2285679526276 -3.52856795262756 53 11.7 16.8143307066685 -5.11433070666846 54 7.5 14.2092774584514 -6.70927745845142 55 3.5 16.2004009602734 -12.7004009602734 56 4.5 14.0682395437390 -9.56823954373904 57 2.2 13.6700148433747 -11.4700148433747 58 5 17.6937435866398 -12.6937435866398 59 2.3 15.6196566055753 -13.3196566055753 60 6.1 15.1135793821956 -9.01357938219555 61 3.3 19.5355328258251 -16.2355328258251 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00880928334294045 0.0176185666858809 0.99119071665706 6 0.00185473775357007 0.00370947550714014 0.99814526224643 7 0.000473390976321406 0.000946781952642813 0.999526609023679 8 0.000105132441693263 0.000210264883386525 0.999894867558307 9 1.87607107876090e-05 3.75214215752181e-05 0.999981239289212 10 6.46613999866556e-06 1.29322799973311e-05 0.999993533860001 11 9.8153595338792e-07 1.96307190677584e-06 0.999999018464047 12 1.48054916711037e-07 2.96109833422074e-07 0.999999851945083 13 2.10102184699763e-08 4.20204369399526e-08 0.999999978989782 14 4.49372942748672e-09 8.98745885497344e-09 0.99999999550627 15 5.9334679804073e-10 1.18669359608146e-09 0.999999999406653 16 6.41691379529816e-10 1.28338275905963e-09 0.999999999358309 17 3.28254277083597e-10 6.56508554167193e-10 0.999999999671746 18 5.22539550251869e-10 1.04507910050374e-09 0.99999999947746 19 1.02416591235419e-10 2.04833182470839e-10 0.999999999897583 20 1.13341266330693e-09 2.26682532661386e-09 0.999999998866587 21 7.74224814876636e-10 1.54844962975327e-09 0.999999999225775 22 1.32239721454959e-09 2.64479442909918e-09 0.999999998677603 23 6.94816641754401e-08 1.38963328350880e-07 0.999999930518336 24 6.4676955866259e-08 1.29353911732518e-07 0.999999935323044 25 1.78302607052521e-07 3.56605214105043e-07 0.999999821697393 26 1.04242257791299e-06 2.08484515582598e-06 0.999998957577422 27 4.07606994916913e-07 8.15213989833827e-07 0.999999592393005 28 1.53677220653656e-07 3.07354441307312e-07 0.99999984632278 29 6.9182539761623e-07 1.38365079523246e-06 0.999999308174602 30 9.42257576969512e-07 1.88451515393902e-06 0.999999057742423 31 2.87569644578297e-06 5.75139289156593e-06 0.999997124303554 32 3.55577968963664e-06 7.11155937927328e-06 0.99999644422031 33 1.14065061133831e-05 2.28130122267662e-05 0.999988593493887 34 6.57935294114009e-06 1.31587058822802e-05 0.999993420647059 35 7.30904203868173e-06 1.46180840773635e-05 0.999992690957961 36 8.16804947261175e-06 1.63360989452235e-05 0.999991831950527 37 3.9274016224499e-06 7.8548032448998e-06 0.999996072598378 38 2.71155341295024e-06 5.42310682590047e-06 0.999997288446587 39 2.67726621820012e-06 5.35453243640025e-06 0.999997322733782 40 8.62304707871745e-06 1.72460941574349e-05 0.999991376952921 41 6.8202773792931e-06 1.36405547585862e-05 0.99999317972262 42 2.63216648596377e-05 5.26433297192754e-05 0.99997367833514 43 6.57405180868515e-05 0.000131481036173703 0.999934259481913 44 6.24436876123391e-05 0.000124887375224678 0.999937556312388 45 0.000106497286306838 0.000212994572613676 0.999893502713693 46 0.000347650777915085 0.00069530155583017 0.999652349222085 47 0.0048987172630841 0.0097974345261682 0.995101282736916 48 0.0100933692293441 0.0201867384586882 0.989906630770656 49 0.00855415257341547 0.0171083051468309 0.991445847426585 50 0.0138323517407480 0.0276647034814959 0.986167648259252 51 0.224153928676077 0.448307857352153 0.775846071323923 52 0.606989866576637 0.786020266846725 0.393010133423363 53 0.950920297524096 0.0981594049518084 0.0490797024759042 54 0.981561200434105 0.0368775991317899 0.0184387995658950 55 0.9670204475874 0.0659591048251993 0.0329795524125996 56 0.926026738624093 0.147946522751815 0.0739732613759075 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 42 0.807692307692308 NOK 5% type I error level 47 0.903846153846154 NOK 10% type I error level 49 0.942307692307692 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/10xj071258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/10xj071258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/1udwe1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/1udwe1258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/27r351258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/27r351258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/3sgtg1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/3sgtg1258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/4pbnf1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/4pbnf1258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/5g7gv1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/5g7gv1258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/681vm1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/681vm1258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/7l4j91258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/7l4j91258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/8vlwr1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/8vlwr1258725140.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/9qa8y1258725140.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258725660t02bngldais95yd/9qa8y1258725140.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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