Workshop 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: Fri, 20 Nov 2009 07:02:04 -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/t12587259863agj88yjb6uc4s2.htm/, Retrieved Fri, 20 Nov 2009 15:06:37 +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/t12587259863agj88yjb6uc4s2.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:

Multivariate

Dataseries X:

» Textbox « » Textfile « » CSV «

108.2 108.5 108.8 112.3 110.2 116.6 109.5 115.5 109.5 120.1 116 132.9 111.2 128.1 112.1 129.3 114 132.5 119.1 131 114.1 124.9 115.1 120.8 115.4 122 110.8 122.1 116 127.4 119.2 135.2 126.5 137.3 127.8 135 131.3 136 140.3 138.4 137.3 134.7 143 138.4 134.5 133.9 139.9 133.6 159.3 141.2 170.4 151.8 175 155.4 175.8 156.6 180.9 161.6 180.3 160.7 169.6 156 172.3 159.5 184.8 168.7 177.7 169.9 184.6 169.9 211.4 185.9 215.3 190.8 215.9 195.8 244.7 211.9 259.3 227.1 289 251.3 310.9 256.7 321 251.9 315.1 251.2 333.2 270.3 314.1 267.2 284.7 243 273.9 229.9 216 187.2 196.4 178.2 190.9 175.2 206.4 192.4 196.3 187 199.5 184 198.9 194.1 214.4 212.7 214.2 217.5 187.6 200.5 180.6 205.9 172.2 196.5

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] = -54.694704417963 + 1.38126912837142X[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) -54.694704417963 7.547577 -7.2467 0 0 X 1.38126912837142 0.043034 32.0971 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.972986203223003 R-squared 0.946702151662315 Adjusted R-squared 0.9457832232427 F-TEST (value) 1030.22404297680 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.7658230603976 Sum Squared Residuals 12645.7127777562 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 108.2 95.172996010336 13.0270039896641 2 108.8 100.421818698147 8.37818130185281 3 110.2 106.361275950144 3.83872404985563 4 109.5 104.841879908936 4.65812009106418 5 109.5 111.195717899444 -1.69571789944433 6 116 128.875962742598 -12.8759627425985 7 111.2 122.245870926416 -11.0458709264157 8 112.1 123.903393880461 -11.8033938804614 9 114 128.32345509125 -14.3234550912499 10 119.1 126.251551398693 -7.1515513986928 11 114.1 117.825809715627 -3.72580971562716 12 115.1 112.162606289304 2.93739371069566 13 115.4 113.82012924335 1.57987075664997 14 110.8 113.958256156187 -3.15825615618717 15 116 121.278982536556 -5.2789825365557 16 119.2 132.052881737853 -12.8528817378527 17 126.5 134.953546907433 -8.45354690743275 18 127.8 131.776627912178 -3.97662791217847 19 131.3 133.15789704055 -1.85789704054988 20 140.3 136.472942948641 3.82705705135871 21 137.3 131.362247173667 5.93775282633298 22 143 136.472942948641 6.5270570513587 23 134.5 130.25723187097 4.24276812903008 24 139.9 129.842851132458 10.0571488675415 25 159.3 140.340496508081 18.9595034919188 26 170.4 154.981949268818 15.4180507311817 27 175 159.954518130955 15.0454818690446 28 175.8 161.612041085001 14.1879589149989 29 180.9 168.518386726858 12.3816132731418 30 180.3 167.275244511324 13.0247554886761 31 169.6 160.783279607978 8.81672039202175 32 172.3 165.617721557278 6.6822784427218 33 184.8 178.325397538295 6.47460246170478 34 177.7 179.982920492341 -2.28292049234097 35 184.6 179.982920492341 4.61707950765903 36 211.4 202.083226546284 9.31677345371636 37 215.3 208.851445275304 6.44855472469642 38 215.9 215.757790917161 0.14220908283931 39 244.7 237.996223883941 6.70377611605949 40 259.3 258.991514635186 0.308485364813961 41 289 292.418227541774 -3.41822754177437 42 310.9 299.87708083498 11.0229191650200 43 321 293.246989018797 27.7530109812028 44 315.1 292.280100628937 22.8198993710628 45 333.2 318.662340980831 14.5376590191687 46 314.1 314.38040668288 -0.280406682879869 47 284.7 280.953693776292 3.74630622370839 48 273.9 262.859068194626 11.0409318053739 49 216 203.878876413166 12.1211235868335 50 196.4 191.447454257824 4.9525457421763 51 190.9 187.303646872709 3.59635312729055 52 206.4 211.061475880698 -4.66147588069786 53 196.3 203.602622587492 -7.30262258749218 54 199.5 199.458815202378 0.0411847976220543 55 198.9 213.409633398929 -14.5096333989292 56 214.4 239.101239186638 -24.7012391866376 57 214.2 245.731331002820 -31.5313310028204 58 187.6 222.249755820506 -34.6497558205063 59 180.6 229.708609113712 -49.108609113712 60 172.2 216.724679307021 -44.5246793070207 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 5.32633432902799e-05 0.000106526686580560 0.99994673665671 6 3.67566274439432e-05 7.35132548878864e-05 0.999963243372556 7 1.26819185214159e-05 2.53638370428317e-05 0.999987318081479 8 1.29560165374478e-06 2.59120330748957e-06 0.999998704398346 9 9.9033352498121e-08 1.98066704996242e-07 0.999999900966647 10 1.37164895890241e-06 2.74329791780481e-06 0.99999862835104 11 2.17683158923165e-07 4.35366317846331e-07 0.99999978231684 12 1.06374520001497e-07 2.12749040002995e-07 0.99999989362548 13 3.68644907866256e-08 7.37289815732512e-08 0.99999996313551 14 6.22716377146135e-09 1.24543275429227e-08 0.999999993772836 15 1.22441854958749e-09 2.44883709917498e-09 0.999999998775581 16 3.01303856864222e-10 6.02607713728445e-10 0.999999999698696 17 3.25135899330630e-09 6.50271798661261e-09 0.99999999674864 18 1.64477215476875e-08 3.2895443095375e-08 0.999999983552278 19 8.66746283068959e-08 1.73349256613792e-07 0.999999913325372 20 2.38028094233192e-06 4.76056188466385e-06 0.999997619719058 21 7.54636980244194e-06 1.50927396048839e-05 0.999992453630198 22 2.07946122459713e-05 4.15892244919427e-05 0.999979205387754 23 1.53563995989539e-05 3.07127991979079e-05 0.9999846436004 24 2.64764940408655e-05 5.29529880817309e-05 0.999973523505959 25 0.000304314973414154 0.000608629946828309 0.999695685026586 26 0.000617057557212414 0.00123411511442483 0.999382942442788 27 0.000680099842254827 0.00136019968450965 0.999319900157745 28 0.000558254465916214 0.00111650893183243 0.999441745534084 29 0.00036364230617476 0.00072728461234952 0.999636357693825 30 0.00025171437271054 0.00050342874542108 0.99974828562729 31 0.000151820693609304 0.000303641387218608 0.99984817930639 32 8.99472904022848e-05 0.000179894580804570 0.999910052709598 33 5.59921379925886e-05 0.000111984275985177 0.999944007862007 34 4.56150396589783e-05 9.12300793179565e-05 0.999954384960341 35 2.86814370405655e-05 5.7362874081131e-05 0.99997131856296 36 2.00398841299929e-05 4.00797682599859e-05 0.99997996011587 37 1.34093529289311e-05 2.68187058578621e-05 0.999986590647071 38 9.67818202034328e-06 1.93563640406866e-05 0.99999032181798 39 5.39636822420693e-06 1.07927364484139e-05 0.999994603631776 40 3.26235697250277e-06 6.52471394500555e-06 0.999996737643027 41 2.51278327933808e-06 5.02556655867617e-06 0.99999748721672 42 1.06542646860194e-06 2.13085293720389e-06 0.999998934573531 43 4.70746855102879e-06 9.41493710205758e-06 0.999995292531449 44 8.70510066233932e-06 1.74102013246786e-05 0.999991294899338 45 7.74823863091375e-06 1.54964772618275e-05 0.99999225176137 46 8.71832825284645e-06 1.74366565056929e-05 0.999991281671747 47 2.3531303258829e-05 4.7062606517658e-05 0.99997646869674 48 0.0159111644243968 0.0318223288487936 0.984088835575603 49 0.0364476221556426 0.0728952443112853 0.963552377844357 50 0.0230244179202818 0.0460488358405637 0.976975582079718 51 0.0125201093680853 0.0250402187361707 0.987479890631915 52 0.0147054716377828 0.0294109432755656 0.985294528362217 53 0.0115253657324846 0.0230507314649691 0.988474634267515 54 0.0435133073637958 0.0870266147275917 0.956486692636204 55 0.396994320950279 0.793988641900559 0.603005679049720 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 43 0.843137254901961 NOK 5% type I error level 48 0.941176470588235 NOK 10% type I error level 50 0.980392156862745 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/10bza01258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/10bza01258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/18vyn1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/18vyn1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/2trxe1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/2trxe1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/3c5xy1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/3c5xy1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/4fuvw1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/4fuvw1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/5wb7s1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/5wb7s1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/6daat1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/6daat1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/7hchq1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/7hchq1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/88p8m1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/88p8m1258725719.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/9kvro1258725719.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587259863agj88yjb6uc4s2/9kvro1258725719.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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