Workshop7 Linear trend

*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 03:09:50 -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/t1258711873qa82docukka68y8.htm/, Retrieved Fri, 20 Nov 2009 11:11:25 +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/t1258711873qa82docukka68y8.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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562325 0 560854 0 555332 0 543599 0 536662 0 542722 0 593530 0 610763 0 612613 0 611324 0 594167 0 595454 0 590865 0 589379 0 584428 0 573100 0 567456 0 569028 0 620735 0 628884 0 628232 0 612117 0 595404 0 597141 0 593408 0 590072 0 579799 0 574205 0 572775 0 572942 0 619567 0 625809 0 619916 0 587625 0 565742 0 557274 0 560576 0 548854 0 531673 0 525919 0 511038 0 498662 1 555362 1 564591 1 541657 1 527070 1 509846 1 514258 1 516922 1 507561 1 492622 1 490243 1 469357 1 477580 1 528379 1 533590 1 517945 1 506174 1 501866 1 516141 1 528222 1 532638 1 536322 1 536535 1 523597 1 536214 1 586570 1 596594 1 580523 1

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] = + 575726.747205707 -66470.4227110583X[t] -804.875142026716M1[t] -4823.62574976884M2[t] -13212.7096908442M3[t] -19500.6269652530M4[t] -30145.3775729951M5[t] -16548.5577288942M6[t] + 34425.1916633637M7[t] + 43581.1077222883M8[t] + 33498.1904478795M9[t] + 13192.5678821509M10[t] -2456.51605892456M11[t] + 192.08394107544t + 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) 575726.747205707 11286.27915 51.0112 0 0 X -66470.4227110583 10150.669975 -6.5484 0 0 M1 -804.875142026716 13039.571626 -0.0617 0.951005 0.475503 M2 -4823.62574976884 13029.148789 -0.3702 0.712642 0.356321 M3 -13212.7096908442 13023.484952 -1.0145 0.314773 0.157386 M4 -19500.6269652530 13022.586324 -1.4974 0.139995 0.069997 M5 -30145.3775729951 13026.453891 -2.3142 0.02442 0.01221 M6 -16548.5577288942 13057.021954 -1.2674 0.21035 0.105175 M7 34425.1916633637 13042.907515 2.6394 0.010785 0.005392 M8 43581.1077222883 13033.54145 3.3438 0.001493 0.000746 M9 33498.1904478795 13028.933999 2.5711 0.012874 0.006437 M10 13192.5678821509 13605.469765 0.9697 0.336465 0.168233 M11 -2456.51605892456 13598.62417 -0.1806 0.857311 0.428655 t 192.08394107544 249.150699 0.771 0.444033 0.222017 Multiple Linear Regression - Regression Statistics Multiple R 0.878227212463633 R-squared 0.771283036711643 Adjusted R-squared 0.717222663570759 F-TEST (value) 14.2670683145608 F-TEST (DF numerator) 13 F-TEST (DF denominator) 55 p-value 3.20965476419133e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21497.7035540613 Sum Squared Residuals 25418319195.4065 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 562325 575113.956004756 -12788.9560047564 2 560854 571287.28933809 -10433.2893380897 3 555332 563090.28933809 -7758.28933808959 4 543599 556994.456004756 -13395.4560047562 5 536662 546541.789338090 -9879.78933808959 6 542722 560330.693123266 -17608.6931232660 7 593530 611496.526456599 -17966.5264565993 8 610763 620844.526456599 -10081.5264565993 9 612613 610953.693123266 1659.30687673404 10 611324 590840.154498613 20483.8455013873 11 594167 575383.154498613 18783.8455013872 12 595454 578031.754498613 17422.2455013872 13 590865 577418.963297662 13446.0367023385 14 589379 573592.296630995 15786.7033690052 15 584428 565395.296630995 19032.7033690052 16 573100 559299.463297662 13800.5367023385 17 567456 548846.796630995 18609.2033690052 18 569028 562635.700416171 6392.29958382878 19 620735 613801.533749505 6933.46625049545 20 628884 623149.533749505 5734.46625049545 21 628232 613258.700416171 14973.2995838288 22 612117 593145.161791518 18971.8382084820 23 595404 577688.161791518 17715.8382084820 24 597141 580336.761791518 16804.2382084820 25 593408 579723.970590567 13684.0294094333 26 590072 575897.3039239 14174.6960760999 27 579799 567700.3039239 12098.6960760999 28 574205 561604.470590567 12600.5294094332 29 572775 551151.8039239 21623.1960760999 30 572942 564940.707709076 8001.2922909235 31 619567 616106.54104241 3460.45895759017 32 625809 625454.54104241 354.458957590171 33 619916 615563.707709076 4352.2922909235 34 587625 595450.169084423 -7825.16908442332 35 565742 579993.169084423 -14251.1690844233 36 557274 582641.769084423 -25367.7690844233 37 560576 582028.977883472 -21452.9778834720 38 548854 578202.311216805 -29348.3112168054 39 531673 570005.311216805 -38332.3112168054 40 525919 563909.477883472 -37990.4778834721 41 511038 553456.811216805 -42418.8112168054 42 498662 500775.292290923 -2113.2922909235 43 555362 551941.125624257 3420.87437574317 44 564591 561289.125624257 3301.87437574316 45 541657 551398.292290924 -9741.2922909235 46 527070 531284.75366627 -4214.75366627031 47 509846 515827.75366627 -5981.75366627032 48 514258 518476.35366627 -4218.35366627032 49 516922 517863.562465319 -941.562465319028 50 507561 514036.895798652 -6475.89579865238 51 492622 505839.895798652 -13217.8957986524 52 490243 499744.062465319 -9501.06246531906 53 469357 489291.395798652 -19934.3957986524 54 477580 503080.299583829 -25500.2995838288 55 528379 554246.132917162 -25867.1329171621 56 533590 563594.132917162 -30004.1329171621 57 517945 553703.299583829 -35758.2995838288 58 506174 533589.760959176 -27415.7609591756 59 501866 518132.760959176 -16266.7609591756 60 516141 520781.360959176 -4640.36095917559 61 528222 520168.569758224 8053.4302417757 62 532638 516341.903091558 16296.0969084423 63 536322 508144.903091558 28177.0969084423 64 536535 502049.069758224 34485.9302417757 65 523597 491596.403091558 32000.5969084423 66 536214 505385.306876734 30828.6931232659 67 586570 556551.140210067 30018.8597899326 68 596594 565899.140210067 30694.8597899326 69 580523 556008.306876734 24514.6931232659 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 4.75383306438665e-05 9.5076661287733e-05 0.999952461669356 18 1.19706509980433e-05 2.39413019960866e-05 0.999988029349002 19 7.67216069366232e-07 1.53443213873246e-06 0.99999923278393 20 1.72037544255747e-05 3.44075088511493e-05 0.999982796245574 21 2.54757208751506e-05 5.09514417503011e-05 0.999974524279125 22 0.000522803680025649 0.00104560736005130 0.999477196319974 23 0.00100751787159313 0.00201503574318627 0.998992482128407 24 0.00116930484583806 0.00233860969167612 0.998830695154162 25 0.00075059313307377 0.00150118626614754 0.999249406866926 26 0.000503064609366793 0.00100612921873359 0.999496935390633 27 0.000456867087111288 0.000913734174222577 0.99954313291289 28 0.000257466978091438 0.000514933956182875 0.999742533021909 29 0.000218493807874308 0.000436987615748616 0.999781506192126 30 0.000115518209266206 0.000231036418532412 0.999884481790734 31 7.08576702481385e-05 0.000141715340496277 0.999929142329752 32 7.17790928857272e-05 0.000143558185771454 0.999928220907114 33 0.000231922292189748 0.000463844584379497 0.99976807770781 34 0.00613920346572203 0.0122784069314441 0.993860796534278 35 0.0375052359899874 0.0750104719799749 0.962494764010013 36 0.119701609899286 0.239403219798571 0.880298390100714 37 0.144086045004329 0.288172090008659 0.85591395499567 38 0.179870631161577 0.359741262323154 0.820129368838423 39 0.227971066662546 0.455942133325092 0.772028933337454 40 0.229551074850393 0.459102149700786 0.770448925149607 41 0.248199393945709 0.496398787891418 0.751800606054291 42 0.200697042146355 0.401394084292709 0.799302957853645 43 0.18496830953507 0.36993661907014 0.81503169046493 44 0.201227018394088 0.402454036788177 0.798772981605911 45 0.24364706163685 0.4872941232737 0.75635293836315 46 0.422967102681411 0.845934205362821 0.577032897318589 47 0.609626778782343 0.780746442435315 0.390373221217657 48 0.791692967961256 0.416614064077489 0.208307032038744 49 0.937423953284224 0.125152093431553 0.0625760467157764 50 0.989593831670816 0.0208123366583679 0.0104061683291840 51 0.988617156448487 0.0227656871030267 0.0113828435515133 52 0.993416769515177 0.0131664609696456 0.00658323048482278 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.472222222222222 NOK 5% type I error level 21 0.583333333333333 NOK 10% type I error level 22 0.611111111111111 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/10jgr41258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/10jgr41258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/1wthc1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/1wthc1258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/2rp9d1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/2rp9d1258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/378vs1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/378vs1258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/41k491258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/41k491258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/5oi281258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/5oi281258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/69s3b1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/69s3b1258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/7c0jc1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/7c0jc1258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/8qnez1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/8qnez1258711785.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/9e7ha1258711785.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258711873qa82docukka68y8/9e7ha1258711785.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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