*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 08:09:06 -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/t1258729808oxz9g9dx0zexxgu.htm/, Retrieved Fri, 20 Nov 2009 16:10:20 +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/t1258729808oxz9g9dx0zexxgu.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:

» Textbox « » Textfile « » CSV «

555 0 562 0 561 0 555 0 544 0 537 0 543 0 594 0 611 0 613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 0 566 0 557 0 561 0 549 0 532 0 526 0 511 0 499 0 555 0 565 0 542 0 527 0 510 0 514 0 517 0 508 0 493 0 490 1 469 1 478 1 528 1 534 1 518 1 506 1 502 1 516 1 528 1 533 1 536 1 537 1 524 1 536 1 587 1 597 1 581 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 591.50371057514 -8.29035250463811X[t] -3.07157287157266M1[t] + 0.940806019377407M2[t] -1.71348175633892M3[t] -9.03443619872196M4[t] -12.6403318903319M5[t] -22.1279529993816M6[t] -18.2822407750979M7[t] + 33.8968047825191M8[t] + 44.4091836734694M9[t] + 35.4215625644197M10[t] + 14.3876211090497M11[t] -1.01237889095032t + 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) 591.50371057514 15.026986 39.3628 0 0 X -8.29035250463811 12.233982 -0.6776 0.500784 0.250392 M1 -3.07157287157266 17.448855 -0.176 0.860903 0.430452 M2 0.940806019377407 17.435715 0.054 0.95716 0.47858 M3 -1.71348175633892 17.426521 -0.0983 0.922024 0.461012 M4 -9.03443619872196 17.42128 -0.5186 0.606093 0.303046 M5 -12.6403318903319 17.514591 -0.7217 0.47348 0.23674 M6 -22.1279529993816 17.494108 -1.2649 0.211153 0.105576 M7 -18.2822407750979 17.477546 -1.046 0.300036 0.150018 M8 33.8968047825191 17.464916 1.9409 0.057316 0.028658 M9 44.4091836734694 17.456226 2.544 0.013744 0.006872 M10 35.4215625644197 17.451482 2.0297 0.047147 0.023574 M11 14.3876211090497 18.194291 0.7908 0.432411 0.216205 t -1.01237889095032 0.262558 -3.8558 3e-04 0.00015 Multiple Linear Regression - Regression Statistics Multiple R 0.763457076793766 R-squared 0.582866708106483 Adjusted R-squared 0.486032193916916 F-TEST (value) 6.01920413382199 F-TEST (DF numerator) 13 F-TEST (DF denominator) 56 p-value 7.92639047064725e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 28.7647049822638 Sum Squared Residuals 46334.8621521335 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 555 587.419758812615 -32.4197588126149 2 562 590.419758812616 -28.419758812616 3 561 586.753092145949 -25.7530921459494 4 555 578.419758812616 -23.419758812616 5 544 573.801484230056 -29.8014842300557 6 537 563.301484230056 -26.3014842300557 7 543 566.134817563389 -23.1348175633891 8 594 617.301484230056 -23.3014842300557 9 611 626.801484230056 -15.8014842300557 10 613 616.801484230056 -3.80148423005572 11 611 594.755163883735 16.2448361162646 12 594 579.355163883735 14.6448361162646 13 595 575.271212121212 19.7287878787876 14 591 578.271212121212 12.7287878787879 15 589 574.604545454545 14.3954545454545 16 584 566.271212121212 17.7287878787878 17 573 561.652937538652 11.3470624613481 18 567 551.152937538652 15.8470624613481 19 569 553.986270871985 15.0137291280148 20 621 605.152937538652 15.8470624613481 21 629 614.652937538652 14.3470624613481 22 628 604.652937538652 23.3470624613482 23 612 582.606617192331 29.3933828076685 24 595 567.206617192331 27.7933828076685 25 597 563.122665429809 33.8773345701915 26 593 566.122665429808 26.8773345701917 27 590 562.455998763142 27.5440012368584 28 580 554.122665429808 25.8773345701917 29 574 549.504390847248 24.495609152752 30 573 539.004390847248 33.995609152752 31 573 541.837724180581 31.1622758194187 32 620 593.004390847248 26.995609152752 33 626 602.504390847248 23.495609152752 34 620 592.504390847248 27.495609152752 35 588 570.458070500928 17.5419294990724 36 566 555.058070500928 10.9419294990724 37 557 550.974118738405 6.02588126159534 38 561 553.974118738404 7.02588126159559 39 549 550.307452071738 -1.30745207173775 40 532 541.974118738404 -9.97411873840442 41 526 537.355844155844 -11.3558441558441 42 511 526.855844155844 -15.8558441558441 43 499 529.689177489177 -30.6891774891774 44 555 580.855844155844 -25.8558441558441 45 565 590.355844155844 -25.3558441558441 46 542 580.355844155844 -38.3558441558441 47 527 558.309523809524 -31.3095238095238 48 510 542.909523809524 -32.9095238095238 49 514 538.825572047001 -24.8255720470008 50 517 541.825572047001 -24.8255720470006 51 508 538.158905380334 -30.1589053803339 52 493 529.825572047 -36.8255720470005 53 490 516.916944959802 -26.9169449598021 54 469 506.416944959802 -37.4169449598021 55 478 509.250278293135 -31.2502782931355 56 528 560.416944959802 -32.4169449598021 57 534 569.916944959802 -35.9169449598021 58 518 559.916944959802 -41.9169449598021 59 506 537.870624613482 -31.8706246134818 60 502 522.470624613482 -20.4706246134818 61 516 518.386672850959 -2.38667285095879 62 528 521.386672850959 6.61332714904145 63 533 517.720006184292 15.2799938157081 64 536 509.386672850959 26.6133271490415 65 537 504.768398268398 32.2316017316018 66 524 494.268398268398 29.7316017316018 67 536 497.101731601732 38.8982683982684 68 587 548.268398268398 38.7316017316018 69 597 557.768398268398 39.2316017316018 70 581 547.768398268398 33.2316017316018 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00411903984332895 0.0082380796866579 0.995880960156671 18 0.000482134583191116 0.000964269166382232 0.99951786541681 19 7.7669714250712e-05 0.000155339428501424 0.99992233028575 20 9.80996717100343e-06 1.96199343420069e-05 0.99999019003283 21 9.41010820462715e-06 1.88202164092543e-05 0.999990589891795 22 8.18733064884226e-06 1.63746612976845e-05 0.999991812669351 23 6.58260966644286e-05 0.000131652193328857 0.999934173903336 24 0.000100978748918412 0.000201957497836824 0.999899021251082 25 4.49679249799968e-05 8.99358499599937e-05 0.99995503207502 26 2.54917462437811e-05 5.09834924875622e-05 0.999974508253756 27 1.34306990079566e-05 2.68613980159132e-05 0.999986569300992 28 9.02518452776325e-06 1.80503690555265e-05 0.999990974815472 29 3.55065168030406e-06 7.10130336060813e-06 0.99999644934832 30 1.35337992499143e-06 2.70675984998287e-06 0.999998646620075 31 6.10635342241606e-07 1.22127068448321e-06 0.999999389364658 32 3.60935141565962e-07 7.21870283131924e-07 0.999999639064858 33 4.02388039935824e-07 8.04776079871649e-07 0.99999959761196 34 1.71833121303689e-06 3.43666242607379e-06 0.999998281668787 35 0.000145791678670359 0.000291583357340717 0.99985420832133 36 0.00360743579909755 0.00721487159819509 0.996392564200902 37 0.0261494816267839 0.0522989632535677 0.973850518373216 38 0.100417395847193 0.200834791694387 0.899582604152807 39 0.340756824639401 0.681513649278803 0.659243175360599 40 0.751196154346844 0.497607691306313 0.248803845653156 41 0.830815222309392 0.338369555381217 0.169184777690608 42 0.925729131390937 0.148541737218125 0.0742708686090625 43 0.947226368277213 0.105547263445574 0.0527736317227869 44 0.961023503433084 0.077952993133833 0.0389764965669165 45 0.978875856847086 0.0422482863058288 0.0211241431529144 46 0.991873572145554 0.0162528557088912 0.00812642785444561 47 0.997845498387438 0.00430900322512409 0.00215450161256204 48 0.999084970693295 0.00183005861341076 0.000915029306705382 49 0.99943369150073 0.00113261699853934 0.00056630849926967 50 0.999716131640888 0.000567736718224272 0.000283868359112136 51 0.99970223768906 0.000595524621878573 0.000297762310939286 52 0.998108773532647 0.00378245293470584 0.00189122646735292 53 0.998385811062537 0.00322837787492503 0.00161418893746252 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.72972972972973 NOK 5% type I error level 29 0.783783783783784 NOK 10% type I error level 31 0.837837837837838 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/10tez91258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/10tez91258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/1pn411258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/1pn411258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/2yz751258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/2yz751258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/3jsap1258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/3jsap1258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/4wra41258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/4wra41258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/55c7g1258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/55c7g1258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/6w4hl1258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/6w4hl1258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/7jxn41258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/7jxn41258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/81wtx1258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/81wtx1258729741.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/9hm4c1258729741.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729808oxz9g9dx0zexxgu/9hm4c1258729741.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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