*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 07:49:03 -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/t12587287806axcngoja85bjzu.htm/, Retrieved Fri, 20 Nov 2009 15:53:12 +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/t12587287806axcngoja85bjzu.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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 569.211538461539 -41.4337606837607X[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) 569.211538461539 4.992182 114.0206 0 0 X -41.4337606837607 9.844716 -4.2087 7.7e-05 3.9e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.454597189459123 R-squared 0.206658604664134 Adjusted R-squared 0.194991819438606 F-TEST (value) 17.7134146784459 F-TEST (DF numerator) 1 F-TEST (DF denominator) 68 p-value 7.70627714348215e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 35.9991389169965 Sum Squared Residuals 88123.7841880342 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 555 569.211538461537 -14.2115384615372 2 562 569.211538461538 -7.21153846153839 3 561 569.211538461538 -8.21153846153849 4 555 569.211538461538 -14.2115384615385 5 544 569.211538461538 -25.2115384615385 6 537 569.211538461538 -32.2115384615385 7 543 569.211538461538 -26.2115384615385 8 594 569.211538461538 24.7884615384615 9 611 569.211538461538 41.7884615384615 10 613 569.211538461538 43.7884615384615 11 611 569.211538461538 41.7884615384615 12 594 569.211538461538 24.7884615384615 13 595 569.211538461538 25.7884615384615 14 591 569.211538461538 21.7884615384615 15 589 569.211538461538 19.7884615384615 16 584 569.211538461538 14.7884615384615 17 573 569.211538461538 3.78846153846151 18 567 569.211538461538 -2.21153846153849 19 569 569.211538461538 -0.211538461538488 20 621 569.211538461538 51.7884615384615 21 629 569.211538461538 59.7884615384615 22 628 569.211538461538 58.7884615384615 23 612 569.211538461538 42.7884615384615 24 595 569.211538461538 25.7884615384615 25 597 569.211538461538 27.7884615384615 26 593 569.211538461538 23.7884615384615 27 590 569.211538461538 20.7884615384615 28 580 569.211538461538 10.7884615384615 29 574 569.211538461538 4.78846153846151 30 573 569.211538461538 3.78846153846151 31 573 569.211538461538 3.78846153846151 32 620 569.211538461538 50.7884615384615 33 626 569.211538461538 56.7884615384615 34 620 569.211538461538 50.7884615384615 35 588 569.211538461538 18.7884615384615 36 566 569.211538461538 -3.21153846153849 37 557 569.211538461538 -12.2115384615385 38 561 569.211538461538 -8.21153846153849 39 549 569.211538461538 -20.2115384615385 40 532 569.211538461538 -37.2115384615385 41 526 569.211538461538 -43.2115384615385 42 511 569.211538461538 -58.2115384615385 43 499 569.211538461538 -70.2115384615385 44 555 569.211538461538 -14.2115384615385 45 565 569.211538461538 -4.21153846153849 46 542 569.211538461538 -27.2115384615385 47 527 569.211538461538 -42.2115384615385 48 510 569.211538461538 -59.2115384615385 49 514 569.211538461538 -55.2115384615385 50 517 569.211538461538 -52.2115384615385 51 508 569.211538461538 -61.2115384615385 52 493 569.211538461538 -76.2115384615385 53 490 527.777777777778 -37.7777777777778 54 469 527.777777777778 -58.7777777777778 55 478 527.777777777778 -49.7777777777778 56 528 527.777777777778 0.222222222222217 57 534 527.777777777778 6.22222222222222 58 518 527.777777777778 -9.77777777777778 59 506 527.777777777778 -21.7777777777778 60 502 527.777777777778 -25.7777777777778 61 516 527.777777777778 -11.7777777777778 62 528 527.777777777778 0.222222222222217 63 533 527.777777777778 5.22222222222222 64 536 527.777777777778 8.22222222222222 65 537 527.777777777778 9.22222222222222 66 524 527.777777777778 -3.77777777777778 67 536 527.777777777778 8.22222222222222 68 587 527.777777777778 59.2222222222222 69 597 527.777777777778 69.2222222222222 70 581 527.777777777778 53.2222222222222 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0147486041367335 0.0294972082734670 0.985251395863266 6 0.0141690604894559 0.0283381209789117 0.985830939510544 7 0.00501776261053816 0.0100355252210763 0.994982237389462 8 0.0460381383695722 0.0920762767391444 0.953961861630428 9 0.163315300309978 0.326630600619956 0.836684699690022 10 0.258073857391292 0.516147714782585 0.741926142608708 11 0.300850214313933 0.601700428627866 0.699149785686067 12 0.248006787274869 0.496013574549738 0.751993212725131 13 0.201912270025968 0.403824540051937 0.798087729974032 14 0.153146120328596 0.306292240657192 0.846853879671404 15 0.110889394693894 0.221778789387788 0.889110605306106 16 0.0749015602041112 0.149803120408222 0.925098439795889 17 0.0478498260605723 0.0956996521211447 0.952150173939428 18 0.0305385315496295 0.0610770630992591 0.96946146845037 19 0.0185856974214338 0.0371713948428677 0.981414302578566 20 0.0324116074519563 0.0648232149039126 0.967588392548044 21 0.0661886549785211 0.132377309957042 0.933811345021479 22 0.110710532054162 0.221421064108325 0.889289467945838 23 0.116507512054175 0.233015024108350 0.883492487945825 24 0.0949055517137583 0.189811103427517 0.905094448286242 25 0.0795241213472794 0.159048242694559 0.92047587865272 26 0.0640536775595692 0.128107355119138 0.93594632244043 27 0.050341389607052 0.100682779214104 0.949658610392948 28 0.0372224469156677 0.0744448938313354 0.962777553084332 29 0.0272541024801680 0.0545082049603359 0.972745897519832 30 0.0198045479372334 0.0396090958744669 0.980195452062767 31 0.0142738059090299 0.0285476118180598 0.98572619409097 32 0.0291131062493444 0.0582262124986887 0.970886893750656 33 0.080854788189021 0.161709576378042 0.91914521181098 34 0.184878677890877 0.369757355781753 0.815121322109123 35 0.212890286095576 0.425780572191152 0.787109713904424 36 0.214874747133914 0.429749494267829 0.785125252866086 37 0.219599924815442 0.439199849630884 0.780400075184558 38 0.227625147643424 0.455250295286848 0.772374852356576 39 0.240274266282091 0.480548532564182 0.759725733717909 40 0.278451111616761 0.556902223233521 0.72154888838324 41 0.322874663701468 0.645749327402937 0.677125336298532 42 0.414174479130677 0.828348958261353 0.585825520869323 43 0.550080591320278 0.899838817359445 0.449919408679722 44 0.534398057690549 0.931203884618902 0.465601942309451 45 0.565400071289678 0.869199857420644 0.434599928710322 46 0.560932834419994 0.878134331160012 0.439067165580006 47 0.555875363686228 0.888249272627543 0.444124636313772 48 0.568604032761834 0.862791934476331 0.431395967238166 49 0.562015134642845 0.87596973071431 0.437984865357155 50 0.547156430104993 0.905687139790013 0.452843569895007 51 0.538546653244873 0.922906693510254 0.461453346755127 52 0.544967482950957 0.910065034098086 0.455032517049043 53 0.534985057305791 0.930029885388418 0.465014942694209 54 0.664037927564132 0.671924144871736 0.335962072435868 55 0.773002144401241 0.453995711197519 0.226997855598759 56 0.720484892143126 0.559030215713748 0.279515107856874 57 0.650577457379548 0.698845085240903 0.349422542620452 58 0.589489008667258 0.821021982665483 0.410510991332742 59 0.581903254227667 0.836193491544666 0.418096745772333 60 0.628292236339869 0.743415527320261 0.371707763660131 61 0.617155336361437 0.765689327277125 0.382844663638563 62 0.557764408579505 0.884471182840991 0.442235591420495 63 0.48035025133726 0.96070050267452 0.51964974866274 64 0.394119843283193 0.788239686566386 0.605880156716807 65 0.312625123032388 0.625250246064777 0.687374876967612 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 6 0.098360655737705 NOK 10% type I error level 13 0.213114754098361 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/10odyf1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/10odyf1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/1vjcb1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/1vjcb1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/2bj0w1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/2bj0w1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/3czwj1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/3czwj1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/4tfqz1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/4tfqz1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/5ba2j1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/5ba2j1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/63i311258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/63i311258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/78fnb1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/78fnb1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/8tcpx1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/8tcpx1258728538.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/9n2nn1258728538.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587287806axcngoja85bjzu/9n2nn1258728538.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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