*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 07:29:56 -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/t1258727547itcnn62f0infnb9.htm/, Retrieved Fri, 20 Nov 2009 15:32:39 +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/t1258727547itcnn62f0infnb9.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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613 0 611 594 543 537 611 0 613 611 594 543 594 0 611 613 611 594 595 0 594 611 613 611 591 0 595 594 611 613 589 0 591 595 594 611 584 0 589 591 595 594 573 0 584 589 591 595 567 0 573 584 589 591 569 0 567 573 584 589 621 0 569 567 573 584 629 0 621 569 567 573 628 0 629 621 569 567 612 0 628 629 621 569 595 0 612 628 629 621 597 0 595 612 628 629 593 0 597 595 612 628 590 0 593 597 595 612 580 0 590 593 597 595 574 0 580 590 593 597 573 0 574 580 590 593 573 0 573 574 580 590 620 0 573 573 574 580 626 0 620 573 573 574 620 0 626 620 573 573 588 0 620 626 620 573 566 0 588 620 626 620 557 0 566 588 620 626 561 0 557 566 588 620 549 0 561 557 566 588 532 0 549 561 557 566 526 0 532 549 561 557 511 0 526 532 549 561 499 0 511 526 532 549 555 0 499 511 526 532 565 0 555 499 511 526 542 0 565 555 499 511 527 0 542 565 555 499 510 0 527 542 565 555 514 0 510 527 542 565 517 0 514 510 527 542 508 0 517 514 510 527 493 0 508 517 514 510 490 0 493 508 517 514 469 0 490 49 etc...

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] = + 79.3607618844651 + 12.4028527007149X[t] + 0.948720399414102Y1[t] + 0.0452222789877021Y2[t] -0.0408328304937311Y3[t] -0.0614850910601708Y4[t] -23.7371627758512M1[t] -27.9989333349529M2[t] -24.4920114941735M3[t] -6.19985693337634M4[t] -6.38345082070392M5[t] -13.9880587951269M6[t] -19.3849873747136M7[t] -15.2421114767019M8[t] -20.9699777464923M9[t] -8.16895780312471M10[t] + 41.071920254817M11[t] -0.36680636578864t + 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) 79.3607618844651 28.751888 2.7602 0.008456 0.004228 X 12.4028527007149 4.154524 2.9854 0.004659 0.002329 Y1 0.948720399414102 0.147118 6.4487 0 0 Y2 0.0452222789877021 0.205777 0.2198 0.827095 0.413548 Y3 -0.0408328304937311 0.20551 -0.1987 0.843441 0.421721 Y4 -0.0614850910601708 0.147137 -0.4179 0.678116 0.339058 M1 -23.7371627758512 10.356885 -2.2919 0.026868 0.013434 M2 -27.9989333349529 13.536032 -2.0685 0.044643 0.022321 M3 -24.4920114941735 11.115007 -2.2035 0.032964 0.016482 M4 -6.19985693337634 10.779711 -0.5751 0.568193 0.284097 M5 -6.38345082070392 8.57903 -0.7441 0.460877 0.230439 M6 -13.9880587951269 8.328297 -1.6796 0.100291 0.050146 M7 -19.3849873747136 9.408562 -2.0604 0.045448 0.022724 M8 -15.2421114767019 9.985531 -1.5264 0.134228 0.067114 M9 -20.9699777464923 9.375399 -2.2367 0.030545 0.015272 M10 -8.16895780312471 10.069057 -0.8113 0.421665 0.210833 M11 41.071920254817 8.5649 4.7954 2e-05 1e-05 t -0.36680636578864 0.120071 -3.0549 0.003857 0.001928 Multiple Linear Regression - Regression Statistics Multiple R 0.991204866841592 R-squared 0.98248708805046 Adjusted R-squared 0.97556337867506 F-TEST (value) 141.901838274907 F-TEST (DF numerator) 17 F-TEST (DF denominator) 43 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.5733111803007 Sum Squared Residuals 1857.96205454184 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 613 606.59726964613 6.40273035387012 2 611 602.183527361317 8.81647263868347 3 594 599.686748832992 -5.68674883299231 4 595 600.266493470975 -5.26649347097542 5 591 599.85473035335 -8.85473035334952 6 589 588.950784994983 0.0492150050170205 7 584 582.113133852358 1.88686614764226 8 573 581.15700306045 -8.15700306044966 9 567 564.727900661605 2.27209933839482 10 569 571.299481108424 -2.29948110842371 11 621 622.556246516211 -1.55624651621067 12 629 631.462758207738 -2.46275820773805 13 628 617.587355654145 10.4126443458549 14 612 610.125559193948 1.87444080605214 15 595 594.517038620247 0.482961379753423 16 597 595.139535663424 1.86046433657554 17 593 596.432607845305 -3.43260784530546 18 590 586.434676040769 3.565323959231 19 580 578.607471668236 1.39252833176405 20 574 572.80103150921 1.19896849079054 21 573 560.930252542991 12.0697474570093 22 573 572.737195625347 0.262804374652884 23 620 622.425892932077 -2.42589293207659 24 626 625.986768460789 0.0132315392114816 25 620 609.762053919115 10.2379460808845 26 588 597.793345238461 -9.7933452384613 27 566 567.168277995484 -1.16827799548417 28 557 562.650750912377 -5.65075091237739 29 561 554.242538048965 6.75746195103478 30 549 552.524849980308 -3.52484998030826 31 532 537.277526835682 -5.2775268356818 32 526 524.772716727579 1.22728327242068 33 511 512.460996554409 -1.46099655440884 34 499 511.825049677965 -12.8250496779655 35 555 549.926385923319 5.0736140766809 36 565 562.054737325818 2.94526267418223 37 542 551.382690133458 -9.38269013345761 38 527 523.836949396993 3.16305060300701 39 510 507.854653059748 2.14534694025179 40 514 509.297724470656 4.7022755293444 41 517 513.800076624195 3.19992337580525 42 508 510.472147082472 -2.47214708247227 43 493 497.187510605381 -4.18751060538104 44 490 485.957334779781 4.04266522021862 45 469 476.521206962408 -7.5212069624076 46 478 470.062483592275 7.93751640772524 47 528 527.570145877797 0.429854122203224 48 534 535.016384452334 -1.01638445233443 49 518 532.192395795099 -14.1923957950993 50 506 510.060618809281 -4.06061880928134 51 502 497.773281491529 4.22671850847127 52 516 511.645495482567 4.35450451743287 53 528 525.670047128185 2.32995287181495 54 533 530.617541901467 2.38245809853252 55 536 529.814357038343 6.18564296165652 56 537 535.31191392298 1.68808607701981 57 524 529.359643278588 -5.35964327858768 58 536 529.075789995989 6.92421000401108 59 587 588.521328750597 -1.52132875059686 60 597 596.479351553321 0.520648446678803 61 581 584.478234852053 -3.47823485205266 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.311461812612402 0.622923625224803 0.688538187387598 22 0.166212528929503 0.332425057859007 0.833787471070497 23 0.0853904517369203 0.170780903473841 0.91460954826308 24 0.0378965674209491 0.0757931348418981 0.96210343257905 25 0.0935274385532758 0.187054877106552 0.906472561446724 26 0.71842378897811 0.563152422043781 0.281576211021891 27 0.615245025683428 0.769509948633143 0.384754974316572 28 0.632863399017075 0.73427320196585 0.367136600982925 29 0.572649685443431 0.854700629113138 0.427350314556569 30 0.515510554772125 0.96897889045575 0.484489445227875 31 0.512852873394608 0.974294253210785 0.487147126605392 32 0.507174275427638 0.985651449144724 0.492825724572362 33 0.567349085719258 0.865301828561483 0.432650914280742 34 0.899785504468328 0.200428991063345 0.100214495531673 35 0.935984800982208 0.128030398035584 0.0640151990177919 36 0.945479285620718 0.109041428758564 0.054520714379282 37 0.945038732790595 0.109922534418809 0.0549612672094046 38 0.923275710754993 0.153448578490013 0.0767242892450066 39 0.841761800407835 0.316476399184331 0.158238199592165 40 0.784529680450721 0.430940639098557 0.215470319549279 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 0 0 OK 10% type I error level 1 0.05 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/10gya1258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/10gya1258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/10yq3l1258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/10yq3l1258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/26adn1258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/26adn1258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/397x91258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/397x91258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/4r7ty1258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/4r7ty1258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/5dgr21258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/5dgr21258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/69l2q1258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/69l2q1258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/7piya1258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/7piya1258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/8tpu41258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/8tpu41258727391.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/9dnb01258727391.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727547itcnn62f0infnb9/9dnb01258727391.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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