*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: Thu, 19 Nov 2009 02:40:11 -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/19/t1258625101p3071rm00i725kw.htm/, Retrieved Thu, 19 Nov 2009 11:05:14 +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/19/t1258625101p3071rm00i725kw.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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96.8 610763 114.1 612613 110.3 611324 103.9 594167 101.6 595454 94.6 590865 95.9 589379 104.7 584428 102.8 573100 98.1 567456 113.9 569028 80.9 620735 95.7 628884 113.2 628232 105.9 612117 108.8 595404 102.3 597141 99 593408 100.7 590072 115.5 579799 100.7 574205 109.9 572775 114.6 572942 85.4 619567 100.5 625809 114.8 619916 116.5 587625 112.9 565742 102 557274 106 560576 105.3 548854 118.8 531673 106.1 525919 109.3 511038 117.2 498662 92.5 555362 104.2 564591 112.5 541657 122.4 527070 113.3 509846 100 514258 110.7 516922 112.8 507561 109.8 492622 117.3 490243 109.1 469357 115.9 477580 96 528379 99.8 533590 116.8 517945 115.7 506174 99.4 501866 94.3 516141 91 528222 93.2 532638 103.1 536322 94.1 536535 91.8 523597 102.7 536214 82.6 586570 89.1 596594

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 Tot_nietwerkende_werkzoekenden[t] = + 760951.910845173 -2044.23080527175Tot_ind_productie[t] + 32107.2016497893M1[t] + 56735.3855812829M2[t] + 41279.4778846503M3[t] + 12534.9776503839M4[t] -393.461085786818M5[t] + 2001.26969137297M6[t] + 401.854354331684M7[t] + 9659.08544072309M8[t] -7942.66093585633M9[t] -20243.2301868085M10[t] + 645.177837797036M11[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) 760951.910845173 78419.25673 9.7036 0 0 Tot_ind_productie -2044.23080527175 873.385413 -2.3406 0.023457 0.011728 M1 32107.2016497893 25524.458209 1.2579 0.214513 0.107257 M2 56735.3855812829 34234.000293 1.6573 0.103985 0.051993 M3 41279.4778846503 34162.426984 1.2083 0.23284 0.11642 M4 12534.9776503839 30573.346758 0.41 0.683632 0.341816 M5 -393.461085786818 27284.181946 -0.0144 0.988554 0.494277 M6 2001.26969137297 27362.000174 0.0731 0.941998 0.470999 M7 401.854354331684 27852.190072 0.0144 0.988548 0.494274 M8 9659.08544072309 32001.763216 0.3018 0.764086 0.382043 M9 -7942.66093585633 28936.819784 -0.2745 0.784891 0.392446 M10 -20243.2301868085 28693.103999 -0.7055 0.483904 0.241952 M11 645.177837797036 33398.300829 0.0193 0.984668 0.492334 Multiple Linear Regression - Regression Statistics Multiple R 0.556241731077129 R-squared 0.309404863391681 Adjusted R-squared 0.136756079239601 F-TEST (value) 1.79210566069865 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.0766682421656164 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 39499.7408316648 Sum Squared Residuals 74891017236.897 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 610763 595177.570544657 15585.4294553426 2 612613 584440.561544949 28172.4384550511 3 611324 576752.730908349 34571.2690916511 4 594167 561091.307827822 33075.6921721782 5 595454 552864.599943776 42589.4000562240 6 590865 569568.946357838 21296.0536421619 7 589379 565312.030973944 24066.9690260565 8 584428 556580.030973944 27847.9690260565 9 573100 542862.32312738 30237.6768726195 10 567456 540169.638661206 27286.3613387945 11 569028 528759.199962517 40268.8000374826 12 620735 595573.638698688 25161.3613013119 13 628884 597426.224430455 31457.7755695445 14 628232 586280.369269694 41951.6307303065 15 612117 585747.346451545 26369.6535484553 16 595404 551074.57688199 44329.4231180098 17 597141 551433.638380086 45707.3616199142 18 593408 560574.330814642 32833.6691853576 19 590072 555499.723108639 34572.2768913609 20 579799 534502.338277009 45296.6617229914 21 574205 547155.207818451 27049.7921815489 22 572775 516047.715158999 56727.2848410012 23 572942 527328.238398827 45613.7616011728 24 619567 586374.600074965 33192.3999250348 25 625809 587613.916565151 38195.0834348489 26 619916 583009.599981259 36906.4000187413 27 587625 564078.499915664 23546.5000843359 28 565742 542693.230580376 23048.769419624 29 557274 552046.907621667 5227.09237833263 30 560576 546264.71517774 14311.2848222599 31 548854 546096.261404389 2757.73859561091 32 531673 527756.376619612 3916.62338038815 33 525919 536116.361469984 -10197.3614699837 34 511038 517274.253642162 -6236.25364216188 35 498662 522013.23830512 -23351.2383051206 36 555362 571860.561357536 -16498.5613575358 37 564591 580050.262585646 -15459.2625856456 38 541657 587711.330833384 -46054.3308333837 39 527070 552017.538164561 -24947.5381645607 40 509846 541875.538258267 -32029.5382582673 41 514258 556135.369232211 -41877.3692322109 42 516922 536656.830392963 -19734.8303929629 43 507561 530764.530364851 -23203.5303648510 44 492622 546154.453867058 -53532.4538670576 45 490243 513220.97645094 -22977.9764509400 46 469357 517683.099803216 -48326.0998032162 47 477580 524670.738351974 -47090.7383519738 48 528379 564705.753539085 -36326.7535390847 49 533590 589044.878128841 -55454.8781288413 50 517945 578921.138370715 -60976.1383707152 51 506174 565713.884559882 -59539.8845598815 52 501866 570290.346451545 -68424.3464515447 53 516141 567787.48482226 -51646.4848222599 54 528222 576928.177256816 -48706.1772568164 55 532638 570831.454148177 -38193.4541481772 56 536322 559850.800262378 -23528.8002623783 57 536535 560647.131133245 -24112.1311332447 58 523597 553048.292734418 -29451.2927344175 59 536214 551654.584981561 -15440.5849815610 60 586570 592098.446329726 -5528.44632972616 61 596594 610918.147745249 -14324.1477452491 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0194194944502963 0.0388389889005925 0.980580505549704 17 0.00444982967359378 0.00889965934718757 0.995550170326406 18 0.000986536918931643 0.00197307383786329 0.999013463081068 19 0.000202181259440084 0.000404362518880168 0.99979781874056 20 4.33620109211557e-05 8.67240218423115e-05 0.999956637989079 21 8.28313972603116e-06 1.65662794520623e-05 0.999991716860274 22 3.20550616958073e-06 6.41101233916146e-06 0.99999679449383 23 1.05581314821786e-06 2.11162629643571e-06 0.999998944186852 24 2.71478086110986e-07 5.42956172221972e-07 0.999999728521914 25 1.33274674714065e-07 2.66549349428130e-07 0.999999866725325 26 1.27310894771374e-07 2.54621789542748e-07 0.999999872689105 27 6.25531859213443e-06 1.25106371842689e-05 0.999993744681408 28 0.000170101917897538 0.000340203835795075 0.999829898082103 29 0.00769467361028357 0.0153893472205671 0.992305326389716 30 0.0181412059234173 0.0362824118468345 0.981858794076583 31 0.0444166092019266 0.0888332184038533 0.955583390798073 32 0.120630450571965 0.24126090114393 0.879369549428035 33 0.180958636303973 0.361917272607947 0.819041363696027 34 0.356148924795929 0.712297849591857 0.643851075204071 35 0.55612165663608 0.88775668672784 0.44387834336392 36 0.519710125445669 0.960579749108662 0.480289874554331 37 0.52168997774098 0.95662004451804 0.47831002225902 38 0.724684093346368 0.550631813307264 0.275315906653632 39 0.744208790310348 0.511582419379304 0.255791209689652 40 0.829858775315769 0.340282449368461 0.170141224684231 41 0.838035234399811 0.323929531200377 0.161964765600189 42 0.871881566317738 0.256236867364524 0.128118433682262 43 0.89061887588855 0.218762248222901 0.109381124111450 44 0.887951501443788 0.224096997112425 0.112048498556212 45 0.945416979435624 0.109166041128752 0.0545830205643761 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.4 NOK 5% type I error level 15 0.5 NOK 10% type I error level 16 0.533333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/101z8w1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/101z8w1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/1ymex1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/1ymex1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/2rfxb1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/2rfxb1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/33nob1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/33nob1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/45wgy1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/45wgy1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/52zbc1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/52zbc1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/63dl51258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/63dl51258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/7s3um1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/7s3um1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/87g4u1258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/87g4u1258623606.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/9n6l01258623606.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258625101p3071rm00i725kw/9n6l01258623606.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly 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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