Multiple lineair regression

*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 10:03:37 -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/t1258650576fb9ftcx1tnca0p1.htm/, Retrieved Thu, 19 Nov 2009 18:09:48 +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/t1258650576fb9ftcx1tnca0p1.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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19 613 18 611 19 594 19 595 22 591 23 589 20 584 14 573 14 567 14 569 15 621 11 629 17 628 16 612 20 595 24 597 23 593 20 590 21 580 19 574 23 573 23 573 23 620 23 626 27 620 26 588 17 566 24 557 26 561 24 549 27 532 27 526 26 511 24 499 23 555 23 565 24 542 17 527 21 510 19 514 22 517 22 508 18 493 16 490 14 469 12 478 14 528 16 534 8 518 3 506 0 502 5 516 1 528 1 533 3 536 6 537 7 524 8 536 14 587 14 597 13 581

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 ICONS[t] = -11.125036969505 + 0.0508454697869908WLH[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) -11.125036969505 11.878739 -0.9366 0.352808 0.176404 WLH 0.0508454697869908 0.021229 2.395 0.019812 0.009906 Multiple Linear Regression - Regression Statistics Multiple R 0.297672917091947 R-squared 0.0886091655700294 Adjusted R-squared 0.0731618632915554 F-TEST (value) 5.73622267323058 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0198119472356164 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.91478213732619 Sum Squared Residuals 2821.03850839443 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19 20.0432360099203 -1.04323600992026 2 18 19.9415450703464 -1.94154507034637 3 19 19.0771720839675 -0.077172083967523 4 19 19.1280175537545 -0.128017553754514 5 22 18.9246356746065 3.07536432539345 6 23 18.8229447350326 4.17705526496743 7 20 18.5687173860976 1.43128261390239 8 14 18.0094172184407 -4.00941721844072 9 14 17.7043443997188 -3.70434439971877 10 14 17.8060353392928 -3.80603533929275 11 15 20.4499997682163 -5.44999976821627 12 11 20.8567635265122 -9.8567635265122 13 17 20.8059180567252 -3.80591805672521 14 16 19.9923905401334 -3.99239054013336 15 20 19.1280175537545 0.871982446245486 16 24 19.2297084933285 4.7702915066715 17 23 19.0263266141805 3.97367338581947 18 20 18.8737902048196 1.12620979518044 19 21 18.3653355069497 2.63466449305035 20 19 18.0602626882277 0.939737311772293 21 23 18.0094172184407 4.99058278155928 22 23 18.0094172184407 4.99058278155928 23 23 20.3991542984293 2.60084570157072 24 23 20.7042271171512 2.29577288284877 25 27 20.3991542984293 6.60084570157072 26 26 18.7720992652456 7.22790073475442 27 17 17.6534989299318 -0.65349892993178 28 24 17.1958897018489 6.80411029815114 29 26 17.3992715809968 8.60072841900317 30 24 16.7891259435529 7.21087405644706 31 27 15.9247529571741 11.0752470428259 32 27 15.6196801384521 11.3803198615479 33 26 14.8569980916473 11.1430019083527 34 24 14.2468524542034 9.7531475457966 35 23 17.0941987622749 5.90580123772512 36 23 17.6026534601448 5.39734653985521 37 24 16.433207655044 7.566792344956 38 17 15.6705256082391 1.32947439176086 39 21 14.8061526218603 6.1938473781397 40 19 15.0095345010083 3.99046549899174 41 22 15.1620709103692 6.83792908963077 42 22 14.7044616822863 7.29553831771369 43 18 13.9417796354815 4.05822036451855 44 16 13.7892432261205 2.21075677387952 45 14 12.7214883605937 1.27851163940633 46 12 13.1790975886766 -1.17909758867659 47 14 15.7213710780261 -1.72137107802613 48 16 16.0264438967481 -0.0264438967480747 49 8 15.2129163801562 -7.21291638015622 50 3 14.6027707427123 -11.6027707427123 51 0 14.3993888635644 -14.3993888635644 52 5 15.1112254405822 -10.1112254405822 53 1 15.7213710780261 -14.7213710780261 54 1 15.9755984269611 -14.9755984269611 55 3 16.1281348363221 -13.1281348363221 56 6 16.1789803061090 -10.1789803061090 57 7 15.5179891988782 -8.51798919887817 58 8 16.1281348363221 -8.12813483632206 59 14 18.7212537954586 -4.72125379545859 60 14 19.2297084933285 -5.2297084933285 61 13 18.4161809767366 -5.41618097673664 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0090799264024347 0.0181598528048694 0.990920073597565 6 0.00410755875177945 0.0082151175035589 0.99589244124822 7 0.00132532382407052 0.00265064764814103 0.99867467617593 8 0.0121430108744538 0.0242860217489076 0.987856989125546 9 0.00741144158773550 0.014822883175471 0.992588558412264 10 0.00347713575861312 0.00695427151722625 0.996522864241387 11 0.00561132529740718 0.0112226505948144 0.994388674702593 12 0.0147842678034045 0.0295685356068090 0.985215732196596 13 0.00724966801005052 0.0144993360201010 0.99275033198995 14 0.00357728943422589 0.00715457886845178 0.996422710565774 15 0.00194252118751156 0.00388504237502311 0.998057478812489 16 0.00289714894266702 0.00579429788533403 0.997102851057333 17 0.00253608403131634 0.00507216806263267 0.997463915968684 18 0.00124648733011526 0.00249297466023052 0.998753512669885 19 0.000636392103521224 0.00127278420704245 0.999363607896479 20 0.000273248408838010 0.000546496817676021 0.999726751591162 21 0.000180211644242739 0.000360423288485478 0.999819788355757 22 0.000109303309431422 0.000218606618862844 0.999890696690569 23 0.000101136550365546 0.000202273100731091 0.999898863449634 24 7.87300068297314e-05 0.000157460013659463 0.99992126999317 25 0.000175237312289327 0.000350474624578655 0.99982476268771 26 0.000233747047821228 0.000467494095642456 0.999766252952179 27 0.000123002165147785 0.000246004330295570 0.999876997834852 28 9.97947764409741e-05 0.000199589552881948 0.99990020522356 29 0.000128262100639454 0.000256524201278908 0.99987173789936 30 9.61357099115602e-05 0.000192271419823120 0.999903864290088 31 0.000134255572527804 0.000268511145055608 0.999865744427472 32 0.000182046354305508 0.000364092708611016 0.999817953645694 33 0.000214749131017271 0.000429498262034541 0.999785250868983 34 0.000244209879786627 0.000488419759573253 0.999755790120213 35 0.000227174654823691 0.000454349309647382 0.999772825345176 36 0.000256471489431785 0.00051294297886357 0.999743528510568 37 0.000439165963218449 0.000878331926436897 0.999560834036782 38 0.00064821535164138 0.00129643070328276 0.999351784648359 39 0.000901578448267607 0.00180315689653521 0.999098421551732 40 0.00121941971384202 0.00243883942768403 0.998780580286158 41 0.00272939324257123 0.00545878648514247 0.997270606757429 42 0.00902682275817959 0.0180536455163592 0.99097317724182 43 0.0222794664337452 0.0445589328674904 0.977720533566255 44 0.0534922130710435 0.106984426142087 0.946507786928956 45 0.161285064703174 0.322570129406348 0.838714935296826 46 0.437450069957235 0.87490013991447 0.562549930042765 47 0.639120834816618 0.721758330366765 0.360879165183383 48 0.943869981582256 0.112260036835488 0.056130018417744 49 0.978975118105924 0.0420497637881527 0.0210248818940763 50 0.984117783060779 0.0317644338784423 0.0158822169392211 51 0.983639439269397 0.032721121461207 0.0163605607306035 52 0.981642728407857 0.0367145431842856 0.0183572715921428 53 0.98199885898181 0.0360022820363791 0.0180011410181896 54 0.99284160708743 0.0143167858251406 0.00715839291257028 55 0.998880084093904 0.00223983181219292 0.00111991590609646 56 0.99962575290676 0.000748494186482188 0.000374247093241094 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.634615384615385 NOK 5% type I error level 47 0.903846153846154 NOK 10% type I error level 47 0.903846153846154 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/10bwry1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/10bwry1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/16p1y1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/16p1y1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/2kqih1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/2kqih1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/31h5f1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/31h5f1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/45lrj1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/45lrj1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/59jwu1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/59jwu1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/6gc0h1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/6gc0h1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/7ftuv1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/7ftuv1258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/86s321258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/86s321258650213.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/94wkf1258650213.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650576fb9ftcx1tnca0p1/94wkf1258650213.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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