*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: Mon, 23 Nov 2009 06:58:38 -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/23/t1258984761ifulxwhls059xuj.htm/, Retrieved Mon, 23 Nov 2009 14:59:37 +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/23/t1258984761ifulxwhls059xuj.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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99.9 98.8 98.6 100.5 107.2 110.4 95.7 96.4 93.7 101.9 106.7 106.2 86.7 81 95.3 94.7 99.3 101 101.8 109.4 96 102.3 91.7 90.7 95.3 96.2 96.6 96.1 107.2 106 108 103.1 98.4 102 103.1 104.7 81.1 86 96.6 92.1 103.7 106.9 106.6 112.6 97.6 101.7 87.6 92 99.4 97.4 98.5 97 105.2 105.4 104.6 102.7 97.5 98.1 108.9 104.5 86.8 87.4 88.9 89.9 110.3 109.8 114.8 111.7 94.6 98.6 92 96.9 93.8 95.1 93.8 97 107.6 112.7 101 102.9 95.4 97.4 96.5 111.4 89.2 87.4 87.1 96.8 110.5 114.1 110.8 110.3 104.2 103.9 88.9 101.6 89.8 94.6 90 95.9 93.9 104.7 91.3 102.8 87.8 98.1 99.7 113.9 73.5 80.9 79.2 95.7 96.9 113.2 95.2 105.9 95.6 108.8 89.7 102.3

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 TotProd[t] = + 39.1032602682758 + 0.58527352083724ProdMetal[t] + 4.07634027845658M1[t] + 3.5801665171541M2[t] + 6.29025054456396M3[t] + 6.01292972203943M4[t] + 1.82916558241524M5[t] + 5.35126929941585M6[t] -0.19740867179095M7[t] + 0.478414521457085M8[t] + 6.48453488259889M9[t] + 7.76983376921275M10[t] + 3.73879347044081M11[t] -0.158866937034356t + 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) 39.1032602682758 18.931742 2.0655 0.044538 0.022269 ProdMetal 0.58527352083724 0.198199 2.953 0.004944 0.002472 M1 4.07634027845658 2.926556 1.3929 0.170352 0.085176 M2 3.5801665171541 2.93028 1.2218 0.228014 0.114007 M3 6.29025054456396 3.718093 1.6918 0.09745 0.048725 M4 6.01292972203943 3.098527 1.9406 0.058453 0.029226 M5 1.82916558241524 2.978324 0.6142 0.542136 0.271068 M6 5.35126929941585 3.727656 1.4356 0.157893 0.078947 M7 -0.19740867179095 3.741121 -0.0528 0.958146 0.479073 M8 0.478414521457085 2.950818 0.1621 0.871914 0.435957 M9 6.48453488259889 3.811301 1.7014 0.09562 0.04781 M10 7.76983376921275 3.93192 1.9761 0.05416 0.02708 M11 3.73879347044081 3.167165 1.1805 0.243875 0.121938 t -0.158866937034356 0.036511 -4.3512 7.5e-05 3.7e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.872950470352862 R-squared 0.762042523689282 Adjusted R-squared 0.694793671688427 F-TEST (value) 11.3316807799127 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 2.42013076245939e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.58625403353776 Sum Squared Residuals 967.551398766505 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.9 100.845757468417 -0.945757468416905 2 98.6 101.185681755504 -2.58568175550370 3 107.2 109.531106702168 -2.33110670216788 4 95.7 100.901089650888 -5.20108965088764 5 93.7 99.777462938834 -6.07746293883393 6 106.7 105.657375858400 1.04262414159970 7 86.7 85.2009382250607 1.49906177493929 8 95.3 93.7361417167446 1.56385828325542 9 99.3 103.270618322127 -3.97061832212663 10 101.8 109.313347846739 -7.51334784673896 11 96 100.967998612988 -4.96799861298823 12 91.7 90.2811653638011 1.41883463619889 13 95.3 97.4176430698281 -2.11764306982815 14 96.6 96.7040750194076 -0.104075019407587 15 107.2 105.049499966072 2.15050003392825 16 108 102.916018996085 5.08398100391512 17 98.4 97.9295870465054 0.470412953494632 18 103.1 102.873062332732 0.226937667267826 19 81.1 86.2209025848346 -5.12090258483465 20 96.6 90.3080273181555 6.29197268184452 21 103.7 104.817328850654 -1.11732885065408 22 106.6 109.279819869006 -2.67981986900584 23 97.6 98.7104312560736 -1.11043125607363 24 87.6 89.1356176964772 -1.53561769647725 25 99.4 96.2135680504206 3.18643194957944 26 98.5 95.3244179437488 3.17558205625117 27 105.2 102.791932609157 2.40806739084287 28 104.6 100.775506343338 3.82449365666229 29 97.5 93.7406170708279 3.75938292917214 30 108.9 100.849604384152 8.05039561584756 31 86.8 85.1338822695945 1.66611773040549 32 88.9 87.1140223279013 1.78597767209873 33 110.3 104.608218816670 5.6917811833302 34 114.8 106.84667045584 7.95332954415995 35 94.6 94.989680097066 -0.389680097065915 36 92 90.0970547041674 1.90294529583255 37 93.8 92.9610357080826 0.838964291917363 38 93.8 93.4180146993366 0.381985300663441 39 107.6 105.158026066857 2.44197393314328 40 101 98.9861578030929 2.01384219690711 41 95.4 91.4245223618295 3.97547763817048 42 96.5 102.981588433517 -6.48158843351713 43 89.2 83.2274790251822 5.97252097481777 44 87.1 89.246006377266 -2.14600637726596 45 110.5 105.218491711858 5.28150828814235 46 110.8 104.120884282256 6.67911571774436 47 104.2 96.185226513091 8.014773486909 48 88.9 90.9414370076902 -2.04143700769019 49 89.8 90.7619957032517 -0.961995703251743 50 90 90.8678105820033 -0.867810582003323 51 93.9 98.5694346557465 -4.66943465574652 52 91.3 97.0212272065969 -5.72122720659689 53 87.8 89.9278105820033 -2.12781058200332 54 99.7 102.538368991198 -2.83836899119795 55 73.5 77.5167978953279 -4.01679789532790 56 79.2 86.6958022599327 -7.49580225993271 57 96.9 102.785342298692 -5.88534229869186 58 95.2 99.6392775461595 -4.43927754615951 59 95.6 97.1466635207812 -1.54666352078121 60 89.7 89.444725227864 0.255274772136011 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.123322465169263 0.246644930338525 0.876677534830737 18 0.113724859917882 0.227449719835765 0.886275140082118 19 0.55026139468553 0.89947721062894 0.44973860531447 20 0.452961411071614 0.905922822143229 0.547038588928386 21 0.355867334403742 0.711734668807485 0.644132665596258 22 0.403499040171852 0.806998080343703 0.596500959828149 23 0.403400869723936 0.806801739447872 0.596599130276064 24 0.46400935811095 0.9280187162219 0.53599064188905 25 0.374949392159977 0.749898784319954 0.625050607840023 26 0.288737633626289 0.577475267252577 0.711262366373711 27 0.212010971713462 0.424021943426925 0.787989028286538 28 0.143678289245282 0.287356578490564 0.856321710754718 29 0.117069000309621 0.234138000619241 0.88293099969038 30 0.211932291884765 0.423864583769531 0.788067708115235 31 0.251416452980742 0.502832905961484 0.748583547019258 32 0.33943850299007 0.67887700598014 0.66056149700993 33 0.309997673815272 0.619995347630544 0.690002326184728 34 0.413088099693492 0.826176199386984 0.586911900306508 35 0.393979700514626 0.787959401029252 0.606020299485374 36 0.326741096832956 0.653482193665912 0.673258903167044 37 0.301953185889429 0.603906371778858 0.698046814110571 38 0.305208583791402 0.610417167582805 0.694791416208598 39 0.259053112900152 0.518106225800303 0.740946887099848 40 0.195477704616283 0.390955409232567 0.804522295383717 41 0.120111110350102 0.240222220700204 0.879888889649898 42 0.378990696243442 0.757981392486884 0.621009303756558 43 0.243106061581106 0.486212123162212 0.756893938418894 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/10381h1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/10381h1258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/1ge3y1258984713.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/1ge3y1258984713.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/2krhg1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/2krhg1258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/39zc71258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/39zc71258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/4pbqm1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/4pbqm1258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/5bivu1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/5bivu1258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/64y581258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/64y581258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/72tbd1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/72tbd1258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/8olyc1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/8olyc1258984714.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/9yiqz1258984714.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t1258984761ifulxwhls059xuj/9yiqz1258984714.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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