Workshop 7

*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 02:11:16 -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/t12587083662ruk95ekfo6vgf7.htm/, Retrieved Fri, 20 Nov 2009 10:12:58 +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/t12587083662ruk95ekfo6vgf7.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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8,9 8,6 8,9 8,5 8,9 8,3 8,9 7,8 9 7,8 9 8 9 8,6 9 8,9 9 8,9 9 8,6 9 8,3 9,1 8,3 9 8,3 9,1 8,4 9,1 8,5 9 8,4 9 8,6 9 8,5 9 8,5 8,9 8,4 8,9 8,5 8,9 8,5 8,9 8,5 8,8 8,5 8,8 8,5 8,7 8,5 8,7 8,5 8,5 8,5 8,5 8,6 8,4 8,4 8,2 8,1 8,2 8 8,1 8 8,1 8 8 8 7,9 7,9 7,8 7,8 7,7 7,8 7,6 7,9 7,5 8,1 7,5 8 7,5 7,6 7,5 7,3 7,5 7 7,4 6,8 7,4 7 7,3 7,1 7,3 7,2 7,3 7,1 7,2 6,9 7,2 6,7 7,3 6,7 7,4 6,6 7,4 6,9 7,5 7,3 7,6 7,5 7,7 7,3 7,9 7,1 8 6,9 8,2 7,1

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] = + 1.29280924113589 + 0.893229584469757X[t] -0.132239691962144M1[t] -0.136510508583347M2[t] -0.120781325204556M3[t] -0.109322958446975M4[t] -0.0871875501363702M5[t] -0.0714583667575799M6[t] -0.162916733515161M7[t] -0.162916733515161M8[t] -0.129322958446975M9[t] -0.0357291833787896M10[t] + 0.0157291833787906M11[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) 1.29280924113589 0.653735 1.9776 0.053857 0.026929 X 0.893229584469757 0.080575 11.0857 0 0 M1 -0.132239691962144 0.255315 -0.5179 0.606925 0.303463 M2 -0.136510508583347 0.255071 -0.5352 0.595044 0.297522 M3 -0.120781325204556 0.254867 -0.4739 0.637766 0.318883 M4 -0.109322958446975 0.254582 -0.4294 0.669579 0.334789 M5 -0.0871875501363702 0.254638 -0.3424 0.733578 0.366789 M6 -0.0714583667575799 0.254536 -0.2807 0.780142 0.390071 M7 -0.162916733515161 0.254781 -0.6394 0.525642 0.262821 M8 -0.162916733515161 0.254781 -0.6394 0.525642 0.262821 M9 -0.129322958446975 0.254582 -0.508 0.613842 0.306921 M10 -0.0357291833787896 0.254475 -0.1404 0.888941 0.444471 M11 0.0157291833787906 0.254475 0.0618 0.950976 0.475488 Multiple Linear Regression - Regression Statistics Multiple R 0.851031331526163 R-squared 0.724254327239195 Adjusted R-squared 0.653851176747074 F-TEST (value) 10.2872431443285 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.65884173064512e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.40232772588807 Sum Squared Residuals 7.60777715385849 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.8423439756137 0.0576560243863022 2 8.9 8.74875020054548 0.151249799454517 3 8.9 8.58583346703032 0.314166532969678 4 8.9 8.15067704155302 0.749322958446976 5 9 8.17281244986363 0.82718755013637 6 9 8.36718755013637 0.632812449863629 7 9 8.81166693406064 0.188333065939356 8 9 9.07963580940157 -0.0796358094015717 9 9 9.11322958446976 -0.113229584469757 10 9 8.93885448419702 0.0611455158029848 11 9 8.72234397561367 0.277656024386331 12 9.1 8.70661479223488 0.393385207765121 13 9 8.57437510027274 0.425624899727265 14 9.1 8.6594272420985 0.440572757901492 15 9.1 8.76447938392427 0.335520616075726 16 9 8.68661479223488 0.313385207765121 17 9 8.88739611743943 0.112603882560565 18 9 8.81380234237125 0.186197657628751 19 9 8.72234397561367 0.277656024386331 20 8.9 8.63302101716669 0.266978982833307 21 8.9 8.75593775068185 0.144062249318146 22 8.9 8.84953152575004 0.0504684742499605 23 8.9 8.90098989250762 -0.000989892507619604 24 8.8 8.88526070912883 -0.0852607091288288 25 8.8 8.75302101716669 0.0469789828333149 26 8.7 8.74875020054548 -0.0487502005454838 27 8.7 8.76447938392427 -0.0644793839242739 28 8.5 8.77593775068185 -0.275937750681854 29 8.5 8.88739611743943 -0.387396117439435 30 8.4 8.72447938392427 -0.324479383924274 31 8.2 8.36505214182577 -0.165052141825766 32 8.2 8.27572918337879 -0.0757291833787907 33 8.1 8.30932295844697 -0.209322958446976 34 8.1 8.40291673351516 -0.302916733515162 35 8 8.45437510027274 -0.454375100272742 36 7.9 8.34932295844698 -0.449322958446975 37 7.8 8.12776030803786 -0.327760308037856 38 7.7 8.12348949141665 -0.423489491416653 39 7.6 8.22854163324242 -0.62854163324242 40 7.5 8.41864591689395 -0.918645916893951 41 7.5 8.35145836675758 -0.85145836675758 42 7.5 8.00989571634847 -0.509895716348468 43 7.5 7.65046847424996 -0.150468474249960 44 7.5 7.38249959890903 0.117500401090967 45 7.4 7.23744745708327 0.162552542916733 46 7.4 7.5096871490454 -0.109687149045404 47 7.3 7.65046847424996 -0.35046847424996 48 7.3 7.72406224931815 -0.424062249318146 49 7.3 7.50249959890903 -0.202499598909026 50 7.2 7.31958286539387 -0.119582865393872 51 7.2 7.15666613187871 0.0433338681212891 52 7.3 7.16812449863629 0.131875501363708 53 7.4 7.10093694849992 0.299063051500079 54 7.4 7.38463500721964 0.0153649927803616 55 7.5 7.65046847424996 -0.150468474249960 56 7.6 7.82911439114391 -0.229114391143912 57 7.7 7.68406224931815 0.0159377506818545 58 7.9 7.59901010749238 0.300989892507620 59 8 7.47182255735601 0.528177442643991 60 8.2 7.63473929087117 0.56526070912883 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0591436358642762 0.118287271728552 0.940856364135724 17 0.0195706844427628 0.0391413688855256 0.980429315557237 18 0.00613296986511946 0.0122659397302389 0.99386703013488 19 0.00188243651159189 0.00376487302318378 0.998117563488408 20 0.000645844864909 0.001291689729818 0.99935415513509 21 0.000203887873636862 0.000407775747273725 0.999796112126363 22 7.00172062510889e-05 0.000140034412502178 0.999929982793749 23 2.97533403541544e-05 5.95066807083089e-05 0.999970246659646 24 0.000118775122735336 0.000237550245470672 0.999881224877265 25 0.000106980508553051 0.000213961017106103 0.999893019491447 26 0.000450393979264859 0.000900787958529717 0.999549606020735 27 0.00159308907565576 0.00318617815131152 0.998406910924344 28 0.00969314577197446 0.0193862915439489 0.990306854228026 29 0.0296907195928844 0.0593814391857687 0.970309280407116 30 0.112075304837800 0.224150609675601 0.8879246951622 31 0.4255721336463 0.8511442672926 0.5744278663537 32 0.604256005221389 0.791487989557221 0.395743994778611 33 0.678673469658677 0.642653060682645 0.321326530341323 34 0.699214375969072 0.601571248061857 0.300785624030928 35 0.72071741891226 0.558565162175479 0.279282581087740 36 0.717305513531532 0.565388972936936 0.282694486468468 37 0.720670528792788 0.558658942414424 0.279329471207212 38 0.716003976645226 0.567992046709547 0.283996023354774 39 0.705654698786697 0.588690602426605 0.294345301213303 40 0.719891051662843 0.560217896674314 0.280108948337157 41 0.670628359298518 0.658743281402964 0.329371640701482 42 0.552033319699904 0.895933360600193 0.447966680300096 43 0.399473384974539 0.798946769949079 0.600526615025461 44 0.260253603819089 0.520507207638177 0.739746396180911 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.310344827586207 NOK 5% type I error level 12 0.413793103448276 NOK 10% type I error level 13 0.448275862068966 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/107vhr1258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/107vhr1258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/1nrcd1258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/1nrcd1258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/2usj61258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/2usj61258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/3prj31258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/3prj31258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/4uq3b1258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/4uq3b1258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/5px961258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/5px961258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/62dp41258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/62dp41258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/7omhh1258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/7omhh1258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/8vn511258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/8vn511258708272.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/9grhl1258708272.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587083662ruk95ekfo6vgf7/9grhl1258708272.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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