*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: Wed, 18 Nov 2009 11:29:56 -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/18/t1258569285jmga4mecu5i7ane.htm/, Retrieved Wed, 18 Nov 2009 19:34:56 +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/18/t1258569285jmga4mecu5i7ane.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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3030.29 101.2 2803.47 101.1 2767.63 100.7 2882.6 100.1 2863.36 99.9 2897.06 99.7 3012.61 99.5 3142.95 99.2 3032.93 99 3045.78 99 3110.52 99.3 3013.24 99.5 2987.1 99.7 2995.55 100 2833.18 100.4 2848.96 100.6 2794.83 100.7 2845.26 100.7 2915.02 100.6 2892.63 100.5 2604.42 100.6 2641.65 100.5 2659.81 100.4 2638.53 100.3 2720.25 100.4 2745.88 100.4 2735.7 100.4 2811.7 100.4 2799.43 100.4 2555.28 100.5 2304.98 100.6 2214.95 100.6 2065.81 100.5 1940.49 100.5 2042.00 100.7 1995.37 101.1 1946.81 101.5 1765.9 101.9 1635.25 102.1 1833.42 102.1 1910.43 102.1 1959.67 102.4 1969.6 102.8 2061.41 103.1 2093.48 103.1 2120.88 102.9 2174.56 102.4 2196.72 101.9 2350.44 101.3 2440.25 100.7 2408.64 100.6 2472.81 101 2407.6 101.5 2454.62 101.9 2448.05 102.1 2497.84 102.3 2645.64 102.5 2756.76 102.9 2849.27 103.6 2921.44 104.3

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 G.indx[t] = + 105.437532012466 -0.00157361441269152Bel20[t] -0.515153858096571M1[t] -0.604484801076168M2[t] -0.701136837488991M3[t] -0.553503480519101M4[t] -0.496742618165726M5[t] -0.396809349156366M6[t] -0.336205720407209M7[t] -0.266001126184697M8[t] -0.381661785517524M9[t] -0.341746121510495M10[t] -0.117698736543333M11[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) 105.437532012466 1.045403 100.8583 0 0 Bel20 -0.00157361441269152 0.000361 -4.3647 6.9e-05 3.5e-05 M1 -0.515153858096571 0.701168 -0.7347 0.466167 0.233084 M2 -0.604484801076168 0.700899 -0.8624 0.392824 0.196412 M3 -0.701136837488991 0.701447 -0.9996 0.322643 0.161322 M4 -0.553503480519101 0.700925 -0.7897 0.433683 0.216841 M5 -0.496742618165726 0.700899 -0.7087 0.481997 0.240999 M6 -0.396809349156366 0.700909 -0.5661 0.573995 0.286998 M7 -0.336205720407209 0.700947 -0.4796 0.633705 0.316852 M8 -0.266001126184697 0.700906 -0.3795 0.706018 0.353009 M9 -0.381661785517524 0.701285 -0.5442 0.588855 0.294427 M10 -0.341746121510495 0.701148 -0.4874 0.628234 0.314117 M11 -0.117698736543333 0.700917 -0.1679 0.867366 0.433683 Multiple Linear Regression - Regression Statistics Multiple R 0.554033310974916 R-squared 0.306952909669829 Adjusted R-squared 0.13000471639404 F-TEST (value) 1.73470496639328 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0892788781851681 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.10821739728157 Sum Squared Residuals 57.7228525829639 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.2 100.153870135735 1.04612986426497 2 101.1 100.421466413842 0.678533586158247 3 100.7 100.381212717980 0.318787282020214 4 100.1 100.347927625923 -0.247927625922541 5 99.9 100.434964829576 -0.53496482957609 6 99.7 100.481867292878 -0.781867292877748 7 99.5 100.360639776240 -0.860639776240403 8 99.2 100.225739467913 -1.0257394679127 9 99 100.283207866264 -1.28320786626420 10 99 100.302902585068 -1.30290258506814 11 99.3 100.425074172958 -1.12507417295766 12 99.5 100.695854119568 -1.19585411956762 13 99.7 100.221834542219 -0.521834542218798 14 100 100.119206557452 -0.119206557451961 15 100.4 100.278062293228 0.121937706772146 16 100.6 100.400864014765 0.199135985234517 17 100.7 100.542804625278 0.157195374722158 18 100.7 100.563380519455 0.136619480544832 19 100.6 100.514208806775 0.0857911932250263 20 100.5 100.619646627698 -0.119646627697643 21 100.6 100.957517378247 -0.357517378246644 22 100.5 100.938847377669 -0.438847377669162 23 100.4 101.134317924902 -0.73431792490184 24 100.3 101.285503176147 -0.985503176147257 25 100.4 100.641753548246 -0.241753548245527 26 100.4 100.512090867869 -0.112090867868647 27 100.4 100.431458226177 -0.0314582261770233 28 100.4 100.459496887782 -0.059496887782358 29 100.4 100.535565998979 -0.135565998979459 30 100.5 101.019697226847 -0.519697226847457 31 100.6 101.474176543093 -0.874176543093307 32 100.6 101.686053642890 -1.08605364289044 33 100.5 101.805081837066 -1.30508183706642 34 100.5 102.042202859272 -1.54220285927195 35 100.7 102.106512645207 -1.40651264520679 36 101.1 102.297589021814 -1.19758902181394 37 101.5 101.858849879598 -0.35884987959766 38 101.9 102.054201520018 -0.154201520018080 39 102.1 102.163142206623 -0.063142206623415 40 102.1 101.998932395430 0.101067604569773 41 102.1 101.934509211862 0.165490788137771 42 102.4 101.956957707191 0.443042292809353 43 102.8 102.001935344822 0.798064655178214 44 103.1 101.927666399815 1.17233360018491 45 103.1 101.761539926267 1.33846007373275 46 102.9 101.758338555367 1.14166144463348 47 102.4 101.897914318660 0.502085681339601 48 101.9 101.980741759818 -0.080741759818489 49 101.3 101.223691894203 0.0763081057970145 50 100.7 100.993034640820 -0.293034640819558 51 100.6 100.946124555992 -0.346124555991922 52 101 100.992779076099 0.00722092390060805 53 101.5 101.152155334304 0.347844665695618 54 101.9 101.178097253629 0.72190274637102 55 102.1 101.249039529070 0.850960470930469 56 102.3 101.240893861684 1.05910613831587 57 102.5 100.892652992155 1.60734700784451 58 102.9 100.757708622624 2.14229137737577 59 103.6 100.836180938273 2.76381906172668 60 104.3 100.840311922653 3.4596880773473 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.232498608284054 0.464997216568107 0.767501391715946 17 0.123574232758145 0.24714846551629 0.876425767241855 18 0.0739531875710039 0.147906375142008 0.926046812428996 19 0.0382036360329733 0.0764072720659465 0.961796363967027 20 0.0173577576636855 0.0347155153273710 0.982642242336314 21 0.00954354443429716 0.0190870888685943 0.990456455565703 22 0.00450186274392769 0.00900372548785538 0.995498137256072 23 0.00321357627791838 0.00642715255583676 0.996786423722082 24 0.00235999142329884 0.00471998284659769 0.9976400085767 25 0.00268822412462091 0.00537644824924182 0.99731177587538 26 0.00164264513468144 0.00328529026936287 0.998357354865319 27 0.000800820598507124 0.00160164119701425 0.999199179401493 28 0.000375946731611483 0.000751893463222967 0.999624053268388 29 0.000203834697789756 0.000407669395579513 0.99979616530221 30 0.000186998839084351 0.000373997678168701 0.999813001160916 31 0.000359797753848312 0.000719595507696625 0.999640202246152 32 0.000547311875529337 0.00109462375105867 0.99945268812447 33 0.000817849716602096 0.00163569943320419 0.999182150283398 34 0.00180325754245384 0.00360651508490768 0.998196742457546 35 0.00524068447947113 0.0104813689589423 0.994759315520529 36 0.019512887414838 0.039025774829676 0.980487112585162 37 0.0103369668577913 0.0206739337155825 0.989663033142209 38 0.0075803654624615 0.015160730924923 0.992419634537538 39 0.00981488567777247 0.0196297713555449 0.990185114322228 40 0.0122200279059209 0.0244400558118417 0.98777997209408 41 0.0104075370184958 0.0208150740369916 0.989592462981504 42 0.0123941579403414 0.0247883158806828 0.987605842059659 43 0.029128979113376 0.058257958226752 0.970871020886624 44 0.0922733332263168 0.184546666452634 0.907726666773683 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.448275862068966 NOK 5% type I error level 23 0.793103448275862 NOK 10% type I error level 25 0.862068965517241 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/10xfe91258568992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/10xfe91258568992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/1u1p71258568991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/1u1p71258568991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/2zh0b1258568991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/2zh0b1258568991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/39wym1258568991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/39wym1258568991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/4ybb71258568991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/4ybb71258568991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/5euv11258568991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/5euv11258568991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/6rz061258568991.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/6rz061258568991.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/7wllz1258568992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/7wllz1258568992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/84nim1258568992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/84nim1258568992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/9vx3n1258568992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258569285jmga4mecu5i7ane/9vx3n1258568992.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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