review Ws 7 model 4 + seizoenaliteit

*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, 27 Nov 2009 03:16:15 -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/27/t1259317214u8g79a7mxiv65sp.htm/, Retrieved Fri, 27 Nov 2009 11:20:26 +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/27/t1259317214u8g79a7mxiv65sp.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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102 1 102.8 94 106.3 101.3 105.1 1 102 102.8 94 106.3 92.4 0 105.1 102 102.8 94 81.4 0 92.4 105.1 102 102.8 105.8 1 81.4 92.4 105.1 102 120.3 1 105.8 81.4 92.4 105.1 100.7 1 120.3 105.8 81.4 92.4 88.8 0 100.7 120.3 105.8 81.4 94.3 0 88.8 100.7 120.3 105.8 99.9 0 94.3 88.8 100.7 120.3 103.4 1 99.9 94.3 88.8 100.7 103.3 1 103.4 99.9 94.3 88.8 98.8 0 103.3 103.4 99.9 94.3 104.2 1 98.8 103.3 103.4 99.9 91.2 0 104.2 98.8 103.3 103.4 74.7 0 91.2 104.2 98.8 103.3 108.5 1 74.7 91.2 104.2 98.8 114.5 1 108.5 74.7 91.2 104.2 96.9 0 114.5 108.5 74.7 91.2 89.6 0 96.9 114.5 108.5 74.7 97.1 0 89.6 96.9 114.5 108.5 100.3 1 97.1 89.6 96.9 114.5 122.6 1 100.3 97.1 89.6 96.9 115.4 1 122.6 100.3 97.1 89.6 109 1 115.4 122.6 100.3 97.1 129.1 1 109 115.4 122.6 100.3 102.8 1 129.1 109 115.4 122.6 96.2 0 102.8 129.1 109 115.4 127.7 1 96.2 102.8 129.1 109 128.9 1 127.7 96.2 102.8 129.1 126.5 1 128.9 127.7 96.2 102.8 119.8 1 126.5 128.9 127.7 96.2 113.2 1 119.8 126.5 128.9 127.7 114.1 1 113.2 119.8 126. etc...

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 Omzet[t] = + 24.2719954836995 -3.31703241456063Uitvoer[t] + 0.347127616231981`Omzet-1`[t] + 0.355694576847724`Omzet-2`[t] + 0.307636566464671`Omzet-3`[t] -0.236660071043794`Omzet-4`[t] -4.91920455650283M1[t] + 6.34477868482915M2[t] -25.1185619148915M3[t] -26.1790950346311M4[t] + 8.80183878654852M5[t] + 22.3700847428009M6[t] -1.49099973052366M7[t] -27.9110842666772M8[t] -9.46201939302143M9[t] -1.42678907684248M10[t] + 20.2095761552773M11[t] + 0.356533729590830t + 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) 24.2719954836995 12.683334 1.9137 0.063214 0.031607 Uitvoer -3.31703241456063 4.600685 -0.721 0.47533 0.237665 `Omzet-1` 0.347127616231981 0.160029 2.1691 0.036395 0.018197 `Omzet-2` 0.355694576847724 0.155791 2.2831 0.028103 0.014052 `Omzet-3` 0.307636566464671 0.152078 2.0229 0.050162 0.025081 `Omzet-4` -0.236660071043794 0.148675 -1.5918 0.119716 0.059858 M1 -4.91920455650283 7.632115 -0.6445 0.523097 0.261549 M2 6.34477868482915 7.917077 0.8014 0.427881 0.21394 M3 -25.1185619148915 7.618837 -3.2969 0.002126 0.001063 M4 -26.1790950346311 10.452156 -2.5047 0.016665 0.008332 M5 8.80183878654852 10.674972 0.8245 0.414784 0.207392 M6 22.3700847428009 8.973643 2.4929 0.017146 0.008573 M7 -1.49099973052366 7.400757 -0.2015 0.841409 0.420704 M8 -27.9110842666772 8.188848 -3.4084 0.001559 0.00078 M9 -9.46201939302143 9.497802 -0.9962 0.325439 0.162719 M10 -1.42678907684248 8.915995 -0.16 0.873708 0.436854 M11 20.2095761552773 8.231792 2.4551 0.018775 0.009388 t 0.356533729590830 0.18995 1.877 0.068213 0.034107 Multiple Linear Regression - Regression Statistics Multiple R 0.951390734495854 R-squared 0.90514432968456 Adjusted R-squared 0.862708898227653 F-TEST (value) 21.3299193294106 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 2.20934381900406e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.0190822837749 Sum Squared Residuals 3814.516372744 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102 94.2404032330187 7.75959676698129 2 105.1 103.746100264482 1.35389973551846 3 92.4 82.365986416481 10.034013583519 4 81.4 76.0274016100569 5.37259838994313 5 105.8 100.855113254624 4.9448867453756 6 120.3 114.696535816866 5.60346418313407 7 100.7 104.525863854725 -3.82586385472518 8 88.8 90.2428085520879 -1.44280855208789 9 94.3 96.6321992962278 -2.33219929622776 10 99.9 93.239152033943 6.66084796605706 11 103.4 116.792915656183 -13.3929156561833 12 103.3 104.654965478633 -1.35496547863286 13 98.8 105.040679705087 -6.24067970508657 14 104.2 111.497952115501 -7.29795211550119 15 91.2 83.1229672864702 8.07703271352985 16 74.7 78.4663610582967 -3.76636105829667 17 108.5 102.861368806265 5.63863119373523 18 114.5 117.372861655084 -2.87286165508431 19 96.9 109.291163297658 -12.3911632976583 20 89.6 93.5553410252276 -3.95534102522758 21 97.1 97.4133924749686 -0.313392474968604 22 100.3 95.6606468208883 4.63935317911174 23 122.6 123.351533796078 -0.751533796077798 24 115.4 116.412552625382 -1.01255262538195 25 109 116.492038505162 -7.49203850516242 26 129.1 129.432920983719 -0.332920983718978 27 102.8 95.5344310452043 7.26556895479571 28 96.2 95.9025472434957 0.297452756504345 29 127.7 123.975292182100 3.7247078179005 30 128.9 133.639298446054 -4.73929844605406 31 126.5 125.949438542287 0.55056145771306 32 119.8 110.732123261511 9.06787673848912 33 113.2 119.272671493447 -6.0726714934468 34 114.1 121.967919762438 -7.867919762438 35 134.1 140.432468546754 -6.33246854675421 36 130 127.397324702197 2.60267529780310 37 121.8 130.364151564395 -8.56415156439549 38 132.1 143.619611582494 -11.5196115824944 39 105.3 107.177012286022 -1.87701228602162 40 103 99.2813333686565 3.71866663134348 41 117.1 129.397061959720 -12.2970619597197 42 126.3 136.715984794680 -10.4159847946798 43 138.1 127.055227454938 11.0447725450619 44 119.5 113.242166377464 6.25783362253559 45 138 129.281736735357 8.71826326464317 46 135.5 138.932281382731 -3.43228138273081 47 178.6 158.123082000985 20.4769179990153 48 162.2 162.435157193788 -0.235157193788282 49 176.9 162.362726992337 14.5372730076632 50 204.9 187.103415053804 17.7965849461961 51 132.2 155.699602965823 -23.4996029658230 52 142.5 148.122356719494 -5.62235671949428 53 164.3 166.311163797292 -2.01116379729165 54 174.9 162.475319287316 12.4246807126842 55 175.4 170.778306850391 4.62169314960861 56 143 152.927560783709 -9.92756078370925 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0291724116304499 0.0583448232608997 0.97082758836955 22 0.00680679627949363 0.0136135925589873 0.993193203720506 23 0.0701598337130306 0.140319667426061 0.929840166286969 24 0.0310909516865713 0.0621819033731427 0.968909048313429 25 0.0144778157974757 0.0289556315949514 0.985522184202524 26 0.00556785999809254 0.0111357199961851 0.994432140001907 27 0.00380019470168722 0.00760038940337443 0.996199805298313 28 0.00146813923991246 0.00293627847982491 0.998531860760087 29 0.000765481099785137 0.00153096219957027 0.999234518900215 30 0.00024668860453997 0.00049337720907994 0.99975331139546 31 0.000124909326817666 0.000249818653635333 0.999875090673182 32 0.000693347882583993 0.00138669576516799 0.999306652117416 33 0.000303866748665010 0.000607733497330021 0.999696133251335 34 0.00211640876208985 0.0042328175241797 0.99788359123791 35 0.000620810807895228 0.00124162161579046 0.999379189192105 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.6 NOK 5% type I error level 12 0.8 NOK 10% type I error level 14 0.933333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/10xhj81259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/10xhj81259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/1wc4a1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/1wc4a1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/2jjps1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/2jjps1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/3yu4k1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/3yu4k1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/42hmg1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/42hmg1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/59d6i1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/59d6i1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/6pviw1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/6pviw1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/7q01e1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/7q01e1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/8bleg1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/8bleg1259316971.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/9mchw1259316971.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259317214u8g79a7mxiv65sp/9mchw1259316971.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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