WS 7 3

*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: Sat, 14 Nov 2009 06:31:17 -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/14/t125820551811zczdon9xblt8v.htm/, Retrieved Sat, 14 Nov 2009 14:32:09 +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/14/t125820551811zczdon9xblt8v.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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103,63 100,30 103,64 98,50 103,66 95,10 103,77 93,10 103,88 92,20 103,91 89,00 103,91 86,40 103,92 84,50 104,05 82,70 104,23 80,80 104,30 81,80 104,31 81,80 104,31 82,90 104,34 83,80 104,55 86,20 104,65 86,10 104,73 86,20 104,75 88,80 104,75 89,60 104,76 87,80 104,94 88,30 105,29 88,60 105,38 91,00 105,43 91,50 105,43 95,40 105,42 98,70 105,52 99,90 105,69 98,60 105,72 100,30 105,74 100,20 105,74 100,40 105,74 101,40 105,95 103,00 106,17 109,10 106,34 111,40 106,37 114,10 106,37 121,80 106,36 127,60 106,44 129,90 106,29 128,00 106,23 123,50 106,23 124,00 106,23 127,40 106,23 127,60 106,34 128,40 106,44 131,40 106,44 135,10 106,48 134,00 106,50 144,50 106,57 147,30 106,40 150,90 106,37 148,70 106,25 141,40 106,21 138,90 106,21 139,80 106,24 145,60 106,19 147,90 106,08 148,50 106,13 151,10 106,09 157,50

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] = + 105.384112125121 -0.0218733363637488X[t] + 0.244600489110058M1[t] + 0.230600085802714M2[t] + 0.225163812858891M3[t] + 0.152232065005675M4[t] + 0.0324264484251042M5[t] -0.0535068965189192M6[t] -0.121817038190091M7[t] -0.177502379497611M8[t] -0.126750254077858M9[t] -0.0234371924761798M10[t] + 0.0249370714892171M11[t] + 0.0801217433075963t + 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) 105.384112125121 0.428976 245.6646 0 0 X -0.0218733363637488 0.005622 -3.8909 0.00032 0.00016 M1 0.244600489110058 0.229863 1.0641 0.292831 0.146416 M2 0.230600085802714 0.230516 1.0004 0.322366 0.161183 M3 0.225163812858891 0.230118 0.9785 0.332959 0.166479 M4 0.152232065005675 0.227147 0.6702 0.506086 0.253043 M5 0.0324264484251042 0.225045 0.1441 0.88606 0.44303 M6 -0.0535068965189192 0.224536 -0.2383 0.812707 0.406353 M7 -0.121817038190091 0.224374 -0.5429 0.589807 0.294903 M8 -0.177502379497611 0.224306 -0.7913 0.432808 0.216404 M9 -0.126750254077858 0.224325 -0.565 0.574798 0.287399 M10 -0.0234371924761798 0.224171 -0.1046 0.917187 0.458593 M11 0.0249370714892171 0.223977 0.1113 0.911833 0.455916 t 0.0801217433075963 0.007817 10.2496 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.946332492074222 R-squared 0.895545185555408 Adjusted R-squared 0.866025346690632 F-TEST (value) 30.3370621248209 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.354095200960798 Sum Squared Residuals 5.76763692179953 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.63 103.514938720255 0.115061279744937 2 103.64 103.62043206571 0.0195679342899749 3 103.66 103.769486879711 -0.109486879710544 4 103.77 103.820423547892 -0.0504235478924228 5 103.88 103.800425677347 0.0795743226531766 6 103.91 103.864608752074 0.0453912479256085 7 103.91 103.933291028257 -0.0232910282565623 8 103.92 103.999286769348 -0.0792867693477565 9 104.05 104.169532643530 -0.119532643529858 10 104.23 104.394526787530 -0.164526787530249 11 104.3 104.501149458439 -0.201149458439500 12 104.31 104.556334130258 -0.246334130257874 13 104.31 104.856995692675 -0.546995692675405 14 104.34 104.903431029948 -0.563431029948282 15 104.55 104.925620493039 -0.375620493039064 16 104.65 104.934997822130 -0.284997822129811 17 104.73 104.893126615220 -0.163126615220464 18 104.75 104.830444339038 -0.0804443390382939 19 104.75 104.824757271584 -0.0747572715837193 20 104.76 104.888565679039 -0.128565679038538 21 104.94 105.008502879584 -0.0685028795840206 22 105.29 105.185375683584 0.104624316415838 23 105.38 105.261375683584 0.118624316415831 24 105.43 105.305623687221 0.124376312779338 25 105.43 105.545039907820 -0.115039907819696 26 105.42 105.538979237820 -0.118979237819582 27 105.52 105.587416704547 -0.0674167045468627 28 105.69 105.623042037274 0.0669579627258855 29 105.72 105.546173492183 0.173826507817234 30 105.74 105.542549224183 0.197450775817281 31 105.74 105.549986158546 0.190013841453607 32 105.74 105.552549224183 0.187450775817279 33 105.95 105.648425754728 0.301574245271936 34 106.17 105.698433207818 0.471566792181528 35 106.34 105.776620541455 0.563379458545159 36 106.37 105.772747205091 0.597252794908903 37 106.37 105.929044747508 0.440955252492114 38 106.36 105.868300736598 0.4916992634016 39 106.44 105.892677533326 0.547322466674447 40 106.29 105.941426867871 0.348573132128953 41 106.23 106.000173008235 0.229826991765055 42 106.23 105.983424738417 0.246575261583356 43 106.23 105.920866996416 0.309133003583678 44 106.23 105.940928731144 0.289071268856351 45 106.34 106.05430393078 0.285696069220001 46 106.44 106.172118726598 0.267881273401967 47 106.44 106.219683389325 0.220316610674844 48 106.48 106.298928731144 0.181071268856347 49 106.5 106.393980931742 0.106019068258051 50 106.57 106.398856929924 0.171143070076289 51 106.4 106.394798389378 0.00520161062202413 52 106.37 106.450109724833 -0.0801097248326048 53 106.25 106.570101207015 -0.320101207015001 54 106.21 106.618972946288 -0.408972946287952 55 106.21 106.611098545197 -0.401098545197003 56 106.24 106.508669596287 -0.268669596287335 57 106.19 106.589234791378 -0.399234791378059 58 106.08 106.759545594469 -0.679545594469084 59 106.13 106.831170927196 -0.701170927196333 60 106.09 106.746366246287 -0.656366246286713 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000926487593250345 0.00185297518650069 0.99907351240675 18 0.000718944203333576 0.00143788840666715 0.999281055796666 19 0.000267722590581001 0.000535445181162002 0.99973227740942 20 9.2853734916939e-05 0.000185707469833878 0.999907146265083 21 4.0045636405382e-05 8.0091272810764e-05 0.999959954363595 22 7.96863741772666e-05 0.000159372748354533 0.999920313625823 23 9.244198643709e-05 0.00018488397287418 0.999907558013563 24 0.000104825745843488 0.000209651491686976 0.999895174254157 25 6.00862444275754e-05 0.000120172488855151 0.999939913755572 26 4.11709076914053e-05 8.23418153828106e-05 0.999958829092309 27 2.36608871637106e-05 4.73217743274212e-05 0.999976339112836 28 7.06042901135717e-06 1.41208580227143e-05 0.999992939570989 29 7.48143811504224e-06 1.49628762300845e-05 0.999992518561885 30 1.12455887008689e-05 2.24911774017378e-05 0.9999887544113 31 1.95369197468025e-05 3.9073839493605e-05 0.999980463080253 32 9.78413309471307e-05 0.000195682661894261 0.999902158669053 33 0.000190334082785605 0.000380668165571209 0.999809665917214 34 0.00048622725510548 0.00097245451021096 0.999513772744895 35 0.000199870390410695 0.000399740780821389 0.99980012960959 36 7.96908023918756e-05 0.000159381604783751 0.999920309197608 37 0.000101429669203049 0.000202859338406098 0.999898570330797 38 0.000906373186333997 0.00181274637266799 0.999093626813666 39 0.00122897425681743 0.00245794851363486 0.998771025743183 40 0.103568557549696 0.207137115099392 0.896431442450304 41 0.386161505743746 0.772323011487491 0.613838494256254 42 0.454878388647269 0.909756777294538 0.545121611352731 43 0.410918967735617 0.821837935471234 0.589081032264383 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.851851851851852 NOK 5% type I error level 23 0.851851851851852 NOK 10% type I error level 23 0.851851851851852 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/10s4ow1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/10s4ow1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/1jixy1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/1jixy1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/2avat1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/2avat1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/3q6ga1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/3q6ga1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/4xul11258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/4xul11258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/5iram1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/5iram1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/61zhg1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/61zhg1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/7zteu1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/7zteu1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/8d3kh1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/8d3kh1258205473.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/91bxp1258205473.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/14/t125820551811zczdon9xblt8v/91bxp1258205473.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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