WS73

*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: Sun, 22 Nov 2009 11:44:57 -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/22/t1258915556snxgpcn1o618rsf.htm/, Retrieved Sun, 22 Nov 2009 19:46:07 +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/22/t1258915556snxgpcn1o618rsf.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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104.89 124 105.15 118.63 105.24 121.86 105.57 119.97 105.62 125.03 106.17 130.09 106.27 126.65 106.41 121.7 106.94 119.24 107.16 122.63 107.32 116.66 107.32 114.12 107.35 113.11 107.55 112.61 107.87 113.4 108.37 115.18 108.38 121.01 107.92 119.44 108.03 116.68 108.14 117.07 108.3 117.41 108.64 119.58 108.66 120.92 109.04 117.09 109.03 116.77 109.03 119.39 109.54 122.49 109.75 124.08 109.83 118.29 109.65 112.94 109.82 113.79 109.95 114.43 110.12 118.7 110.15 120.36 110.21 118.27 109.99 118.34 110.14 117.82 110.14 117.65 110.81 118.18 110.97 121.02 110.99 124.78 109.73 131.16 109.81 130.14 110.02 131.75 110.18 134.73 110.21 135.35 110.25 140.32 110.36 136.35 110.51 131.6 110.6 128.9 110.95 133.89 111.18 138.25 111.19 146.23 111.69 144.76 111.7 149.3 111.83 156.8 111.77 159.08 111.73 165.12 112.01 163.14 111.86 153.43 112.04 151.01

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 AKW[t] = + 110.055899257576 -0.038009298269539AKB[t] -0.174770514728344M1[t] -0.285363474701875M2[t] + 0.0732185403524886M3[t] + 0.299697191177373M4[t] + 0.336207016778144M5[t] + 0.0638871977515323M6[t] + 0.0184703036138447M7[t] + 0.0764184642465924M8[t] + 0.199242753311017M9[t] + 0.295251074336222M10[t] + 0.25339064685611M11[t] + 0.125505490971034t + 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) 110.055899257576 0.745913 147.5452 0 0 AKB -0.038009298269539 0.006538 -5.8133 1e-06 0 M1 -0.174770514728344 0.273445 -0.6391 0.525832 0.262916 M2 -0.285363474701875 0.28765 -0.9921 0.326253 0.163126 M3 0.0732185403524886 0.286692 0.2554 0.799536 0.399768 M4 0.299697191177373 0.28629 1.0468 0.300532 0.150266 M5 0.336207016778144 0.286672 1.1728 0.246789 0.123394 M6 0.0638871977515323 0.286451 0.223 0.824479 0.412239 M7 0.0184703036138447 0.285883 0.0646 0.94876 0.47438 M8 0.0764184642465924 0.285887 0.2673 0.790405 0.395203 M9 0.199242753311017 0.28619 0.6962 0.489739 0.244869 M10 0.295251074336222 0.287662 1.0264 0.309965 0.154982 M11 0.25339064685611 0.286609 0.8841 0.381143 0.190571 t 0.125505490971034 0.004867 25.7864 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.978580216162726 R-squared 0.957619239465087 Adjusted R-squared 0.945896901444792 F-TEST (value) 81.6918295486061 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.450700805243576 Sum Squared Residuals 9.54716714481878 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 104.89 105.293481248395 -0.403481248395319 2 105.15 105.512503711100 -0.36250371110043 3 105.24 105.873821183715 -0.633821183715225 4 105.57 106.297642899241 -0.727642899240572 5 105.62 106.267331166569 -0.6473311665685 6 106.17 105.928189789269 0.241810210730943 7 106.27 106.139030372150 0.130969627850375 8 106.41 106.510630050188 -0.100630050187622 9 106.94 106.852462703966 0.0875372960338539 10 107.16 106.945124994829 0.214875005171350 11 107.32 107.255685568989 0.0643144310112762 12 107.32 107.224344030708 0.0956559692917247 13 107.35 107.213468398203 0.136531601796802 14 107.55 107.247385578335 0.302614421664536 15 107.87 107.701445738728 0.168554261272077 16 108.37 107.985773329604 0.384226670395938 17 108.38 107.926194437264 0.453805562735537 18 107.92 107.839054707492 0.0809452925079438 19 108.03 108.024048967549 0.00595103245067025 20 108.14 108.192678992828 -0.0526789928279927 21 108.3 108.428085611452 -0.128085611451811 22 108.64 108.567119246203 0.072880753796853 23 108.66 108.599831850013 0.0601681499871106 24 109.04 108.617522306500 0.422477693499861 25 109.03 108.580420258189 0.449579741810914 26 109.03 108.495748427720 0.534251572279602 27 109.54 108.862007109110 0.677992890889782 28 109.75 109.153556466658 0.596443533342424 29 109.83 109.53564562021 0.294354379789987 30 109.65 109.592181037896 0.0578189621035373 31 109.82 109.639961731201 0.180038268799287 32 109.95 109.799089431912 0.150910568088020 33 110.12 109.885119508337 0.234880491663495 34 110.15 110.043537885205 0.106462114794691 35 110.21 110.206622382080 0.0033776179204208 36 109.99 110.076076575316 -0.086076575315632 37 110.14 110.046576386658 0.0934236133415222 38 110.14 110.067950498362 0.072049501638196 39 110.81 110.531893076304 0.278106923695657 40 110.97 110.775930811015 0.194069188985225 41 110.99 110.795031166093 0.194968833906884 42 109.73 110.405717515078 -0.675717515077871 43 109.81 110.524575596146 -0.714575596146148 44 110.02 110.646834277536 -0.626834277535977 45 110.18 110.781896348728 -0.601896348728198 46 110.21 110.979844395797 -0.769844395797338 47 110.25 110.874583246889 -0.624583246888643 48 110.36 110.897595005134 -0.537595005133638 49 110.51 111.028874148157 -0.518874148156633 50 110.6 111.146411784482 -0.546411784481903 51 110.95 111.440832892142 -0.490832892142291 52 111.18 111.627096493483 -0.447096493483015 53 111.19 111.485797609864 -0.295797609863907 54 111.69 111.394856950265 0.295143049735447 55 111.7 111.302383332954 0.397616667045815 56 111.83 111.200767247536 0.629232752463572 57 111.77 111.362435827517 0.40756417248266 58 111.73 111.354373477966 0.375626522034444 59 112.01 111.513276952030 0.496723047969836 60 111.86 111.754462082342 0.105537917657683 61 112.04 111.797179560397 0.242820439602715 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0270735384923653 0.0541470769847305 0.972926461507635 18 0.106658770337142 0.213317540674284 0.893341229662858 19 0.101461526905173 0.202923053810346 0.898538473094827 20 0.282488812757886 0.564977625515771 0.717511187242114 21 0.509113652546352 0.981772694907297 0.490886347453648 22 0.488935370969272 0.977870741938545 0.511064629030728 23 0.415402622498876 0.830805244997753 0.584597377501124 24 0.311184242022723 0.622368484045446 0.688815757977277 25 0.228933604988870 0.457867209977740 0.77106639501113 26 0.160067267947015 0.320134535894031 0.839932732052985 27 0.130906054367787 0.261812108735575 0.869093945632213 28 0.0989791019990597 0.197958203998119 0.90102089800094 29 0.071211158540358 0.142422317080716 0.928788841459642 30 0.0666476776975794 0.133295355395159 0.93335232230242 31 0.0471173624441003 0.0942347248882007 0.9528826375559 32 0.0326833912183891 0.0653667824367782 0.967316608781611 33 0.0250975600139592 0.0501951200279184 0.974902439986041 34 0.0312857971026515 0.062571594205303 0.968714202897349 35 0.0527302061992754 0.105460412398551 0.947269793800725 36 0.0712653675498066 0.142530735099613 0.928734632450193 37 0.0564113892288552 0.112822778457710 0.943588610771145 38 0.0469377782199335 0.093875556439867 0.953062221780066 39 0.076581219998753 0.153162439997506 0.923418780001247 40 0.239417801675653 0.478835603351306 0.760582198324347 41 0.986655704387136 0.0266885912257284 0.0133442956128642 42 0.986875856465775 0.0262482870684507 0.0131241435342253 43 0.988648297933655 0.0227034041326907 0.0113517020663453 44 0.982761488493878 0.0344770230122443 0.0172385115061221 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 4 0.142857142857143 NOK 10% type I error level 10 0.357142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/10bg651258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/10bg651258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/138mz1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/138mz1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/2jvsx1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/2jvsx1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/3bq9o1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/3bq9o1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/47bw61258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/47bw61258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/5z69v1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/5z69v1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/6ak5l1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/6ak5l1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/7726t1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/7726t1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/8jevc1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/8jevc1258915493.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/914lc1258915493.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258915556snxgpcn1o618rsf/914lc1258915493.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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