SHW - WS7

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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 07:58:47 -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/t1258556378418rdhsm190kydm.htm/, Retrieved Wed, 18 Nov 2009 15:59:50 +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/t1258556378418rdhsm190kydm.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)

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Dataseries X:

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8 2.26 7.8 7.8 8.3 8.5 8.6 8.6 2.41 8 7.8 7.8 8.3 8.5 8.9 2.26 8.6 8 7.8 7.8 8.3 8.9 2.03 8.9 8.6 8 7.8 7.8 8.6 2.86 8.9 8.9 8.6 8 7.8 8.3 2.55 8.6 8.9 8.9 8.6 8 8.3 2.27 8.3 8.6 8.9 8.9 8.6 8.3 2.26 8.3 8.3 8.6 8.9 8.9 8.4 2.57 8.3 8.3 8.3 8.6 8.9 8.5 3.07 8.4 8.3 8.3 8.3 8.6 8.4 2.76 8.5 8.4 8.3 8.3 8.3 8.6 2.51 8.4 8.5 8.4 8.3 8.3 8.5 2.87 8.6 8.4 8.5 8.4 8.3 8.5 3.14 8.5 8.6 8.4 8.5 8.4 8.5 3.11 8.5 8.5 8.6 8.4 8.5 8.5 3.16 8.5 8.5 8.5 8.6 8.4 8.5 2.47 8.5 8.5 8.5 8.5 8.6 8.5 2.57 8.5 8.5 8.5 8.5 8.5 8.5 2.89 8.5 8.5 8.5 8.5 8.5 8.5 2.63 8.5 8.5 8.5 8.5 8.5 8.5 2.38 8.5 8.5 8.5 8.5 8.5 8.5 1.69 8.5 8.5 8.5 8.5 8.5 8.5 1.96 8.5 8.5 8.5 8.5 8.5 8.6 2.19 8.5 8.5 8.5 8.5 8.5 8.4 1.87 8.6 8.5 8.5 8.5 8.5 8.1 1.6 8.4 8.6 8.5 8.5 8.5 8 1.63 8.1 8.4 8.6 8.5 8.5 8 1.22 8 8.1 8.4 8.6 8.5 8 1.21 8 8 8.1 8.4 8.6 8 1.49 8 8 8 8.1 8.4 7.9 1.64 8 8 8 8 8.1 7.8 1.66 7.9 8 8 8 8 7.8 1.77 7.8 7.9 8 8 8 7.9 1.82 7.8 7.8 7.9 8 8 8.1 1.78 7.9 7.8 7.8 7.9 8 8 1.28 8.1 7.9 7.8 7.8 7.9 7.6 1.2 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.503509380194537 + 0.0414818132820681X[t] + 1.33626413393048Y1[t] -0.492087963870134Y2[t] -0.305971990782305Y3[t] + 0.315642153418853Y4[t] + 0.0855267462042557Y5[t] -0.0999994484620697M1[t] + 0.0366515842356858M2[t] -0.0228980548083576M3[t] -0.0962768815913776M4[t] -0.000526169999079095M5[t] -0.0440976932868833M6[t] + 0.0416662738636087M7[t] -0.0730109866690508M8[t] -0.0333481723841644M9[t] -0.00834119278490148M10[t] + 0.00126336559598207M11[t] -0.00461047464397417t + 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) 0.503509380194537 0.757902 0.6643 0.510702 0.255351 X 0.0414818132820681 0.02334 1.7773 0.083973 0.041986 Y1 1.33626413393048 0.16749 7.9782 0 0 Y2 -0.492087963870134 0.269045 -1.829 0.07569 0.037845 Y3 -0.305971990782305 0.263844 -1.1597 0.253817 0.126909 Y4 0.315642153418853 0.24996 1.2628 0.21479 0.107395 Y5 0.0855267462042557 0.148674 0.5753 0.568692 0.284346 M1 -0.0999994484620697 0.092342 -1.0829 0.286041 0.143021 M2 0.0366515842356858 0.09362 0.3915 0.69774 0.34887 M3 -0.0228980548083576 0.093056 -0.2461 0.807028 0.403514 M4 -0.0962768815913776 0.093396 -1.0308 0.309491 0.154746 M5 -0.000526169999079095 0.093827 -0.0056 0.995557 0.497778 M6 -0.0440976932868833 0.093047 -0.4739 0.638413 0.319207 M7 0.0416662738636087 0.092734 0.4493 0.655902 0.327951 M8 -0.0730109866690508 0.096713 -0.7549 0.455207 0.227604 M9 -0.0333481723841644 0.097323 -0.3427 0.733852 0.366926 M10 -0.00834119278490148 0.096862 -0.0861 0.931853 0.465926 M11 0.00126336559598207 0.096648 0.0131 0.989643 0.494821 t -0.00461047464397417 0.002676 -1.7232 0.093434 0.046717 Multiple Linear Regression - Regression Statistics Multiple R 0.986273403292289 R-squared 0.972735226041754 Adjusted R-squared 0.959102839062631 F-TEST (value) 71.3547251505868 F-TEST (DF numerator) 18 F-TEST (DF denominator) 36 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.136065730897524 Sum Squared Residuals 0.666499792488392 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.95614327950036 0.0438567204996425 2 8.6 8.4429638264195 0.157036173580505 3 8.9 8.90099590237315 -0.000995902373146965 4 8.9 8.81513447448975 0.0848655255102492 5 8.6 8.67262346351554 -0.0726234635155403 6 8.3 8.32540190734465 -0.0254019073446474 7 8.3 8.2876963348623 0.0123036651377047 8 8.3 8.43306979180985 -0.133069791809849 9 8.4 8.47808044477724 -0.0780804447772375 10 8.5 8.53249359987968 -0.032493599879675 11 8.4 8.5833879146439 -0.183387914643900 12 8.6 8.35371121222514 0.246288787774863 13 8.5 8.5814633813374 -0.0814633813373996 14 8.5 8.5633741118508 -0.0633741118508071 15 8.5 8.46297240127342 0.0370275987265798 16 8.5 8.4722301456521 0.027769854347895 17 8.5 8.52028906533477 -0.0202890653347681 18 8.5 8.46770257411077 0.0322974258892291 19 8.5 8.56213024686755 -0.0621302468675505 20 8.5 8.43205724023758 0.067942759762421 21 8.5 8.45673912655797 0.0432608734420258 22 8.5 8.44851318034864 0.0514868196513640 23 8.5 8.4647073536717 0.0352926463282963 24 8.6 8.46837433048662 0.131625669513376 25 8.4 8.48411664052336 -0.084116640523364 26 8.1 8.28849548581788 -0.188495485817880 27 8 7.89252098004498 0.107479019955023 28 8 7.90428272443867 0.0957172755613254 29 8 8.08143278081254 -0.0814327808125382 30 8 7.96366489441146 0.0363351055885383 31 7.9 7.99381841970713 -0.0938184197071276 32 7.8 7.73318123278266 0.0668187672173368 33 7.8 7.68837895487857 0.111621045121432 34 7.9 7.7906555459632 0.109344454036796 35 8.1 7.92664975429822 0.173350245701775 36 8 8.077962147854 -0.0779621478540032 37 7.6 7.70257316301505 -0.102573163015051 38 7.3 7.32300522613164 -0.0230052261316447 39 7 7.14670890887445 -0.146708908874448 40 6.8 6.93957459382125 -0.139574593821246 41 7 6.89835217808715 0.101647821912850 42 7.1 7.20776612174027 -0.107766121740274 43 7.2 7.27119443512765 -0.0711944351276502 44 7.1 7.10169173516991 -0.00169173516990879 45 6.9 6.97680147378622 -0.0768014737862203 46 6.7 6.82833767380849 -0.128337673808485 47 6.7 6.72525497738617 -0.0252549773861713 48 6.6 6.89995230943424 -0.299952309434236 49 6.9 6.67570353562383 0.224296464376172 50 7.3 7.18216134978017 0.117838650219827 51 7.5 7.49680180743401 0.00319819256599207 52 7.3 7.36877806159822 -0.0687780615982234 53 7.1 7.02730251225 0.0726974877499958 54 6.9 6.83546450239285 0.0645354976071543 55 7.1 6.88516056343538 0.214839436564624 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 22 0.136683515958010 0.273367031916019 0.86331648404199 23 0.073066607496475 0.14613321499295 0.926933392503525 24 0.093872651830149 0.187745303660298 0.906127348169851 25 0.0640604554888204 0.128120910977641 0.93593954451118 26 0.270164154388744 0.540328308777488 0.729835845611256 27 0.220251259145818 0.440502518291637 0.779748740854182 28 0.362831742185525 0.725663484371049 0.637168257814476 29 0.530040532009086 0.93991893598183 0.469959467990914 30 0.38915417685945 0.7783083537189 0.61084582314055 31 0.311253242373927 0.622506484747854 0.688746757626073 32 0.196556470187695 0.39311294037539 0.803443529812305 33 0.10688308605022 0.21376617210044 0.89311691394978 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/10dhqs1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/10dhqs1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/1501a1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/1501a1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/2lvn21258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/2lvn21258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/3pdmc1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/3pdmc1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/4qfja1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/4qfja1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/519fw1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/519fw1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/6uav61258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/6uav61258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/741nq1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/741nq1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/83b3u1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/83b3u1258556322.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/9meag1258556322.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258556378418rdhsm190kydm/9meag1258556322.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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