Model 5

*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, 20 Nov 2009 17:01:36 -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/21/t1258761751via3oko5a9iooy3.htm/, Retrieved Sat, 21 Nov 2009 01:02:41 +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/21/t1258761751via3oko5a9iooy3.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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103.86 93.6 104.08 107.47 104.2 103.86 111.1 95.3 107.47 117.33 102.7 111.1 119.04 103.1 117.33 123.68 100 119.04 125.9 107.2 123.68 124.54 107 125.9 119.39 119 124.54 118.8 110.4 119.39 114.81 101.7 118.8 117.9 102.4 114.81 120.53 98.8 117.9 125.15 105.6 120.53 126.49 104.4 125.15 131.85 106.3 126.49 127.4 107.2 131.85 131.08 108.5 127.4 122.37 106.9 131.08 124.34 114.2 122.37 119.61 125.9 124.34 119.97 110.6 119.61 116.46 110.5 119.97 117.03 106.7 116.46 120.96 104.7 117.03 124.71 107.4 120.96 127.08 109.8 124.71 131.91 103.4 127.08 137.69 114.8 131.91 142.46 114.3 137.69 144.32 109.6 142.46 138.06 118.3 144.32 124.45 127.3 138.06 126.71 112.3 124.45 121.83 114.9 126.71 122.51 108.2 121.83 125.48 105.4 122.51 127.77 122.1 125.48 128.03 113.5 127.77 132.84 110 128.03 133.41 125.3 132.84 139.99 114.3 133.41 138.53 115.6 139.99 136.12 127.1 138.53 124.75 123 136.12 122.88 122.2 124.75 121.46 126.4 122.88 118.4 112.7 121.46 122.45 105.8 118.4 128.94 120.9 122.45 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 Y[t] = + 4.10695106045493 + 0.218386375569317X[t] + 0.777029829453182Y1[t] + 2.90011676027211M1[t] + 2.73605265757977M2[t] + 2.83169003558597M3[t] + 6.24422789198119M4[t] + 1.62928897653466M5[t] + 4.27991344256131M6[t] + 0.984204629583514M7[t] -1.71621086446030M8[t] -8.89832924544343M9[t] -0.780062755102813M10[t] -4.41660041423161M11[t] -0.0270101800160684t + 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) 4.10695106045493 14.385418 0.2855 0.776605 0.388303 X 0.218386375569317 0.112462 1.9419 0.058572 0.029286 Y1 0.777029829453182 0.086773 8.9547 0 0 M1 2.90011676027211 2.065918 1.4038 0.1674 0.0837 M2 2.73605265757977 2.118924 1.2912 0.203361 0.101681 M3 2.83169003558597 2.060335 1.3744 0.176283 0.088141 M4 6.24422789198119 2.102023 2.9706 0.004801 0.0024 M5 1.62928897653466 2.443118 0.6669 0.508325 0.254163 M6 4.27991344256131 2.301956 1.8593 0.069687 0.034843 M7 0.984204629583514 2.416872 0.4072 0.685819 0.34291 M8 -1.71621086446030 2.602141 -0.6595 0.512988 0.256494 M9 -8.89832924544343 2.782186 -3.1983 0.002563 0.001281 M10 -0.780062755102813 2.203139 -0.3541 0.724979 0.362489 M11 -4.41660041423161 2.166337 -2.0387 0.047512 0.023756 t -0.0270101800160684 0.050675 -0.533 0.59671 0.298355 Multiple Linear Regression - Regression Statistics Multiple R 0.948824809745246 R-squared 0.900268519588102 Adjusted R-squared 0.86853577582068 F-TEST (value) 28.370333375091 F-TEST (DF numerator) 14 F-TEST (DF denominator) 44 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.99332082126786 Sum Squared Residuals 394.238659717571 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 103.86 108.294287043486 -4.43428704348628 2 107.47 110.247161779333 -2.77716177933291 3 111.1 111.177227919082 -0.0772279190820975 4 117.33 118.999433055589 -1.66943305558926 5 119.04 119.285734347848 -0.245734347847702 6 123.68 122.561071877958 1.11892812204165 7 125.9 124.416153197726 1.48384680227368 8 124.54 123.370056469939 1.16994353006135 9 119.39 117.724803847715 1.66519615228506 10 118.8 119.936233706459 -1.13623370645947 11 114.81 113.914276800484 0.895723199515833 12 117.9 115.35638847808 2.54361152191997 13 120.53 119.844326279297 0.685673720703138 14 125.15 123.181867801922 1.96813219807833 15 126.49 126.578309161302 -0.0883091613023478 16 131.85 131.419990922730 0.430009077269542 17 127.4 131.139469451149 -3.73946945114928 18 131.08 130.589203284333 0.490796715666681 19 122.37 129.776535862816 -7.40653586281628 20 124.34 121.875400915875 2.46459908412482 21 119.61 118.752141713060 0.85785828694023 22 119.97 119.826735383860 0.143264616139788 23 116.46 116.421079645762 0.0389203542384318 24 117.03 117.253426951433 -0.223426951433023 25 120.96 120.132667783339 0.827332216661246 26 124.71 123.584963944418 1.1250360555815 27 127.08 127.091580304224 -0.0115803042244265 28 131.91 130.920995872764 0.989004127236 29 137.69 132.521705535050 5.16829446494953 30 142.46 139.527359047516 2.93264095248422 31 144.32 138.884656375838 5.43534362416217 32 138.06 139.502467652014 -1.44246765201390 33 124.45 129.394609738762 -4.94460973876165 34 126.71 123.634694436689 3.07530556331136 35 121.83 122.295038588588 -0.465038588588186 36 122.51 121.429534538758 1.08046546124223 37 125.48 124.219539551448 1.26046044855211 38 127.77 129.983296334223 -2.21329633422303 39 128.03 129.953199011765 -1.92319901176482 40 132.84 132.776402129309 0.0635978706908114 41 133.41 135.213278059727 -1.80327805972695 42 139.99 135.877549217263 4.11245078273666 43 138.53 137.951588790312 0.578411209688453 44 136.12 136.601142884297 -0.481142884297153 45 124.75 126.623988294482 -1.8739882944816 46 122.88 125.705706343468 -2.82570634346801 47 121.46 121.506335500637 -0.0463355006368328 48 118.4 121.800650031729 -3.40065003172919 49 122.45 120.789179342430 1.66082065756978 50 128.94 127.042710140104 1.89728985989611 51 133.25 131.149683603626 2.10031639637369 52 137.94 137.753178019607 0.186821980392906 53 140.04 139.419812606226 0.620187393774412 54 130.74 139.394816572929 -8.65481657292921 55 131.55 131.641065773308 -0.0910657733080271 56 129.47 131.180932077875 -1.71093207787511 57 125.45 121.154456405982 4.29554359401796 58 127.87 127.126630129524 0.743369870476339 59 124.68 125.103269464529 -0.423269464529245 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.410116894075216 0.820233788150431 0.589883105924784 19 0.668610706200472 0.662778587599057 0.331389293799528 20 0.726211400802203 0.547577198395594 0.273788599197797 21 0.601086151295307 0.797827697409387 0.398913848704693 22 0.561464920311856 0.877070159376288 0.438535079688144 23 0.454574956963624 0.909149913927249 0.545425043036376 24 0.354201431940987 0.708402863881974 0.645798568059013 25 0.309101619628602 0.618203239257204 0.690898380371398 26 0.240147744554566 0.480295489109133 0.759852255445434 27 0.172553907064405 0.345107814128811 0.827446092935595 28 0.126381522733550 0.252763045467099 0.87361847726645 29 0.182539945510494 0.365079891020987 0.817460054489506 30 0.157579549709880 0.315159099419760 0.84242045029012 31 0.299017923072181 0.598035846144362 0.700982076927819 32 0.315955621732942 0.631911243465885 0.684044378267058 33 0.439749128130368 0.879498256260736 0.560250871869632 34 0.447598757601301 0.895197515202601 0.552401242398699 35 0.398165110875164 0.796330221750327 0.601834889124836 36 0.444729538894641 0.889459077789281 0.55527046110536 37 0.328396136594037 0.656792273188074 0.671603863405963 38 0.357490242300242 0.714980484600484 0.642509757699758 39 0.301570913985412 0.603141827970824 0.698429086014588 40 0.211197090117342 0.422394180234684 0.788802909882658 41 0.133469780763824 0.266939561527649 0.866530219236176 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/21/t1258761751via3oko5a9iooy3/10u20i1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/10u20i1258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/118y51258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/118y51258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/2r5eq1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/2r5eq1258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/3e8zm1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/3e8zm1258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/4ivyr1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/4ivyr1258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/5xiev1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/5xiev1258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/6j2q81258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/6j2q81258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/7fyq51258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/7fyq51258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/8z06k1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/8z06k1258761692.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/9bnjt1258761692.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258761751via3oko5a9iooy3/9bnjt1258761692.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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