Model 2

*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, 18 Dec 2009 09:35:48 -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/Dec/18/t1261154205i9jehgcrvi9xvgb.htm/, Retrieved Fri, 18 Dec 2009 17:36:58 +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/Dec/18/t1261154205i9jehgcrvi9xvgb.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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3397 562 3971 561 4625 555 4486 544 4132 537 4685 543 3172 594 4280 611 4207 613 4158 611 3933 594 3151 595 3616 591 4221 589 4436 584 4807 573 4849 567 5024 569 3521 621 4650 629 5393 628 5147 612 4845 595 3995 597 4493 593 4680 590 5463 580 4761 574 5307 573 5069 573 3501 620 4952 626 5152 620 5317 588 5189 566 4030 557 4420 561 4571 549 4551 532 4819 526 5133 511 4532 499 3339 555 4380 565 4632 542 4719 527 4212 510 3615 514 3420 517 4571 508 4407 493 4386 490 4386 469 4744 478 3185 528 3890 534 4520 518 3990 506 3809 502 3236 516 3551 528 3264 533 3579 536 3537 537 3038 524 2888 536 2198 587

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 wng[t] = + 736.698080235686 + 5.16139244290088totWL[t] + 195.970674997014M1[t] + 611.72911395432M2[t] + 951.907384311828M3[t] + 938.709072302566M4[t] + 1001.07035961969M5[t] + 1002.61308103147M6[t] -599.144832296955M7[t] + 632.996201124087M8[t] + 1028.81645462161M9[t] + 993.701898242288M10[t] + 804.587341862962M11[t] + 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) 736.698080235686 1089.648673 0.6761 0.50187 0.250935 totWL 5.16139244290088 1.909489 2.703 0.009167 0.004583 M1 195.970674997014 334.430424 0.586 0.560328 0.280164 M2 611.72911395432 334.389113 1.8294 0.072864 0.036432 M3 951.907384311828 334.840109 2.8429 0.006299 0.003149 M4 938.709072302566 335.631909 2.7968 0.007135 0.003568 M5 1001.07035961969 337.948978 2.9622 0.004534 0.002267 M6 1002.61308103147 337.207887 2.9733 0.004396 0.002198 M7 -599.144832296955 338.744274 -1.7687 0.082588 0.041294 M8 632.996201124087 356.404709 1.7761 0.081357 0.040679 M9 1028.81645462161 353.439483 2.9109 0.005227 0.002614 M10 993.701898242288 350.135446 2.838 0.006382 0.003191 M11 804.587341862962 349.284459 2.3035 0.025126 0.012563 Multiple Linear Regression - Regression Statistics Multiple R 0.707918320713642 R-squared 0.501148348802023 Adjusted R-squared 0.390292426313583 F-TEST (value) 4.52071786109835 F-TEST (DF numerator) 12 F-TEST (DF denominator) 54 p-value 5.36987071211303e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 552.219682679157 Sum Squared Residuals 16467115.2086665 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3397 3833.37130814302 -436.37130814302 2 3971 4243.96835465741 -272.968354657405 3 4625 4553.17827035751 71.821729642493 4 4486 4483.20464147634 2.79535852366338 5 4132 4509.43618169316 -377.436181693156 6 4685 4541.94725776234 143.052742237658 7 3172 3203.42035902186 -31.4203590218569 8 4280 4523.30506397222 -243.305063972216 9 4207 4929.44810235555 -722.448102355546 10 4158 4884.01076109042 -726.010761090418 11 3933 4607.15253318178 -674.152533181776 12 3151 3807.72658376172 -656.726583761715 13 3616 3983.05168898713 -367.051688987125 14 4221 4388.48734305863 -167.48734305863 15 4436 4702.85865120163 -266.858651201633 16 4807 4632.88502232046 174.114977679538 17 4849 4664.27795498018 184.722045019817 18 5024 4676.14346127776 347.856538722235 19 3521 3342.77795498018 178.222045019817 20 4650 4616.21012794443 33.7898720555683 21 5393 5006.86898899906 386.131011000941 22 5147 4889.17215353332 257.827846466682 23 4845 4612.31392562468 232.686074375323 24 3995 3818.04936864752 176.950631352483 25 4493 3993.37447387293 499.625526127073 26 4680 4393.64873550153 286.351264498469 27 5463 4682.21308143003 780.78691856997 28 4761 4638.04641476336 122.953585236637 29 5307 4695.24630963759 611.753690362412 30 5069 4696.78903104937 372.210968950631 31 3501 3337.61656253728 163.383437462718 32 4952 4600.72595061573 351.274049384271 33 5152 4965.57784945585 186.422150544149 34 5317 4765.2987349037 551.701265096303 35 5189 4462.63354478055 726.366455219449 36 4030 3611.59367093148 418.406329068519 37 4420 3828.2099157001 591.790084299901 38 4571 4182.03164534259 388.968354657405 39 4551 4434.46624417079 116.533755829213 40 4819 4390.29957750412 428.70042249588 41 5133 4375.23997817773 757.760021822267 42 4532 4314.8459902747 217.154009725297 43 3339 3002.12605374872 336.873946251275 44 4380 4285.88101159878 94.1189884012246 45 4632 4562.98923890958 69.0107610904175 46 4719 4450.45379588674 268.546204113257 47 4212 4173.5955679781 38.4044320218986 48 3615 3389.65379588674 225.346204113257 49 3420 3601.10864821246 -181.108648212460 50 4571 3970.41455518366 600.585444816342 51 4407 4233.17193889765 173.828061102347 52 4386 4204.48944955969 181.510550440312 53 4386 4158.4614955759 227.538504424104 54 4744 4206.45674897378 537.543251026215 55 3185 2862.7684577904 322.231542209599 56 3890 4125.87784586885 -235.877845868848 57 4520 4439.11582027996 80.8841797200387 58 3990 4342.06455458582 -352.064554585824 59 3809 4132.30442843489 -323.304428434895 60 3236 3399.97658077255 -163.976580772545 61 3551 3657.88396508437 -106.883965084370 62 3264 4099.44936625618 -835.44936625618 63 3579 4455.11181394239 -876.11181394239 64 3537 4447.07489437603 -910.07489437603 65 3038 4442.33807993545 -1404.33807993545 66 2888 4505.81751066204 -1617.81751066204 67 2198 3167.29061192155 -969.290611921553 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0298166347754024 0.0596332695508049 0.970183365224598 17 0.0381059153949387 0.0762118307898774 0.961894084605061 18 0.0129220981454203 0.0258441962908407 0.98707790185458 19 0.00396367605017963 0.00792735210035925 0.99603632394982 20 0.00138381136773344 0.00276762273546689 0.998616188632267 21 0.0208965016823982 0.0417930033647965 0.979103498317602 22 0.0481606278241969 0.0963212556483939 0.951839372175803 23 0.0648411216446742 0.129682243289348 0.935158878355326 24 0.0688027577633309 0.137605515526662 0.93119724223667 25 0.0734021499738658 0.146804299947732 0.926597850026134 26 0.0488679081216265 0.097735816243253 0.951132091878373 27 0.0597387199206228 0.119477439841246 0.940261280079377 28 0.036795669881755 0.07359133976351 0.963204330118245 29 0.0292835886717609 0.0585671773435219 0.97071641132824 30 0.0196443842268674 0.0392887684537349 0.980355615773133 31 0.0113588430143201 0.0227176860286402 0.98864115698568 32 0.00806318573972838 0.0161263714794568 0.991936814260272 33 0.005294716825304 0.010589433650608 0.994705283174696 34 0.0148047588514267 0.0296095177028534 0.985195241148573 35 0.0547563157763535 0.109512631552707 0.945243684223646 36 0.0710065518436408 0.142013103687282 0.92899344815636 37 0.124163637283985 0.248327274567971 0.875836362716015 38 0.143521244890784 0.287042489781569 0.856478755109215 39 0.133488070519016 0.266976141038032 0.866511929480984 40 0.16190410193069 0.32380820386138 0.83809589806931 41 0.481587884180515 0.96317576836103 0.518412115819485 42 0.454580511197527 0.909161022395054 0.545419488802473 43 0.434095589690401 0.868191179380803 0.565904410309598 44 0.561033033994982 0.877933932010036 0.438966966005018 45 0.53916892208731 0.92166215582538 0.46083107791269 46 0.808642323730585 0.382715352538831 0.191357676269415 47 0.822034839479632 0.355930321040736 0.177965160520368 48 0.761287939261534 0.477424121476931 0.238712060738466 49 0.709764808190449 0.580470383619103 0.290235191809551 50 0.837039902733413 0.325920194533174 0.162960097266587 51 0.708989568789716 0.582020862420567 0.291010431210284 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0555555555555556 NOK 5% type I error level 9 0.25 NOK 10% type I error level 15 0.416666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/10a3sv1261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/10a3sv1261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/1dwnc1261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/1dwnc1261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/2jo761261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/2jo761261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/32cd11261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/32cd11261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/4zs3x1261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/4zs3x1261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/5qywl1261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/5qywl1261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/64pvz1261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/64pvz1261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/7iy471261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/7iy471261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/84xq01261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/84xq01261154142.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/9ood41261154142.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261154205i9jehgcrvi9xvgb/9ood41261154142.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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