Multiple..

*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: Tue, 09 Dec 2008 10:33:14 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/09/t1228844084she7men3bdo2df0.htm/, Retrieved Tue, 09 Dec 2008 17:34:44 +0000

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/2008/Dec/09/t1228844084she7men3bdo2df0.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

101,02 0 100,67 0 100,47 0 100,38 0 100,33 0 100,34 0 100,37 0 100,39 0 100,21 0 100,21 0 100,22 0 100,28 0 100,25 0 100,25 0 100,21 0 100,16 0 100,18 0 100,1 1 99,96 1 99,88 1 99,88 1 99,86 1 99,84 1 99,8 1 99,82 1 99,81 1 99,92 1 100,03 1 99,99 1 100,02 1 100,01 1 100,13 1 100,33 1 100,13 1 99,96 1 100,05 1 99,83 1 99,8 1 100,01 1 100,1 1 100,13 1 100,16 1 100,41 1 101,34 1 101,65 1 101,85 1 102,07 1 102,12 1 102,14 1 102,21 1 102,28 1 102,19 1 102,33 1 102,54 1 102,44 1 102,78 1 102,9 1 103,08 1 102,77 1 102,65 1 102,71 1 103,29 1 102,86 1 103,45 1 103,72 1 103,65 1 103,83 1 104,45 1 105,14 1 105,07 1 105,31 1 105,19 1 105,3 1 105,02 1 105,17 1 105,28 1 105,45 1 105,38 1 105,8 1 105,96 1 105,08 1 105,11 1 105,61 1 105,5 1

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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation Suiker[t] = + 99.35609689441 -2.49431304347826dummie[t] + 0.145857660455483M1[t] + 0.0386457556935807M2[t] -0.084280434782609M3[t] -0.0929209109730872M4[t] -0.120132815734991M5[t] + 0.140414285714285M6[t] + 0.126059523809522M7[t] + 0.323133333333331M8[t] + 0.255921428571428M9[t] + 0.168709523809519M10[t] + 0.131497619047617M11[t] + 0.104354761904762t + 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) 99.35609689441 0.283309 350.699 0 0 dummie -2.49431304347826 0.239469 -10.416 0 0 M1 0.145857660455483 0.336383 0.4336 0.665908 0.332954 M2 0.0386457556935807 0.336175 0.115 0.908808 0.454404 M3 -0.084280434782609 0.336013 -0.2508 0.802684 0.401342 M4 -0.0929209109730872 0.335897 -0.2766 0.782875 0.391438 M5 -0.120132815734991 0.335827 -0.3577 0.721628 0.360814 M6 0.140414285714285 0.335731 0.4182 0.677056 0.338528 M7 0.126059523809522 0.335476 0.3758 0.708229 0.354115 M8 0.323133333333331 0.335267 0.9638 0.33846 0.16923 M9 0.255921428571428 0.335105 0.7637 0.447609 0.223804 M10 0.168709523809519 0.334988 0.5036 0.616104 0.308052 M11 0.131497619047617 0.334919 0.3926 0.695789 0.347895 t 0.104354761904762 0.003947 26.4405 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.960511194532887 R-squared 0.922581754822994 Adjusted R-squared 0.908204080718693 F-TEST (value) 64.1676635685463 F-TEST (DF numerator) 13 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.626531715411946 Sum Squared Residuals 27.4779393291925 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.02 99.6063093167702 1.41369068322981 2 100.67 99.603452173913 1.06654782608696 3 100.47 99.5848807453416 0.885119254658385 4 100.38 99.680595031056 0.699404968944095 5 100.33 99.7577378881988 0.572262111801242 6 100.34 100.122639751553 0.217360248447209 7 100.37 100.212639751553 0.157360248447211 8 100.39 100.514068322981 -0.124068322981365 9 100.21 100.551211180124 -0.341211180124230 10 100.21 100.568354037267 -0.358354037267083 11 100.22 100.63549689441 -0.415496894409936 12 100.28 100.608354037267 -0.328354037267080 13 100.25 100.858566459627 -0.608566459627327 14 100.25 100.855709316770 -0.60570931677019 15 100.21 100.837137888199 -0.627137888198765 16 100.16 100.932852173913 -0.772852173913046 17 100.18 101.009995031056 -0.829995031055894 18 100.1 98.8805838509317 1.21941614906832 19 99.96 98.9705838509317 0.989416149068318 20 99.88 99.2720124223602 0.607987577639749 21 99.88 99.3091552795031 0.570844720496891 22 99.86 99.326298136646 0.533701863354041 23 99.84 99.3934409937888 0.446559006211186 24 99.8 99.366298136646 0.433701863354034 25 99.82 99.6165105590062 0.203489440993786 26 99.81 99.613653416149 0.196346583850936 27 99.92 99.5950819875776 0.324918012422362 28 100.03 99.690796273292 0.339203726708078 29 99.99 99.7679391304348 0.222060869565214 30 100.02 100.132840993789 -0.112840993788824 31 100.01 100.222840993789 -0.212840993788814 32 100.13 100.524269565217 -0.394269565217394 33 100.33 100.561412422360 -0.231412422360249 34 100.13 100.578555279503 -0.448555279503106 35 99.96 100.645698136646 -0.685698136645967 36 100.05 100.618555279503 -0.568555279503108 37 99.83 100.868767701863 -1.03876770186335 38 99.8 100.865910559006 -1.06591055900621 39 100.01 100.847339130435 -0.837339130434778 40 100.1 100.943053416149 -0.843053416149071 41 100.13 101.020196273292 -0.89019627329193 42 100.16 101.385098136646 -1.22509813664597 43 100.41 101.475098136646 -1.06509813664596 44 101.34 101.776526708075 -0.436526708074529 45 101.65 101.813669565217 -0.163669565217385 46 101.85 101.830812422360 0.0191875776397507 47 102.07 101.897955279503 0.17204472049689 48 102.12 101.870812422360 0.249187577639756 49 102.14 102.121024844720 0.0189751552795065 50 102.21 102.118167701863 0.0918322981366409 51 102.28 102.099596273292 0.180403726708075 52 102.19 102.195310559006 -0.0053105590062117 53 102.33 102.272453416149 0.0575465838509305 54 102.54 102.637355279503 -0.0973552795030992 55 102.44 102.727355279503 -0.287355279503106 56 102.78 103.028783850932 -0.248783850931675 57 102.9 103.065926708075 -0.165926708074528 58 103.08 103.083069565217 -0.00306956521738853 59 102.77 103.150212422360 -0.38021242236025 60 102.65 103.123069565217 -0.473069565217386 61 102.71 103.373281987578 -0.663281987577644 62 103.29 103.370424844721 -0.0804248447204903 63 102.86 103.351853416149 -0.49185341614907 64 103.45 103.447567701863 0.00243229813665016 65 103.72 103.524710559006 0.195289440993788 66 103.65 103.889612422360 -0.239612422360244 67 103.83 103.979612422360 -0.14961242236025 68 104.45 104.281040993789 0.168959006211183 69 105.14 104.318183850932 0.821816149068323 70 105.07 104.335326708075 0.734673291925461 71 105.31 104.402469565217 0.907530434782612 72 105.19 104.375326708075 0.814673291925462 73 105.3 104.625539130435 0.674460869565217 74 105.02 104.622681987578 0.397318012422357 75 105.17 104.604110559006 0.565889440993789 76 105.28 104.699824844720 0.580175155279505 77 105.45 104.776967701863 0.673032298136649 78 105.38 105.141869565217 0.238130434782603 79 105.8 105.231869565217 0.568130434782605 80 105.96 105.533298136646 0.426701863354031 81 105.08 105.570440993789 -0.490440993788822 82 105.11 105.587583850932 -0.477583850931675 83 105.61 105.654726708075 -0.0447267080745334 84 105.5 105.627583850932 -0.127583850931678 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0473946293606764 0.0947892587213529 0.952605370639324 18 0.0157357227132345 0.0314714454264691 0.984264277286765 19 0.00575218636309166 0.0115043727261833 0.994247813636908 20 0.00208492899990761 0.00416985799981523 0.997915071000092 21 0.000613861770709948 0.00122772354141990 0.99938613822929 22 0.000174158084773430 0.000348316169546861 0.999825841915227 23 4.7329813757484e-05 9.4659627514968e-05 0.999952670186243 24 1.54969089090967e-05 3.09938178181935e-05 0.99998450309109 25 1.19054237988529e-05 2.38108475977059e-05 0.999988094576201 26 3.55590764633136e-06 7.11181529266273e-06 0.999996444092354 27 2.48861646712502e-06 4.97723293425004e-06 0.999997511383533 28 6.3645167365997e-06 1.27290334731994e-05 0.999993635483263 29 6.96619572297149e-06 1.39323914459430e-05 0.999993033804277 30 8.9026245970264e-06 1.78052491940528e-05 0.999991097375403 31 9.55590881929488e-06 1.91118176385898e-05 0.99999044409118 32 1.56062880303316e-05 3.12125760606633e-05 0.99998439371197 33 0.000120499224513532 0.000240998449027065 0.999879500775486 34 0.000114716292312209 0.000229432584624418 0.999885283707688 35 5.71275652025656e-05 0.000114255130405131 0.999942872434797 36 3.20822685141228e-05 6.41645370282456e-05 0.999967917731486 37 2.19664935047045e-05 4.3932987009409e-05 0.999978033506495 38 1.40624935799802e-05 2.81249871599605e-05 0.99998593750642 39 7.47639518307626e-06 1.49527903661525e-05 0.999992523604817 40 5.65536923268882e-06 1.13107384653776e-05 0.999994344630767 41 6.53245986843546e-06 1.30649197368709e-05 0.999993467540132 42 6.88008963528944e-06 1.37601792705789e-05 0.999993119910365 43 1.91768231993571e-05 3.83536463987142e-05 0.9999808231768 44 0.00322331574879640 0.00644663149759279 0.996776684251204 45 0.0442073237395299 0.0884146474790598 0.95579267626047 46 0.180708494225725 0.361416988451450 0.819291505774275 47 0.398089593470131 0.796179186940261 0.601910406529869 48 0.566845496824946 0.866309006350107 0.433154503175054 49 0.609347431253587 0.781305137492827 0.390652568746413 50 0.641088664779104 0.717822670441792 0.358911335220896 51 0.669936055335308 0.660127889329384 0.330063944664692 52 0.644342336446601 0.711315327106798 0.355657663553399 53 0.620830742678782 0.758338514642437 0.379169257321218 54 0.576489809055752 0.847020381888497 0.423510190944248 55 0.509613609085267 0.980772781829466 0.490386390914733 56 0.445510647272847 0.891021294545695 0.554489352727153 57 0.37486099130724 0.74972198261448 0.62513900869276 58 0.317367994859665 0.63473598971933 0.682632005140335 59 0.267214826504279 0.534429653008557 0.732785173495721 60 0.228445348981780 0.456890697963560 0.77155465101822 61 0.282400466566214 0.564800933132429 0.717599533433786 62 0.234565494619994 0.469130989239987 0.765434505380006 63 0.278865375475204 0.557730750950409 0.721134624524796 64 0.268855960020566 0.537711920041131 0.731144039979434 65 0.264597282960931 0.529194565921861 0.73540271703907 66 0.268434640798942 0.536869281597883 0.731565359201058 67 0.502187016077776 0.995625967844448 0.497812983922224 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.490196078431373 NOK 5% type I error level 27 0.529411764705882 NOK 10% type I error level 29 0.568627450980392 NOK

Charts produced by software:

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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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