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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: Fri, 20 Nov 2009 07:06:33 -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/20/t1258726023gf5a21uwupalve5.htm/, Retrieved Fri, 20 Nov 2009 15:07:16 +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/20/t1258726023gf5a21uwupalve5.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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89.1 0 100 88 82.6 0 89.1 100 102.7 0 82.6 89.1 91.8 0 102.7 82.6 94.1 0 91.8 102.7 103.1 0 94.1 91.8 93.2 0 103.1 94.1 91 0 93.2 103.1 94.3 0 91 93.2 99.4 0 94.3 91 115.7 0 99.4 94.3 116.8 0 115.7 99.4 99.8 0 116.8 115.7 96 0 99.8 116.8 115.9 0 96 99.8 109.1 0 115.9 96 117.3 0 109.1 115.9 109.8 0 117.3 109.1 112.8 0 109.8 117.3 110.7 0 112.8 109.8 100 0 110.7 112.8 113.3 0 100 110.7 122.4 0 113.3 100 112.5 0 122.4 113.3 104.2 0 112.5 122.4 92.5 0 104.2 112.5 117.2 0 92.5 104.2 109.3 0 117.2 92.5 106.1 0 109.3 117.2 118.8 0 106.1 109.3 105.3 0 118.8 106.1 106 0 105.3 118.8 102 0 106 105.3 112.9 0 102 106 116.5 0 112.9 102 114.8 0 116.5 112.9 100.5 0 114.8 116.5 85.4 0 100.5 114.8 114.6 0 85.4 100.5 109.9 0 114.6 85.4 100.7 0 109.9 114.6 115.5 0 100.7 109.9 100.7 1 115.5 100.7 99 1 100.7 115.5 102.3 1 99 100.7 108.8 1 102.3 99 105.9 1 108.8 102.3 113.2 1 105.9 108.8 95.7 1 113.2 105.9 80.9 1 95.7 113.2 113.9 1 80.9 95.7 98.1 1 113.9 80.9 102.8 1 98.1 113.9 1 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation TotaleIndustrieleProductie[t] = + 48.7719098176646 -3.92815091693391X[t] + 0.229632225851288Y1[t] + 0.35939013498551Y2[t] -16.6549309698687M1[t] -24.6048091612375M2[t] + 7.98612935353004M3[t] -3.39181676279789M4[t] -9.8962790917531M5[t] -0.591645919035228M6[t] -10.2867511923731M7[t] -11.8673684644641M8[t] -8.68197056383971M9[t] -0.597406321343375M10[t] + 4.17151212199479M11[t] + 0.0603498253718281t + 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) 48.7719098176646 14.322241 3.4053 0.001421 0.00071 X -3.92815091693391 2.800057 -1.4029 0.167669 0.083835 Y1 0.229632225851288 0.134701 1.7048 0.095293 0.047646 Y2 0.35939013498551 0.126988 2.8301 0.006986 0.003493 M1 -16.6549309698687 3.091986 -5.3865 3e-06 1e-06 M2 -24.6048091612375 3.912985 -6.288 0 0 M3 7.98612935353004 4.388498 1.8198 0.075599 0.037799 M4 -3.39181676279789 3.893557 -0.8711 0.388408 0.194204 M5 -9.8962790917531 3.544963 -2.7916 0.007729 0.003865 M6 -0.591645919035228 3.2706 -0.1809 0.857278 0.428639 M7 -10.2867511923731 3.035381 -3.3889 0.00149 0.000745 M8 -11.8673684644641 3.544847 -3.3478 0.001677 0.000838 M9 -8.68197056383971 3.393313 -2.5586 0.014028 0.007014 M10 -0.597406321343375 3.411245 -0.1751 0.861782 0.430891 M11 4.17151212199479 3.139765 1.3286 0.190828 0.095414 t 0.0603498253718281 0.07321 0.8243 0.414191 0.207096 Multiple Linear Regression - Regression Statistics Multiple R 0.904468438807846 R-squared 0.818063156799503 Adjusted R-squared 0.756039232981151 F-TEST (value) 13.1894776472923 F-TEST (DF numerator) 15 F-TEST (DF denominator) 44 p-value 1.17428289314603e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.75429381103554 Sum Squared Residuals 994.545624232637 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 89.1 86.7668831370217 2.33311686297828 2 82.6 80.6870451290717 1.91295487092829 3 102.7 107.928371529836 -5.22837152983556 4 91.8 98.8903471010845 -7.09034710108453 5 94.1 97.1669850489309 -3.06698504893088 6 103.1 103.142769695136 -0.042769695136433 7 93.2 96.4013015902987 -3.20130159029867 8 91 95.8421863225214 -4.84218632252139 9 94.3 95.0247808152882 -0.72478081528819 10 99.4 103.136822931497 -3.73682293149747 11 115.7 110.323202997501 5.37679700249879 12 116.8 111.787935670680 5.01206432931964 13 99.8 101.304009174884 -1.50400917488376 14 96 89.906062117899 6.09393788210103 15 115.9 115.575115705050 0.324884294950293 16 109.1 107.461518195589 1.63848180441069 17 117.3 106.607770242429 10.6922297575712 18 109.8 115.411884574598 -5.61188457459761 19 112.8 107.001886539628 5.79811346037193 20 110.7 103.475089758071 7.22491024192851 21 100 107.316780214736 -7.3167802147365 22 113.3 112.249910182526 1.05008981747369 23 122.4 116.287812610713 6.11218738928652 24 112.5 119.046192364645 -6.54619236464452 25 104.2 103.448702412588 0.751297587411934 26 92.5 90.0952642356689 2.40473576433112 27 117.2 117.076917412968 0.123082587031616 28 109.3 107.226372521209 2.07362747879137 29 106.1 107.845101767542 -1.74510176754219 30 118.8 113.636079576522 5.16392042347777 31 105.3 105.767604964914 -0.467604964913886 32 106 105.711557183518 0.288442816481648 33 102 104.266280645306 -2.26628064530606 34 112.9 111.744238904259 1.15576109574108 35 116.5 117.638937894806 -1.13893789480592 36 114.8 118.271804082590 -3.47180408258966 37 100.5 102.580652640093 -2.08065264009346 38 85.4 90.7964202149477 -5.39642021494772 39 114.6 114.840983014440 -0.240983014439798 40 109.9 104.801856680060 5.09814331993991 41 100.7 107.772664656553 -7.07266465655256 42 115.5 113.335897542379 2.16410245762148 43 100.7 99.865158878211 0.834841121789101 44 99 100.265308486678 -1.26530848667826 45 102.3 97.8017074309417 4.49829256905829 46 108.8 106.093444614644 2.70655538535624 47 105.9 113.601309796839 -7.7013097968393 48 113.2 111.160249922653 2.03975007734658 49 95.7 95.199752635413 0.500247364586997 50 80.9 85.9152083024127 -5.01520830241273 51 113.9 108.878612337707 5.02138766229344 52 98.1 99.8199055020574 -1.71990550205743 53 102.8 101.607478284546 1.19252171545447 54 104.7 106.373368611365 -1.67336861136521 55 95.9 98.8640480269485 -2.96404802694849 56 94.6 96.0058582492105 -1.40585824921052 57 101.6 95.7904508937275 5.80954910627246 58 103.9 105.075583367074 -1.17558336707354 59 110.3 112.94873670014 -2.64873670014008 60 114.1 111.133817959432 2.96618204056796 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.885744354662036 0.228511290675929 0.114255645337964 20 0.849676890981094 0.300646218037811 0.150323109018906 21 0.849103452071177 0.301793095857646 0.150896547928823 22 0.843242895736402 0.313514208527196 0.156757104263598 23 0.944068599006053 0.111862801987895 0.0559314009939474 24 0.998294136268217 0.00341172746356511 0.00170586373178255 25 0.996847767447684 0.00630446510463106 0.00315223255231553 26 0.997033543714508 0.00593291257098407 0.00296645628549204 27 0.993317212101977 0.0133655757960456 0.0066827878980228 28 0.986181828766204 0.0276363424675911 0.0138181712337956 29 0.98282661270671 0.0343467745865817 0.0171733872932908 30 0.980818982859855 0.0383620342802906 0.0191810171401453 31 0.970699007747668 0.058601984504664 0.029300992252332 32 0.964007354431402 0.0719852911371953 0.0359926455685977 33 0.949033504642436 0.101932990715128 0.0509664953575642 34 0.911306062724148 0.177387874551703 0.0886939372758516 35 0.914689068886684 0.170621862226633 0.0853109311133163 36 0.881094183847517 0.237811632304967 0.118905816152483 37 0.81992531440963 0.360149371180739 0.180074685590370 38 0.75469051759435 0.4906189648113 0.24530948240565 39 0.700993085412501 0.598013829174998 0.299006914587499 40 0.725193528041864 0.549612943916273 0.274806471958136 41 0.79097818107525 0.418043637849499 0.209021818924750 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.130434782608696 NOK 5% type I error level 7 0.304347826086957 NOK 10% type I error level 9 0.391304347826087 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/100bth1258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/100bth1258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/18oxh1258725988.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/18oxh1258725988.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/2zd021258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/2zd021258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/38l4n1258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/38l4n1258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/43l8o1258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/43l8o1258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/5ljw61258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/5ljw61258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/66pmr1258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/66pmr1258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/7z6v01258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/7z6v01258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/83l351258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/83l351258725989.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/962nt1258725989.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726023gf5a21uwupalve5/962nt1258725989.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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