M1

*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, 17 Nov 2009 23:35:29 -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/t1258527721wvdpaoq9gpayh4t.htm/, Retrieved Wed, 18 Nov 2009 08:02:13 +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/t1258527721wvdpaoq9gpayh4t.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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21 2472,81 19 2407,6 25 2454,62 21 2448,05 23 2497,84 23 2645,64 19 2756,76 18 2849,27 19 2921,44 19 2981,85 22 3080,58 23 3106,22 20 3119,31 14 3061,26 14 3097,31 14 3161,69 15 3257,16 11 3277,01 17 3295,32 16 3363,99 20 3494,17 24 3667,03 23 3813,06 20 3917,96 21 3895,51 19 3801,06 23 3570,12 23 3701,61 23 3862,27 23 3970,1 27 4138,52 26 4199,75 17 4290,89 24 4443,91 26 4502,64 24 4356,98 27 4591,27 27 4696,96 26 4621,4 24 4562,84 23 4202,52 23 4296,49 24 4435,23 17 4105,18 21 4116,68 19 3844,49 22 3720,98 22 3674,4 18 3857,62 16 3801,06 14 3504,37 12 3032,6 14 3047,03 16 2962,34 8 2197,82 3 2014,45 0 1862,83 5 1905,41 1 1810,99 1 1670,07 3 1864,44

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 Consvertr[t] = -2.48912082726048 + 0.00617525476907199Aand[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) -2.48912082726048 2.57129 -0.968 0.336974 0.168487 Aand 0.00617525476907199 0.000739 8.3511 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.736012952675855 R-squared 0.54171506650663 Adjusted R-squared 0.53394752526098 F-TEST (value) 69.7408677179512 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.39782629915430e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.67736310296413 Sum Squared Residuals 1290.78581022124 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 21 12.7811109182484 8.21888908175156 2 19 12.3784225547572 6.62157744524276 3 25 12.668783033999 12.331216966001 4 21 12.6282116101662 8.3717883898338 5 23 12.9356775451183 10.0643224548817 6 23 13.8483801999871 9.15161980001287 7 19 14.5345745099264 4.46542549007359 8 18 15.1058473286133 2.89415267138674 9 19 15.5515154652972 3.44848453470282 10 19 15.9245626058968 3.07543739410318 11 22 16.5342455092473 5.4657544907527 12 23 16.6925790415263 6.3074209584737 13 20 16.7734131264535 3.22658687354654 14 14 16.4149395871088 -2.41493958710883 15 14 16.6375575215339 -2.63755752153387 16 14 17.0351204235667 -3.03512042356673 17 15 17.6246719963700 -2.62467199637003 18 11 17.7472508035361 -6.74725080353611 19 17 17.8603197183578 -0.86031971835782 20 16 18.28437446335 -2.28437446334999 21 20 19.0882691291878 0.911730870812217 22 24 20.1557236685696 3.84427633143043 23 23 21.0574961224971 1.94250387750285 24 20 21.7052803477728 -1.7052803477728 25 21 21.5666458782071 -0.566645878207134 26 19 20.9833930652683 -1.98339306526828 27 23 19.5572797288988 3.4427202711012 28 23 20.3692639784841 2.63073602151592 29 23 21.3613804096832 1.63861959031682 30 23 22.0272581314322 0.972741868567788 31 27 23.0672945396393 3.93270546036068 32 26 23.4454053891496 2.55459461085041 33 17 24.0082181088028 -7.00821810880282 34 24 24.9531555935662 -0.95315559356621 35 26 25.3158283061538 0.684171693846191 36 24 24.4163406964908 -0.416340696490779 37 27 25.8631411363367 1.13685886366334 38 27 26.5158038128799 0.484196187120124 39 26 26.0492015625288 -0.0492015625287938 40 24 25.6875786432519 -1.68757864325194 41 23 23.4625108448599 -0.462510844859926 42 23 24.0427995355096 -1.04279953550962 43 24 24.8995543821707 -0.899554382170662 44 17 22.8614115456385 -5.86141154563846 45 21 22.9324269754828 -1.93242697548279 46 19 21.2515843798891 -2.25158437988908 47 22 20.488878663361 1.511121336639 48 22 20.2012352962176 1.79876470378237 49 18 21.332665475007 -3.33266547500700 50 16 20.9833930652683 -4.98339306526828 51 14 19.1512567278323 -5.15125672783232 52 12 16.2379567854272 -4.23795678542723 53 14 16.3270657117449 -2.32706571174494 54 16 15.8040833853522 0.19591661464777 55 8 11.0829776093013 -3.08297760930132 56 3 9.95062114229659 -6.95062114229659 57 0 9.01432901420989 -9.01432901420989 58 5 9.27727136227698 -4.27727136227698 59 1 8.6942038069812 -7.6942038069812 60 1 7.82398690492357 -6.82398690492357 61 3 9.0242711743881 -6.0242711743881 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.197063080321898 0.394126160643797 0.802936919678102 6 0.17796047473698 0.35592094947396 0.82203952526302 7 0.229356243763354 0.458712487526709 0.770643756236646 8 0.175867517398528 0.351735034797056 0.824132482601472 9 0.117889602655467 0.235779205310934 0.882110397344533 10 0.0786644319803607 0.157328863960721 0.92133556801964 11 0.141600815060914 0.283201630121827 0.858399184939086 12 0.259048995751742 0.518097991503483 0.740951004248258 13 0.237827572451378 0.475655144902757 0.762172427548621 14 0.527843711189506 0.944312577620988 0.472156288810494 15 0.644043704671593 0.711912590656815 0.355956295328407 16 0.666491473530611 0.667017052938778 0.333508526469389 17 0.605139530555251 0.789720938889498 0.394860469444749 18 0.735166446876784 0.529667106246431 0.264833553123216 19 0.681795143563268 0.636409712873465 0.318204856436732 20 0.610119509535043 0.779760980929914 0.389880490464957 21 0.709458393685063 0.581083212629875 0.290541606314937 22 0.9358226391249 0.128354721750199 0.0641773608750996 23 0.965454140527479 0.069091718945043 0.0345458594725215 24 0.953748376671007 0.0925032466579865 0.0462516233289932 25 0.941191704633683 0.117616590732635 0.0588082953663173 26 0.916068984233025 0.167862031533951 0.0839310157669754 27 0.950195836175118 0.0996083276497634 0.0498041638248817 28 0.964992986763284 0.070014026473431 0.0350070132367155 29 0.967645251285498 0.0647094974290046 0.0323547487145023 30 0.964349451685961 0.0713010966280773 0.0356505483140387 31 0.98828040194625 0.0234391961075004 0.0117195980537502 32 0.992549979118432 0.0149000417631350 0.00745002088156752 33 0.998699200034983 0.00260159993003401 0.00130079996501701 34 0.997757420807216 0.00448515838556836 0.00224257919278418 35 0.996928777101728 0.00614244579654371 0.00307122289827186 36 0.994683879541669 0.0106322409166621 0.00531612045833106 37 0.993309646246006 0.0133807075079875 0.00669035375399373 38 0.990029253789218 0.0199414924215643 0.00997074621078214 39 0.983941798133344 0.0321164037333119 0.0160582018666559 40 0.97445401810593 0.0510919637881394 0.0255459818940697 41 0.960207595718543 0.0795848085629138 0.0397924042814569 42 0.937729703096857 0.124540593806285 0.0622702969031427 43 0.905875073216318 0.188249853567363 0.0941249267836817 44 0.951591722316937 0.0968165553661252 0.0484082776830626 45 0.928199339416434 0.143601321167132 0.0718006605835658 46 0.898079347925233 0.203841304149533 0.101920652074767 47 0.902192463463798 0.195615073072403 0.0978075365362017 48 0.946039051305658 0.107921897388685 0.0539609486943425 49 0.917442338233353 0.165115323533294 0.0825576617666472 50 0.927497222153495 0.14500555569301 0.072502777846505 51 0.968965153881723 0.0620696922365544 0.0310348461182772 52 0.979137691159993 0.0417246176800145 0.0208623088400072 53 0.978609776090753 0.0427804478184949 0.0213902239092475 54 0.950032999624433 0.099934000751133 0.0499670003755665 55 0.930581225827775 0.138837548344450 0.0694187741722248 56 0.871144337196848 0.257711325606305 0.128855662803152 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0576923076923077 NOK 5% type I error level 11 0.211538461538462 NOK 10% type I error level 22 0.423076923076923 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/105u041258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/105u041258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/1rhaf1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/1rhaf1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/2mrin1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/2mrin1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/3lh4r1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/3lh4r1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/4vf1h1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/4vf1h1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/5sk8s1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/5sk8s1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/68q6e1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/68q6e1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/78nyf1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/78nyf1258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/8kxy01258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/8kxy01258526123.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/9ffvo1258526123.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258527721wvdpaoq9gpayh4t/9ffvo1258526123.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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