WS 7

*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 08:43:04 -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/t1258731810utzgisur5yycwbv.htm/, Retrieved Fri, 20 Nov 2009 16:43:42 +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/t1258731810utzgisur5yycwbv.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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100 100 96,21064363 97,82226485 96,31280765 94,04971502 107,1793443 91,12460521 114,9066592 93,13202153 92,56060184 93,88342812 114,9995356 92,55349954 107,1236185 94,43494835 117,7765394 96,25017563 107,3650971 100,4355715 106,2970187 101,5036685 114,5072908 99,39789728 98,0031578 99,68990733 103,0649206 101,6895041 100,2879168 103,6652759 104,6066685 103,0532766 111,1544534 100,9500712 104,9874617 102,345366 109,9284852 101,6472299 111,5352466 99,56809393 132,4974459 95,67727392 100,3436426 96,58494865 123,0983561 96,32604937 114,2379493 95,37109101 104,569518 96,00056203 109,0833101 96,88367859 106,9843039 94,85280372 133,6769759 92,46943974 124,8537197 93,99180173 122,5132349 93,45262168 116,8013374 92,26698759 116,0118882 90,39653498 129,7575926 90,43001228 125,1973623 91,04995327 143,7912139 89,07845784 127,9465032 89,69314509 130,2962757 87,92459054 108,4424631 85,8789319 129,3675118 83,20612366 143,6797622 83,85722053 131,8844618 83,01393462 117,6186496 82,8450 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Import[t] = + 222.724184604475 -1.08884531590375Wisselkoers[t] -3.43675003487935M1[t] -10.6811622089806M2[t] -2.69348059950483M3[t] + 2.95796464459367M4[t] + 1.09917038098968M5[t] -4.38448659729871M6[t] -9.19465016228743M7[t] -13.1031916205358M8[t] + 3.13537411617422M9[t] -8.82688454470125M10[t] -1.81513701099472M11[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) 222.724184604475 25.925399 8.591 0 0 Wisselkoers -1.08884531590375 0.277986 -3.9169 0.000289 0.000145 M1 -3.43675003487935 9.158767 -0.3752 0.709169 0.354584 M2 -10.6811622089806 9.194129 -1.1617 0.251208 0.125604 M3 -2.69348059950483 9.178091 -0.2935 0.770456 0.385228 M4 2.95796464459367 9.125966 0.3241 0.747281 0.37364 M5 1.09917038098968 9.133286 0.1203 0.904721 0.45236 M6 -4.38448659729871 9.155683 -0.4789 0.634243 0.317122 M7 -9.19465016228743 9.118665 -1.0083 0.318458 0.159229 M8 -13.1031916205358 9.111566 -1.4381 0.157036 0.078518 M9 3.13537411617422 9.110401 0.3442 0.732266 0.366133 M10 -8.82688454470125 9.110883 -0.9688 0.33759 0.168795 M11 -1.81513701099472 9.110179 -0.1992 0.842932 0.421466 Multiple Linear Regression - Regression Statistics Multiple R 0.589301759016927 R-squared 0.347276563180444 Adjusted R-squared 0.180623770800983 F-TEST (value) 2.0838328492553 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.0368566998434336 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.4043696553305 Sum Squared Residuals 9751.83566286806 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 110.402902979220 -10.4029029792204 2 96.21064363 105.529707522475 -9.31906389247546 3 96.31280765 117.625112343360 -21.3123046933602 4 107.1793443 126.461549702581 -19.2822054025813 5 114.9066592 122.416989581877 -7.51033038187651 6 92.56060184 116.115167057727 -23.5545652177274 7 114.9995356 112.753089997558 2.24644560244179 8 107.1236185 106.795941815429 0.327676684571365 9 117.7765394 121.05800583101 -3.28146643100999 10 107.3650971 104.538498481882 2.82659861811785 11 106.2970187 110.387253600208 -4.0902349002078 12 114.5072908 114.495249740464 0.0120410595355431 13 98.0031578 110.740545930446 -12.7373881304458 14 103.0649206 101.318882179634 1.74603842036619 15 100.2879168 107.155253919385 -6.86733711938481 16 104.6066685 113.473071734625 -8.86640323462469 17 111.1544534 113.904342819194 -2.74988941919418 18 104.9874617 106.901425633621 -1.91396393362092 19 109.9284852 102.851424290981 7.07706090901949 20 111.5352466 101.206740294794 10.3285063052064 21 132.4974459 121.681807174417 10.8156387255833 22 100.3436426 108.731231135417 -8.38758853541656 23 123.0983561 116.024879937442 7.07347616255807 24 114.2379493 118.879818885606 -4.6418695856058 25 104.569518 114.757672279102 -10.1881542791023 26 109.0833101 106.551682775248 2.53162732475197 27 106.9843039 116.75067297411 -9.76636907410992 28 133.6769759 124.997232923925 8.67974297607486 29 124.8537197 121.480821938400 3.37289776160027 30 122.5132349 116.584248631983 5.9289862680174 31 116.8013374 113.065057192266 3.73628020773383 32 116.0118882 111.193149297036 4.81873890296378 33 129.7575926 127.395263432452 2.36232916754785 34 125.1973623 114.757984928478 10.4393773715215 35 143.7912139 123.916386026466 19.8748278735339 36 127.9465032 125.062223704553 2.88427949544739 37 130.2962757 123.551156007361 6.74511969263898 38 108.4424631 118.534149661362 -10.0916865613618 39 129.3675118 129.432106003271 -0.0645942032705193 40 143.6797622 134.37460747027 9.30515472973008 41 131.8844618 133.434021119737 -1.54955931973706 42 117.6186496 128.134218580256 -10.5155689802560 43 118.9560695 127.849774358138 -8.89370485813757 44 104.8202842 125.159700906158 -20.3394167061585 45 134.624315 140.345028158509 -5.72071315850896 46 140.401226 128.360685689375 12.0405403106253 47 143.8005015 136.549527328591 7.2509741714094 48 153.4317823 133.886039407665 19.5457428923351 49 153.2924677 126.709142003871 26.5833256961295 50 127.3149438 112.181859091281 15.1330847087191 51 153.5525216 115.541916509875 38.0106050901254 52 136.9276493 126.763938368599 10.163710931401 53 131.7730101 123.336128740793 8.43688135920747 54 144.3391845 114.284072636413 30.0551118635870 55 107.4208229 111.586904761058 -4.16608186105755 56 113.6249652 108.760470386583 4.86449481341694 57 124.2221603 128.397948603612 -4.17578830361216 58 102.0618557 118.980783464848 -16.9189277648482 59 96.36853348 126.477576787294 -30.1090433072935 60 111.6838488 129.484042661712 -17.8001938617122 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0164602011231979 0.0329204022463958 0.983539798876802 17 0.00490423534048509 0.00980847068097018 0.995095764659515 18 0.00773078816436919 0.0154615763287384 0.992269211835631 19 0.00360767850484775 0.0072153570096955 0.996392321495152 20 0.00114720300222862 0.00229440600445725 0.998852796997771 21 0.00255463759854957 0.00510927519709914 0.99744536240145 22 0.00121186982893058 0.00242373965786116 0.99878813017107 23 0.00258897099071304 0.00517794198142608 0.997411029009287 24 0.00099719580818489 0.00199439161636978 0.999002804191815 25 0.000820178105316653 0.00164035621063331 0.999179821894683 26 0.00056857275831295 0.0011371455166259 0.999431427241687 27 0.000752491706442405 0.00150498341288481 0.999247508293558 28 0.00832326819390265 0.0166465363878053 0.991676731806097 29 0.0058385260251215 0.011677052050243 0.994161473974879 30 0.010239044839618 0.020478089679236 0.989760955160382 31 0.00514345429342053 0.0102869085868411 0.99485654570658 32 0.00255688563230979 0.00511377126461959 0.99744311436769 33 0.00113693255927581 0.00227386511855161 0.998863067440724 34 0.000998055077239102 0.00199611015447820 0.99900194492276 35 0.00216047531413558 0.00432095062827116 0.997839524685864 36 0.00099721537356709 0.00199443074713418 0.999002784626433 37 0.00121748848052088 0.00243497696104176 0.998782511519479 38 0.00107713696450453 0.00215427392900906 0.998922863035495 39 0.00153246600827186 0.00306493201654371 0.998467533991728 40 0.000850471020863827 0.00170094204172765 0.999149528979136 41 0.000350207698458927 0.000700415396917854 0.999649792301541 42 0.00143805136995181 0.00287610273990363 0.998561948630048 43 0.00110131670892402 0.00220263341784803 0.998898683291076 44 0.0386942439982345 0.077388487996469 0.961305756001765 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 22 0.758620689655172 NOK 5% type I error level 28 0.96551724137931 NOK 10% type I error level 29 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/10jp1r1258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/10jp1r1258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/1bcq01258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/1bcq01258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/2iine1258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/2iine1258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/3qojl1258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/3qojl1258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/4lqux1258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/4lqux1258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/5bc8b1258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/5bc8b1258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/664o61258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/664o61258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/7nd821258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/7nd821258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/8ssr61258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/8ssr61258731778.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/90oex1258731778.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731810utzgisur5yycwbv/90oex1258731778.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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