review 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: Tue, 24 Nov 2009 13:46:35 -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/24/t12590956687zhngm0la306rxq.htm/, Retrieved Tue, 24 Nov 2009 21:47:59 +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/24/t12590956687zhngm0la306rxq.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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1,6 0 1,4 1,7 0 1,6 2 0 1,7 2 0 2 2,1 0 2 2,5 0 2,1 2,5 0 2,5 2,6 0 2,5 2,7 0 2,6 3,7 0 2,7 4 0 3,7 5 0 4 5,1 0 5 5,1 0 5,1 5 0 5,1 5,1 0 5 4,7 0 5,1 4,5 0 4,7 4,5 0 4,5 4,6 0 4,5 4,6 0 4,6 4,6 0 4,6 4,6 0 4,6 5,3 0 4,6 5,4 0 5,3 5,3 0 5,4 5,2 0 5,3 5 0 5,2 4,2 0 5 4,3 0 4,2 4,3 0 4,3 4,3 0 4,3 4 0 4,3 4 0 4 4,1 0 4 4,4 0 4,1 3,6 0 4,4 3,7 0 3,6 3,8 0 3,7 3,3 0 3,8 3,3 0 3,3 3,3 0 3,3 3,5 0 3,3 3,3 0 3,5 3,3 0 3,3 3,4 0 3,3 3,4 0 3,4 5,2 0 3,4 5,3 0 5,2 4,8 1 5,3 5 1 4,8 4,6 1 5 4,6 1 4,6 3,5 1 4,6 3,5 1 3,5

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 IndGez[t] = + 1.42438152443353 -0.167230832567180InvlMex[t] + 0.882141236165582`IndGez-1`[t] -0.982303190498912M1[t] -0.97592854981554M2[t] -0.825357250922293M3[t] -1.09592854981554M4[t] -1.13950030258242M5[t] -1.10542923062600M6[t] -0.924286632839504M7[t] -0.988304098246186M8[t] -1.03830409824619M9[t] -0.719197036437907M10[t] -0.861785876383442M11[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) 1.42438152443353 0.234209 6.0817 0 0 InvlMex -0.167230832567180 0.150408 -1.1118 0.272681 0.136341 `IndGez-1` 0.882141236165582 0.042393 20.8085 0 0 M1 -0.982303190498912 0.215471 -4.5589 4.6e-05 2.3e-05 M2 -0.97592854981554 0.217225 -4.4927 5.6e-05 2.8e-05 M3 -0.825357250922293 0.217242 -3.7992 0.000473 0.000236 M4 -1.09592854981554 0.217225 -5.0451 1e-05 5e-06 M5 -1.13950030258242 0.217368 -5.2423 5e-06 3e-06 M6 -1.10542923062600 0.217907 -5.0729 9e-06 4e-06 M7 -0.924286632839504 0.218548 -4.2292 0.000128 6.4e-05 M8 -0.988304098246186 0.227301 -4.348 8.9e-05 4.4e-05 M9 -1.03830409824619 0.227301 -4.568 4.4e-05 2.2e-05 M10 -0.719197036437907 0.227439 -3.1621 0.002945 0.001472 M11 -0.861785876383442 0.226923 -3.7977 0.000475 0.000237 Multiple Linear Regression - Regression Statistics Multiple R 0.962194952626626 R-squared 0.925819126860156 Adjusted R-squared 0.902298362206059 F-TEST (value) 39.3617784317609 F-TEST (DF numerator) 13 F-TEST (DF denominator) 41 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.320861133855173 Sum Squared Residuals 4.22102655597193 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.6 1.67707606456644 -0.0770760645664356 2 1.7 1.85987895248292 -0.15987895248292 3 2 2.09866437499272 -0.0986643749927234 4 2 2.09273544694915 -0.0927354469491545 5 2.1 2.04916369418227 0.0508363058177288 6 2.5 2.17144888975526 0.328551110244742 7 2.5 2.70544798200798 -0.205447982007984 8 2.6 2.64143051660130 -0.0414305166013019 9 2.7 2.67964464021786 0.0203553597821408 10 3.7 3.0869658256427 0.613034174357303 11 4 3.82651822186274 0.173481778137255 12 5 4.95294646909586 0.0470535309041391 13 5.1 4.85278451476253 0.247215485237469 14 5.1 4.94737327906246 0.15262672093754 15 5 5.09794457795571 -0.0979445779557072 16 5.1 4.7391591554459 0.360840844554098 17 4.7 4.78380152629558 -0.0838015262955758 18 4.5 4.46501610378577 0.0349838962142288 19 4.5 4.46973045433915 0.0302695456608515 20 4.6 4.40571298893247 0.194287011067534 21 4.6 4.44392711254902 0.156072887450976 22 4.6 4.7630341743573 -0.163034174357303 23 4.6 4.62044533441177 -0.0204453344117677 24 5.3 5.48223121079521 -0.18223121079521 25 5.4 5.1174268856122 0.282573114387795 26 5.3 5.21201564991213 0.0879843500878647 27 5.2 5.27437282518882 -0.0743728251888235 28 5 4.91558740267902 0.084412597320982 29 4.2 4.69558740267902 -0.495587402679018 30 4.3 4.02394548570298 0.276054514297019 31 4.3 4.29330220710603 0.00669779289396837 32 4.3 4.22928474169935 0.070715258300651 33 4 4.17928474169935 -0.179284741699349 34 4 4.23374943265795 -0.233749432657954 35 4.1 4.09116059271242 0.0088394072875814 36 4.4 5.04116059271242 -0.641160592712419 37 3.6 4.32349977306318 -0.723499773063182 38 3.7 3.62416142481409 0.0758385751859131 39 3.8 3.86294684732389 -0.0629468473238928 40 3.3 3.68058967204720 -0.380589672047203 41 3.3 3.19594730119753 0.104052698802471 42 3.3 3.23001837315396 0.0699816268460437 43 3.5 3.41116097094045 0.0888390290595506 44 3.3 3.52357175276688 -0.223571752766883 45 3.3 3.29714350553377 0.00285649446623263 46 3.4 3.61625056734205 -0.216250567342046 47 3.4 3.56187585101307 -0.161875851013069 48 5.2 4.42366172739651 0.77633827260349 49 5.3 5.02921276199565 0.270787238004353 50 4.8 4.9565706937284 -0.156570693728398 51 5 4.66607137453885 0.333928625461147 52 4.6 4.57192832287872 0.0280716771212774 53 4.6 4.17550007564561 0.424499924354394 54 3.5 4.20957114760203 -0.709571147602033 55 3.5 3.42035838560639 0.0796416143936131 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.178880341166081 0.357760682332162 0.821119658833919 18 0.181103307737641 0.362206615475283 0.818896692262359 19 0.0977050454092338 0.195410090818468 0.902294954590766 20 0.0513025329271525 0.102605065854305 0.948697467072848 21 0.0231229580446458 0.0462459160892916 0.976877041955354 22 0.13412888835865 0.2682577767173 0.86587111164135 23 0.0854344386100902 0.170868877220180 0.91456556138991 24 0.0551698135868272 0.110339627173654 0.944830186413173 25 0.038480659361674 0.076961318723348 0.961519340638326 26 0.0203123529982687 0.0406247059965375 0.979687647001731 27 0.00972970359330364 0.0194594071866073 0.990270296406696 28 0.00528936708339018 0.0105787341667804 0.99471063291661 29 0.0123146804252546 0.0246293608505092 0.987685319574745 30 0.00973279333350235 0.0194655866670047 0.990267206666498 31 0.00449762448957547 0.00899524897915093 0.995502375510425 32 0.00222755636149520 0.00445511272299039 0.997772443638505 33 0.00123948015100782 0.00247896030201563 0.998760519848992 34 0.00111384365177268 0.00222768730354536 0.998886156348227 35 0.000439794117558488 0.000879588235116976 0.999560205882442 36 0.0233682983587977 0.0467365967175955 0.976631701641202 37 0.195703567348831 0.391407134697662 0.804296432651169 38 0.115294210592324 0.230588421184649 0.884705789407676 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.227272727272727 NOK 5% type I error level 12 0.545454545454545 NOK 10% type I error level 13 0.590909090909091 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/10kj2u1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/10kj2u1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/1rnjk1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/1rnjk1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/26f3v1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/26f3v1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/3lsyv1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/3lsyv1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/4nhcb1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/4nhcb1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/5vemt1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/5vemt1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/6gdkz1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/6gdkz1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/7rwgu1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/7rwgu1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/8vwjq1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/8vwjq1259095591.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/9dieg1259095591.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590956687zhngm0la306rxq/9dieg1259095591.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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