Workshop 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: Sat, 21 Nov 2009 06:42:15 -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/21/t1258810989bneo1scsea3416e.htm/, Retrieved Sat, 21 Nov 2009 14:43:22 +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/21/t1258810989bneo1scsea3416e.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:

WS 7 part 2

Dataseries X:

» Textbox « » Textfile « » CSV «

9.3 98.3 9.3 112.3 8.7 113.9 8.2 106.2 8.3 98.6 8.5 96.5 8.6 95.9 8.5 103.7 8.2 103.1 8.1 103.7 7.9 112.1 8.6 86.9 8.7 95 8.7 111.8 8.5 108.8 8.4 109.3 8.5 101.4 8.7 100.5 8.7 100.7 8.6 113.5 8.5 106.1 8.3 111.6 8 114.9 8.2 88.6 8.1 99.5 8.1 115.1 8 118 7.9 111.4 7.9 107.3 8 105.3 8 105.3 7.9 117.9 8 110.2 7.7 112.4 7.2 117.5 7.5 93 7.3 103.5 7 116.3 7 120 7 114.3 7.2 104.7 7.3 109.8 7.1 112.6 6.8 114.4 6.4 115.7 6.1 114.7 6.5 118.4 7.7 94.9 7.9 103.8 7.5 115.1 6.9 113.7 6.6 104 6.9 94.3 7.7 92.5 8 93.2 8 104.7 7.7 94 7.3 98.1 7.4 102.7 8.1 82.4

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 Y[t] = + 12.1462142797098 -0.0462787604274321X[t] + 0.742587338241901M1[t] + 1.25511786026870M2[t] + 0.990289718193554M3[t] + 0.520021757297351M4[t] + 0.299973001171929M5[t] + 0.564238222626601M6[t] + 0.63293105409161M7[t] + 0.943323526066729M8[t] + 0.511004148721019M9[t] + 0.356519722495564M10[t] + 0.488839099841274M11[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) 12.1462142797098 1.55747 7.7987 0 0 X -0.0462787604274321 0.017123 -2.7027 0.009541 0.004771 M1 0.742587338241901 0.47363 1.5679 0.123622 0.061811 M2 1.25511786026870 0.610256 2.0567 0.045289 0.022645 M3 0.990289718193554 0.61944 1.5987 0.116592 0.058296 M4 0.520021757297351 0.552833 0.9406 0.351694 0.175847 M5 0.299973001171929 0.482362 0.6219 0.537024 0.268512 M6 0.564238222626601 0.47989 1.1758 0.24561 0.122805 M7 0.63293105409161 0.484441 1.3065 0.197734 0.098867 M8 0.943323526066729 0.572327 1.6482 0.105976 0.052988 M9 0.511004148721019 0.520697 0.9814 0.331428 0.165714 M10 0.356519722495564 0.54307 0.6565 0.514711 0.257355 M11 0.488839099841274 0.598388 0.8169 0.418092 0.209046 Multiple Linear Regression - Regression Statistics Multiple R 0.496313832173041 R-squared 0.24632742000629 Adjusted R-squared 0.0539003783057682 F-TEST (value) 1.28010812736837 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.261695716134239 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.68873946915187 Sum Squared Residuals 22.2950166492772 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.3 8.33959946793523 0.960400532064768 2 9.3 8.20422734397793 1.09577265602207 3 8.7 7.86535318521888 0.834646814781115 4 8.2 7.7514316796139 0.448568320386093 5 8.3 7.88310150273697 0.416898497263031 6 8.5 8.24455212108925 0.25544787891075 7 8.6 8.34101220881072 0.258987791189283 8 8.5 8.29043034945187 0.209569650548135 9 8.2 7.88587822836262 0.314121771637384 10 8.1 7.7036265458807 0.396373454119299 11 7.9 7.44720433563598 0.452795664364019 12 8.6 8.124589998566 0.475410001434004 13 8.7 8.4923193773457 0.207680622654303 14 8.7 8.22736672419164 0.472633275808357 15 8.5 8.10137486339879 0.398625136601213 16 8.4 7.60796752228887 0.792032477711132 17 8.5 7.75352097354016 0.74647902645984 18 8.7 8.05943707937952 0.640562920620478 19 8.7 8.11887415875904 0.581125841240957 20 8.6 7.83689849726303 0.763101502736969 21 8.5 7.74704194708032 0.752958052919681 22 8.3 7.33802433850399 0.961975661496013 23 8 7.31762380643917 0.682376193560829 24 8.2 8.04591610583936 0.154083894160637 25 8.1 8.28406495542225 -0.184064955422253 26 8.1 8.07464681478112 0.0253531852188832 27 8 7.67561026746641 0.324389732533588 28 7.9 7.51078212539126 0.38921787460874 29 7.9 7.48047628701831 0.419523712981690 30 8 7.83729902932785 0.162700970672153 31 8 7.90599186079285 0.0940081392071448 32 7.9 7.63327195138233 0.266728048617671 33 8 7.55729902932785 0.442700970672153 34 7.7 7.30100133016204 0.398998669837959 35 7.2 7.19729902932785 0.00270097067215272 36 7.5 7.84228955995866 -0.342289559958661 37 7.3 8.09894991371252 -0.798949913712524 38 7 8.0191123022682 -1.01911230226820 39 7 7.58305274661155 -0.583052746611547 40 7 7.37657372015171 -0.376573720151707 41 7.2 7.60080106412963 -0.400801064129633 42 7.3 7.6290446074044 -0.329044607404403 43 7.1 7.5681569096726 -0.468156909672601 44 6.8 7.79524761287834 -0.995247612878342 45 6.4 7.30276584697697 -0.90276584697697 46 6.1 7.19456018117895 -1.09456018117895 47 6.5 7.15564814494316 -0.655648144943159 48 7.7 7.75435991514654 -0.054359915146539 49 7.9 8.0850662855843 -0.185066285584294 50 7.5 8.07464681478112 -0.574646814781117 51 6.9 7.87460893730437 -0.97460893730437 52 6.6 7.85324495255426 -1.25324495255426 53 6.9 8.08210017257493 -1.18210017257493 54 7.7 8.42966716279898 -0.729667162798978 55 8 8.46596486196478 -0.465964861964783 56 8 8.24415158902443 -0.244151589024433 57 7.7 8.30701494825225 -0.607014948252247 58 7.3 7.96278760427432 -0.662787604274321 59 7.4 7.88222468365384 -0.482224683653842 60 8.1 8.33284442048944 -0.232844420489441 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0874404191120598 0.174880838224120 0.91255958088794 17 0.0326868477491820 0.0653736954983639 0.967313152250818 18 0.0119940871120098 0.0239881742240196 0.98800591288799 19 0.00523987759258985 0.0104797551851797 0.99476012240741 20 0.00364534707904228 0.00729069415808455 0.996354652920958 21 0.00183800137968609 0.00367600275937218 0.998161998620314 22 0.00103944891931884 0.00207889783863769 0.998960551080681 23 0.000480360990344215 0.00096072198068843 0.999519639009656 24 0.000533989305006299 0.00106797861001260 0.999466010694994 25 0.0111578030685698 0.0223156061371395 0.98884219693143 26 0.0417669272958461 0.0835338545916923 0.958233072704154 27 0.0598655982300874 0.119731196460175 0.940134401769913 28 0.0742342116063968 0.148468423212794 0.925765788393603 29 0.0924425217094447 0.184885043418889 0.907557478290555 30 0.087223329168826 0.174446658337652 0.912776670831174 31 0.0770876444687386 0.154175288937477 0.922912355531261 32 0.0764994098945029 0.152998819789006 0.923500590105497 33 0.129095029980392 0.258190059960784 0.870904970019608 34 0.333293359965631 0.666586719931261 0.66670664003437 35 0.385972351917548 0.771944703835096 0.614027648082452 36 0.368561944684645 0.73712388936929 0.631438055315355 37 0.538692066287264 0.922615867425472 0.461307933712736 38 0.720550036079432 0.558899927841136 0.279449963920568 39 0.70897841189667 0.582043176206661 0.291021588103331 40 0.818158816567777 0.363682366864446 0.181841183432223 41 0.915855456554655 0.168289086890689 0.0841445434453446 42 0.929204074257457 0.141591851485087 0.0707959257425433 43 0.879771314439204 0.240457371121591 0.120228685560796 44 0.956214488106274 0.0875710237874524 0.0437855118937262 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.172413793103448 NOK 5% type I error level 8 0.275862068965517 NOK 10% type I error level 11 0.379310344827586 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/102v6o1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/102v6o1258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/169gc1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/169gc1258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/2dw471258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/2dw471258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/3tmfs1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/3tmfs1258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/4mt1o1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/4mt1o1258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/5bnmc1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/5bnmc1258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/6t6go1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/6t6go1258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/7zjj21258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/7zjj21258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/8h9s81258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/8h9s81258810930.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/9xsyc1258810930.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258810989bneo1scsea3416e/9xsyc1258810930.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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