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 06:06:11 -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/t1258808840zm2i4obuujwmsk2.htm/, Retrieved Sat, 21 Nov 2009 14:07:32 +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/t1258808840zm2i4obuujwmsk2.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 1

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 10.5509226624105 -0.0254339243934731X[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) 10.5509226624105 1.045228 10.0944 0 0 X -0.0254339243934731 0.009851 -2.5819 0.012373 0.006187 Multiple Linear Regression - Regression Statistics Multiple R 0.321072294028018 R-squared 0.103087417992414 Adjusted R-squared 0.0876234079578005 F-TEST (value) 6.66627981756805 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.0123734938424256 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.676353286019171 Sum Squared Residuals 26.5323185155180 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.3 8.0507678945322 1.24923210546781 2 9.3 7.69469295302352 1.60530704697648 3 8.7 7.65399867399396 1.04600132600604 4 8.2 7.8498398918237 0.350160108176299 5 8.3 8.0431377172141 0.256862282785904 6 8.5 8.0965489584404 0.40345104155961 7 8.6 8.11180931307647 0.488190686923526 8 8.5 7.91342470280738 0.586575297192617 9 8.2 7.92868505744347 0.271314942556532 10 8.1 7.91342470280738 0.186575297192616 11 7.9 7.69977973790221 0.200220262097791 12 8.6 8.34071463261773 0.259285367382268 13 8.7 8.1346998450306 0.5653001549694 14 8.7 7.70740991522025 0.992590084779748 15 8.5 7.78371168840067 0.71628831159933 16 8.4 7.77099472620393 0.629005273796066 17 8.5 7.97192272891237 0.528077271087628 18 8.7 7.9948132608665 0.705186739133502 19 8.7 7.9897264759878 0.710273524012197 20 8.6 7.66417224375135 0.935827756248653 21 8.5 7.85238328426305 0.647616715736952 22 8.3 7.71249670009895 0.587503299901055 23 8 7.62856474960048 0.371435250399516 24 8.2 8.29747696114883 -0.0974769611488285 25 8.1 8.02024718525997 0.079752814740029 26 8.1 7.62347796472179 0.47652203527821 27 8 7.54971958398072 0.450280416019282 28 7.9 7.71758348497764 0.18241651502236 29 7.9 7.82186257499088 0.07813742500912 30 8 7.87273042377783 0.127269576222173 31 8 7.87273042377783 0.127269576222173 32 7.9 7.55226297642007 0.347737023579935 33 8 7.74810419424981 0.251895805750192 34 7.7 7.69214956058417 0.007850439415833 35 7.2 7.56243654617745 -0.362436546177454 36 7.5 8.18556769381755 -0.685567693817546 37 7.3 7.91851148768608 -0.618511487686078 38 7 7.59295725544962 -0.592957255449622 39 7 7.49885173519377 -0.498851735193772 40 7 7.64382510423657 -0.643825104236568 41 7.2 7.88799077841391 -0.68799077841391 42 7.3 7.7582777640072 -0.458277764007198 43 7.1 7.68706277570547 -0.587062775705473 44 6.8 7.64128171179722 -0.841281711797221 45 6.4 7.6082176100857 -1.20821761008571 46 6.1 7.63365153447918 -1.53365153447918 47 6.5 7.53954601422333 -1.03954601422333 48 7.7 8.13724323746995 -0.437243237469947 49 7.9 7.91088131036804 -0.0108813103680359 50 7.5 7.62347796472179 -0.12347796472179 51 6.9 7.65908545887265 -0.759085458872652 52 6.6 7.90579452548934 -1.30579452548934 53 6.9 8.15250359210603 -1.25250359210603 54 7.7 8.19828465601428 -0.498284656014282 55 8 8.18048090893885 -0.180480908938851 56 8 7.88799077841391 0.112009221586090 57 7.7 8.16013376942407 -0.460133769424073 58 7.3 8.05585467941083 -0.755854679410833 59 7.4 7.93885862720086 -0.538858627200856 60 8.1 8.45516729238836 -0.355167292388361 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.567749281201477 0.864501437597047 0.432250718798523 6 0.407343359745886 0.814686719491772 0.592656640254114 7 0.271336278114378 0.542672556228757 0.728663721885622 8 0.184676999614656 0.369353999229312 0.815323000385344 9 0.158056447600281 0.316112895200563 0.841943552399719 10 0.145893778583485 0.291787557166969 0.854106221416515 11 0.176842669315960 0.353685338631919 0.82315733068404 12 0.117394006176639 0.234788012353278 0.882605993823361 13 0.082926561170128 0.165853122340256 0.917073438829872 14 0.0690466853235581 0.138093370647116 0.930953314676442 15 0.0499895766637498 0.0999791533274995 0.95001042333625 16 0.0362464471071114 0.0724928942142228 0.963753552892889 17 0.0250842682730431 0.0501685365460862 0.974915731726957 18 0.0207385674113659 0.0414771348227319 0.979261432588634 19 0.0182871609041648 0.0365743218083295 0.981712839095835 20 0.0195194156671223 0.0390388313342447 0.980480584332878 21 0.0180630261812731 0.0361260523625462 0.981936973818727 22 0.0185514779927121 0.0371029559854242 0.981448522007288 23 0.0240864141688033 0.0481728283376065 0.975913585831197 24 0.0227667786123451 0.0455335572246903 0.977233221387655 25 0.0233428010131278 0.0466856020262557 0.976657198986872 26 0.0294288491461238 0.0588576982922475 0.970571150853876 27 0.0416144570974553 0.0832289141949106 0.958385542902545 28 0.0553809630278187 0.110761926055637 0.944619036972181 29 0.0690771997390417 0.138154399478083 0.930922800260958 30 0.0807775099504788 0.161555019900958 0.919222490049521 31 0.0962996614507103 0.192599322901421 0.90370033854929 32 0.15966827371593 0.31933654743186 0.84033172628407 33 0.241970188843075 0.483940377686151 0.758029811156925 34 0.342145898615852 0.684291797231703 0.657854101384148 35 0.483303036124154 0.966606072248308 0.516696963875846 36 0.601967791327846 0.796064417344309 0.398032208672154 37 0.669536509876157 0.660926980247685 0.330463490123843 38 0.732438839377179 0.535122321245643 0.267561160622821 39 0.764827382094226 0.470345235811549 0.235172617905774 40 0.777132742535954 0.445734514928091 0.222867257464046 41 0.778662295549767 0.442675408900467 0.221337704450234 42 0.761732496360564 0.476535007278873 0.238267503639436 43 0.742628318608421 0.514743362783159 0.257371681391579 44 0.729532124236992 0.540935751526016 0.270467875763008 45 0.770817863424235 0.458364273151531 0.229182136575765 46 0.902507269030837 0.194985461938326 0.097492730969163 47 0.907731989784093 0.184536020431814 0.0922680102159068 48 0.86832601831054 0.263347963378921 0.131673981689460 49 0.85418178748546 0.291636425029079 0.145818212514540 50 0.839303859341435 0.321392281317129 0.160696140658565 51 0.766361265917734 0.467277468164532 0.233638734082266 52 0.868069975875192 0.263860048249617 0.131930024124808 53 0.96536074348433 0.0692785130313386 0.0346392565156693 54 0.919735317664322 0.160529364671356 0.0802646823356778 55 0.840129167829463 0.319741664341075 0.159870832170537 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 8 0.156862745098039 NOK 10% type I error level 14 0.274509803921569 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/10eu1k1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/10eu1k1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/1vywi1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/1vywi1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/2tv8p1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/2tv8p1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/3txc01258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/3txc01258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/49kvn1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/49kvn1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/5dlwp1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/5dlwp1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/6ro7s1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/6ro7s1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/76xsu1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/76xsu1258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/860281258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/860281258808767.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/9cy7h1258808767.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258808840zm2i4obuujwmsk2/9cy7h1258808767.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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