1e link

*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 08:40:59 -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/t12587317904emzog4nzoan2g5.htm/, Retrieved Fri, 20 Nov 2009 16:43:22 +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/20/t12587317904emzog4nzoan2g5.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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130 87.1 136.7 110.5 138.1 110.8 139.5 104.2 140.4 88.9 144.6 89.8 151.4 90 147.9 93.9 141.5 91.3 143.8 87.8 143.6 99.7 150.5 73.5 150.1 79.2 154.9 96.9 162.1 95.2 176.7 95.6 186.6 89.7 194.8 92.8 196.3 88 228.8 101.1 267.2 92.7 237.2 95.8 254.7 103.8 258.2 81.8 257.9 87.1 269.6 105.9 266.9 108.1 269.6 102.6 253.9 93.7 258.6 103.5 274.2 100.6 301.5 113.3 304.5 102.4 285.1 102.1 287.7 106.9 265.5 87.3 264.1 93.1 276.1 109.1 258.9 120.3 239.1 104.9 250.1 92.6 276.8 109.8 297.6 111.4 295.4 117.9 283 121.6 275.8 117.8 279.7 124.2 254.6 106.8 234.6 102.7 176.9 116.8 148.1 113.6 122.7 96.1 124.9 85 121.6 83.2 128.4 84.9 144.5 83 151.8 79.6 167.1 83.2 173.8 83.8 203.7 82.8 199.8 71.4

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] = -32.1256554641616 + 2.48426090622502X[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) -32.1256554641616 53.365483 -0.602 0.549485 0.274743 X 2.48426090622502 0.54279 4.5768 2.5e-05 1.2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.511874031200764 R-squared 0.262015023817721 Adjusted R-squared 0.249506803882428 F-TEST (value) 20.9474269858676 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 2.48152024050485e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 52.7017608582515 Sum Squared Residuals 163871.060256060 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 130 184.253469468037 -54.2534694680369 2 136.7 242.385174673703 -105.685174673703 3 138.1 243.13045294557 -105.03045294557 4 139.5 226.734330964485 -87.234330964485 5 140.4 188.725139099242 -48.3251390992422 6 144.6 190.960973914845 -46.3609739148447 7 151.4 191.457826096090 -40.0578260960897 8 147.9 201.146443630367 -53.2464436303673 9 141.5 194.687365274182 -53.1873652741822 10 143.8 185.992452102395 -42.1924521023947 11 143.6 215.555156886472 -71.9551568864724 12 150.5 150.467521143377 0.0324788566230207 13 150.1 164.627808308860 -14.5278083088596 14 154.9 208.599226349042 -53.6992263490423 15 162.1 204.375982808460 -42.2759828084598 16 176.7 205.369687170950 -28.6696871709498 17 186.6 190.712547824222 -4.11254782422224 18 194.8 198.413756633520 -3.61375663351975 19 196.3 186.489304283640 9.81069571636031 20 228.8 219.033122155187 9.76687784481263 21 267.2 198.165330542897 69.0346694571027 22 237.2 205.866539352195 31.3334606478052 23 254.7 225.740626601995 28.9593733980051 24 258.2 171.086886665045 87.1131133349554 25 257.9 184.253469468037 73.6465305319628 26 269.6 230.957574505067 38.6424254949325 27 266.9 236.422948498762 30.4770515012375 28 269.6 222.759513514525 46.8404864854751 29 253.9 200.649591449122 53.2504085508777 30 258.6 224.995348330127 33.6046516698726 31 274.2 217.790991702075 56.4090082979251 32 301.5 249.341105211133 52.1588947888674 33 304.5 222.26266133328 82.2373386667201 34 285.1 221.517383061412 63.5826169385876 35 287.7 233.441835411292 54.2581645887075 36 265.5 184.750321649282 80.7496783507178 37 264.1 199.159034905387 64.9409650946128 38 276.1 238.907209404987 37.1927905950125 39 258.9 266.730931554708 -7.8309315547077 40 239.1 228.473313598842 10.6266864011575 41 250.1 197.916904452275 52.1830955477252 42 276.8 240.646192039345 36.153807960655 43 297.6 244.621009489305 52.978990510695 44 295.4 260.768705379768 34.6312946202323 45 283 269.9604707328 13.0395292671998 46 275.8 260.520279289145 15.2797207108549 47 279.7 276.419549088985 3.28045091101474 48 254.6 233.19340932067 21.4065906793300 49 234.6 223.007939605147 11.5920603948526 50 176.9 258.03601838292 -81.1360183829201 51 148.1 250.086383483 -101.986383483 52 122.7 206.611817624062 -83.9118176240623 53 124.9 179.036521564965 -54.1365215649646 54 121.6 174.564851933760 -52.9648519337596 55 128.4 178.788095474342 -50.3880954743422 56 144.5 174.067999752515 -29.5679997525146 57 151.8 165.621512671350 -13.8215126713495 58 167.1 174.564851933760 -7.46485193375964 59 173.8 176.055408477495 -2.25540847749462 60 203.7 173.571147571270 30.1288524287304 61 199.8 145.250573240304 54.5494267596955 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00106925020750271 0.00213850041500542 0.998930749792497 6 0.000326039431802339 0.000652078863604679 0.999673960568198 7 0.000217757268728956 0.000435514537457911 0.999782242731271 8 5.02391465324365e-05 0.000100478293064873 0.999949760853468 9 7.3811039455315e-06 1.4762207891063e-05 0.999992618896054 10 1.02379293373754e-06 2.04758586747507e-06 0.999998976207066 11 1.82001879197195e-07 3.64003758394390e-07 0.99999981799812 12 2.74569353559092e-08 5.49138707118184e-08 0.999999972543065 13 4.02344342416218e-09 8.04688684832436e-09 0.999999995976557 14 5.59512837186938e-09 1.11902567437388e-08 0.999999994404872 15 2.28786827245647e-08 4.57573654491295e-08 0.999999977121317 16 7.60792642944245e-07 1.52158528588849e-06 0.999999239207357 17 9.70363455783111e-06 1.94072691156622e-05 0.999990296365442 18 7.81554872109713e-05 0.000156310974421943 0.99992184451279 19 0.000207595271384531 0.000415190542769062 0.999792404728616 20 0.00414330232410595 0.00828660464821189 0.995856697675894 21 0.0807226848971425 0.161445369794285 0.919277315102857 22 0.127469969317316 0.254939938634632 0.872530030682684 23 0.206113262390441 0.412226524780882 0.793886737609559 24 0.351908128151584 0.703816256303169 0.648091871848416 25 0.458266512874342 0.916533025748684 0.541733487125658 26 0.544476782419903 0.911046435160193 0.455523217580096 27 0.570915504353119 0.858168991293762 0.429084495646881 28 0.59965098393867 0.80069803212266 0.40034901606133 29 0.611611766011718 0.776776467976563 0.388388233988282 30 0.589065556975452 0.821868886049095 0.410934443024548 31 0.613058626593177 0.773882746813647 0.386941373406823 32 0.629778245985218 0.740443508029564 0.370221754014782 33 0.723582771940229 0.552834456119542 0.276417228059771 34 0.748013562717055 0.50397287456589 0.251986437282945 35 0.748780379865453 0.502439240269094 0.251219620134547 36 0.824051189074325 0.351897621851349 0.175948810925675 37 0.855232264962822 0.289535470074355 0.144767735037178 38 0.835517286838167 0.328965426323667 0.164482713161833 39 0.781289442726811 0.437421114546378 0.218710557273189 40 0.721889560382242 0.556220879235516 0.278110439617758 41 0.738254557576292 0.523490884847416 0.261745442423708 42 0.715979686843367 0.568040626313267 0.284020313156633 43 0.751259765571884 0.497480468856231 0.248740234428116 44 0.753112276095156 0.493775447809687 0.246887723904844 45 0.72673202090492 0.546535958190159 0.273267979095079 46 0.728302236119433 0.543395527761133 0.271697763880567 47 0.780670265189504 0.438659469620992 0.219329734810496 48 0.887320457369858 0.225359085260284 0.112679542630142 49 0.96174530943379 0.0765093811324183 0.0382546905662092 50 0.968899693671566 0.0622006126568678 0.0311003063284339 51 0.982480064112384 0.0350398717752314 0.0175199358876157 52 0.970577658952258 0.0588446820954847 0.0294223410477424 53 0.951549772606583 0.0969004547868332 0.0484502273934166 54 0.9485548377456 0.102890324508802 0.0514451622544011 55 0.934676107852178 0.130647784295643 0.0653238921478217 56 0.899846457980227 0.200307084039546 0.100153542019773 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.307692307692308 NOK 5% type I error level 17 0.326923076923077 NOK 10% type I error level 21 0.403846153846154 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/103ahj1258731655.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/103ahj1258731655.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/1np2l1258731654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/1np2l1258731654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/2zdlp1258731654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/2zdlp1258731654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/3kemt1258731654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/3kemt1258731654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/4hbyv1258731654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/4hbyv1258731654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/5o7qv1258731654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/5o7qv1258731654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/64uzu1258731655.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/64uzu1258731655.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/7x8hg1258731655.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/7x8hg1258731655.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/8ufjh1258731655.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/8ufjh1258731655.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/9zi6m1258731655.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587317904emzog4nzoan2g5/9zi6m1258731655.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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