WS 3

*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 07:32:47 -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/t1258814091r751jp843emcme4.htm/, Retrieved Sat, 21 Nov 2009 15:35:03 +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/t1258814091r751jp843emcme4.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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-22 46 -20 50 -17 49 -21 48 -16 50 -11 47 -19 50 -31 49 -36 51 -33 52 -26 48 -38 55 -27 56 -21 43 -17 44 -14 50 -16 49 -16 47 -15 46 -7 50 -9 49 2 53 -6 54 0 56 7 56 4 58 -5 53 2 51 0 52 3 53 10 56 4 54 5 54 7 56 1 59 -8 62 -3 62 -16 73 -22 76 -32 80 -30 77 -32 81 -38 80 -41 80 -46 81 -58 80 -55 77 -48 71 -58 71 -58 64 -68 64 -75 47 -77 41 -75 35 -71 34 -63 33 -61 23 -53 16 -41 16 -35 8 -33 9

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 Econ[t] = -33.2695148235069 + 0.134293788016668Price[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) -33.2695148235069 10.268411 -3.24 0.001966 0.000983 Price 0.134293788016668 0.184634 0.7274 0.469887 0.234943 Multiple Linear Regression - Regression Statistics Multiple R 0.0942715210230083 R-squared 0.00888711967599149 Adjusted R-squared -0.00791140371933063 F-TEST (value) 0.529041717944463 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.469886505641225 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 24.1538239168656 Sum Squared Residuals 34421.0253786101 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 -22 -27.0920005747401 5.09200057474014 2 -20 -26.5548254226735 6.5548254226735 3 -17 -26.6891192106902 9.68911921069017 4 -21 -26.8234129987068 5.82341299870683 5 -16 -26.5548254226735 10.5548254226735 6 -11 -26.9577067867235 15.9577067867235 7 -19 -26.5548254226735 7.5548254226735 8 -31 -26.6891192106902 -4.31088078930983 9 -36 -26.4205316346568 -9.57946836534317 10 -33 -26.2862378466402 -6.71376215335984 11 -26 -26.8234129987068 0.823412998706835 12 -38 -25.8833564825902 -12.1166435174098 13 -27 -25.7490626945735 -1.25093730542651 14 -21 -27.4948819387902 6.49488193879017 15 -17 -27.3605881507735 10.3605881507735 16 -14 -26.5548254226735 12.5548254226735 17 -16 -26.6891192106902 10.6891192106902 18 -16 -26.9577067867235 10.9577067867235 19 -15 -27.0920005747402 12.0920005747402 20 -7 -26.5548254226735 19.5548254226735 21 -9 -26.6891192106902 17.6891192106902 22 2 -26.1519440586235 28.1519440586235 23 -6 -26.0176502706068 20.0176502706068 24 0 -25.7490626945735 25.7490626945735 25 7 -25.7490626945735 32.7490626945735 26 4 -25.4804751185402 29.4804751185402 27 -5 -26.1519440586235 21.1519440586235 28 2 -26.4205316346568 28.4205316346568 29 0 -26.2862378466402 26.2862378466402 30 3 -26.1519440586235 29.1519440586235 31 10 -25.7490626945735 35.7490626945735 32 4 -26.0176502706068 30.0176502706068 33 5 -26.0176502706068 31.0176502706068 34 7 -25.7490626945735 32.7490626945735 35 1 -25.3461813305235 26.3461813305235 36 -8 -24.9432999664735 16.9432999664735 37 -3 -24.9432999664735 21.9432999664735 38 -16 -23.4660682982901 7.46606829829015 39 -22 -23.0631869342401 1.06318693424014 40 -32 -22.5260117821735 -9.47398821782653 41 -30 -22.9288931462235 -7.07110685377652 42 -32 -22.3917179941568 -9.6082820058432 43 -38 -22.5260117821735 -15.4739882178265 44 -41 -22.5260117821735 -18.4739882178265 45 -46 -22.3917179941568 -23.6082820058432 46 -58 -22.5260117821735 -35.4739882178265 47 -55 -22.9288931462235 -32.0711068537765 48 -48 -23.7346558743235 -24.2653441256765 49 -58 -23.7346558743235 -34.2653441256765 50 -58 -24.6747123904401 -33.3252876095599 51 -68 -24.6747123904401 -43.3252876095599 52 -75 -26.9577067867235 -48.0422932132765 53 -77 -27.7634695148235 -49.2365304851765 54 -75 -28.5692322429235 -46.4307677570765 55 -71 -28.7035260309402 -42.2964739690598 56 -63 -28.8378198189568 -34.1621801810432 57 -61 -30.1807576991235 -30.8192423008765 58 -53 -31.1208142152402 -21.8791857847598 59 -41 -31.1208142152402 -9.8791857847598 60 -35 -32.1951645193735 -2.80483548062646 61 -33 -32.0608707313569 -0.939129268643131 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.000757889284408572 0.00151577856881714 0.999242110715591 6 0.00207879413227522 0.00415758826455045 0.997921205867725 7 0.000327518173643545 0.000655036347287089 0.999672481826356 8 0.000854715604137354 0.00170943120827471 0.999145284395863 9 0.00108424757950729 0.00216849515901457 0.998915752420493 10 0.000337530919477169 0.000675061838954338 0.999662469080523 11 0.000116785709564423 0.000233571419128845 0.999883214290436 12 2.93879602855256e-05 5.87759205710513e-05 0.999970612039714 13 1.70641417713899e-05 3.41282835427797e-05 0.999982935858229 14 6.35032678563016e-06 1.27006535712603e-05 0.999993649673214 15 1.53772889845019e-06 3.07545779690038e-06 0.999998462271102 16 1.07792116912813e-06 2.15584233825625e-06 0.99999892207883 17 3.98613206632468e-07 7.97226413264937e-07 0.999999601386793 18 1.10734842006491e-07 2.21469684012982e-07 0.999999889265158 19 2.91644889984556e-08 5.83289779969112e-08 0.99999997083551 20 7.85541257812095e-08 1.57108251562419e-07 0.999999921445874 21 6.70688801262593e-08 1.34137760252519e-07 0.99999993293112 22 1.33408435287851e-06 2.66816870575703e-06 0.999998665915647 23 1.58899846053650e-06 3.17799692107301e-06 0.99999841100154 24 3.19133979311066e-06 6.38267958622131e-06 0.999996808660207 25 1.00780255420579e-05 2.01560510841157e-05 0.999989921974458 26 1.14931161308352e-05 2.29862322616704e-05 0.99998850688387 27 7.51636571093336e-06 1.50327314218667e-05 0.99999248363429 28 1.15509157692112e-05 2.31018315384223e-05 0.99998844908423 29 1.31374482572945e-05 2.6274896514589e-05 0.999986862551743 30 1.97956059912904e-05 3.95912119825809e-05 0.999980204394009 31 5.35162485396946e-05 0.000107032497079389 0.99994648375146 32 9.97889260621418e-05 0.000199577852124284 0.999900211073938 33 0.000251288785967508 0.000502577571935016 0.999748711214032 34 0.000876165718310033 0.00175233143662007 0.99912383428169 35 0.00227732594518255 0.0045546518903651 0.997722674054818 36 0.00559940634016368 0.0111988126803274 0.994400593659836 37 0.0215934004197479 0.0431868008394957 0.978406599580252 38 0.0747349796079234 0.149469959215847 0.925265020392077 39 0.151877389964057 0.303754779928114 0.848122610035943 40 0.217058125213673 0.434116250427346 0.782941874786327 41 0.280638010538565 0.56127602107713 0.719361989461435 42 0.354999060611893 0.709998121223787 0.645000939388107 43 0.417866135351634 0.835732270703269 0.582133864648366 44 0.485338154788014 0.970676309576028 0.514661845211986 45 0.544057372628648 0.911885254742705 0.455942627371352 46 0.560559576009781 0.878880847980438 0.439440423990219 47 0.58135703351782 0.83728593296436 0.41864296648218 48 0.71033138106084 0.579337237878321 0.289668618939161 49 0.807286880365491 0.385426239269017 0.192713119634509 50 0.935792072317004 0.128415855365993 0.0642079276829964 51 0.998422704646782 0.00315459070643605 0.00157729535321802 52 0.999489968677544 0.00102006264491236 0.00051003132245618 53 0.999041491708842 0.00191701658231562 0.00095850829115781 54 0.99763441538848 0.0047311692230382 0.0023655846115191 55 0.991843531958258 0.0163129360834847 0.00815646804174236 56 0.98714657819966 0.0257068436006786 0.0128534218003393 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 35 0.673076923076923 NOK 5% type I error level 39 0.75 NOK 10% type I error level 39 0.75 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/10i4eg1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/10i4eg1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/1rgxq1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/1rgxq1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/2zduk1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/2zduk1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/3qg521258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/3qg521258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/4ki4m1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/4ki4m1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/58rev1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/58rev1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/698oy1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/698oy1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/7wcer1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/7wcer1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/8yz8d1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/8yz8d1258813962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/9rx8w1258813962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258814091r751jp843emcme4/9rx8w1258813962.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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