WS 7.5

*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 13:26:33 -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/t1258748852d5neeajxj0xca6o.htm/, Retrieved Fri, 20 Nov 2009 21:27:44 +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/t1258748852d5neeajxj0xca6o.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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96,3 94,0 96,2 100,0 107,2 91,1 96,3 96,2 114,9 93,1 107,2 96,3 92,6 93,9 114,9 107,2 115,0 92,6 92,6 114,9 107,1 94,4 115,0 92,6 117,8 96,3 107,1 115,0 107,4 100,4 117,8 107,1 106,3 101,5 107,4 117,8 114,5 99,4 106,3 107,4 98,0 99,7 114,5 106,3 103,1 101,7 98,0 114,5 100,3 103,7 103,1 98,0 104,6 103,1 100,3 103,1 111,2 101,0 104,6 100,3 105,0 102,3 111,2 104,6 109,9 101,6 105,0 111,2 111,5 99,6 109,9 105,0 132,5 95,7 111,5 109,9 100,3 96,6 132,5 111,5 123,1 96,3 100,3 132,5 114,2 95,4 123,1 100,3 104,6 96,0 114,2 123,1 109,1 96,9 104,6 114,2 107,0 94,9 109,1 104,6 133,7 92,5 107,0 109,1 124,9 94,0 133,7 107,0 122,5 93,5 124,9 133,7 116,8 92,3 122,5 124,9 116,0 90,4 116,8 122,5 129,8 90,4 116,0 116,8 125,2 91,0 129,8 116,0 143,8 89,1 125,2 129,8 127,9 89,7 143,8 125,2 130,3 87,9 127,9 143,8 108,4 85,9 130,3 127,9 129,4 83,2 108,4 130,3 143,7 83,9 129,4 108,4 131,9 83,0 143,7 129,4 117,6 82,8 131,9 143,7 119,0 78,7 117,6 131,9 104,8 77,6 119,0 117,6 134,6 78,5 104,8 11 etc...

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] = + 56.6688184694744 -0.414546054148085X[t] + 0.33464476226903Y1[t] + 0.436658336644705Y2[t] + 13.8710231589707M1[t] + 21.8094757766993M2[t] + 13.4085304100481M3[t] + 4.64011859452247M4[t] + 4.34174638169133M5[t] + 4.82811135206612M6[t] + 23.9529557196014M7[t] + 7.17288389240209M8[t] + 11.3657529755177M9[t] + 16.1462848792535M10[t] + 5.96354837739477M11[t] -0.0685256950949151t + 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) 56.6688184694744 35.495238 1.5965 0.117871 0.058935 X -0.414546054148085 0.284414 -1.4575 0.152403 0.076202 Y1 0.33464476226903 0.141499 2.365 0.022726 0.011363 Y2 0.436658336644705 0.161109 2.7103 0.009692 0.004846 M1 13.8710231589707 7.787796 1.7811 0.082125 0.041063 M2 21.8094757766993 8.076136 2.7005 0.009938 0.004969 M3 13.4085304100481 7.859373 1.7061 0.095383 0.047692 M4 4.64011859452247 7.653853 0.6062 0.547614 0.273807 M5 4.34174638169133 7.707779 0.5633 0.57623 0.288115 M6 4.82811135206612 7.872739 0.6133 0.543005 0.271503 M7 23.9529557196014 8.033431 2.9817 0.004755 0.002378 M8 7.17288389240209 8.335585 0.8605 0.394392 0.197196 M9 11.3657529755177 7.779751 1.4609 0.151473 0.075737 M10 16.1462848792535 8.021862 2.0128 0.050577 0.025289 M11 5.96354837739477 8.106628 0.7356 0.466039 0.23302 t -0.0685256950949151 0.145264 -0.4717 0.63956 0.31978 Multiple Linear Regression - Regression Statistics Multiple R 0.783083562088961 R-squared 0.613219865213936 Adjusted R-squared 0.475084102790342 F-TEST (value) 4.43925493626692 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 6.60816942024134e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.3831582891788 Sum Squared Residuals 5442.20429073303 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.3 107.362646638182 -11.0626466381815 2 107.2 114.808919914822 -7.6089199148215 3 114.9 109.201650487176 5.69834951282385 4 92.6 107.369416672136 -14.7694166721359 5 115 103.441119628167 11.5588803718328 6 107.1 100.87133777363 6.22866222637008 7 117.8 126.277472062105 -8.47747206210496 8 107.4 107.860333814589 -0.460333814589034 9 106.3 112.720615217547 -6.42061521754726 10 114.5 113.393812200298 1.10618779970174 11 98 105.281949067397 -7.28194906739706 12 103.1 96.4797426696588 6.6202573303412 13 100.3 103.954973758173 -3.65497375817286 14 104.6 113.36358049583 -8.7635804958301 15 111.2 105.980985282947 5.21901471705336 16 105 100.691424180481 4.30857581951862 17 109.9 101.421856006246 8.47814399375395 18 111.5 101.601265037743 9.89873496225682 19 132.5 124.949370790551 7.55062920944943 20 100.3 115.453875165804 -15.1538751658042 21 123.1 118.096846094545 5.00315390545463 22 114.2 116.751445891694 -2.55144589169392 23 104.6 113.228927753556 -8.62892775355634 24 109.1 99.7249133184128 9.37508668158718 25 107 111.670484289006 -4.67048428900626 26 133.7 121.797530255732 11.9024697442685 27 124.9 120.724272758393 4.17572724160745 28 122.5 120.808511955292 1.69148804470785 29 116.8 116.293328520425 0.506671479575255 30 116 114.543350145705 1.45664985429479 31 129.8 130.843000489456 -1.04300048945553 32 125.2 118.014446384669 7.18555361533069 33 143.8 127.412946414831 16.3870535851693 34 127.9 136.091989220621 -8.1919892206211 35 130.3 129.387903262648 0.912096737352066 36 108.4 118.045201175249 -9.6452011752493 37 129.4 126.686232699580 2.71376730041953 38 143.7 131.730699819441 11.9693001805589 39 131.9 137.589565376414 -5.68956537641419 40 117.6 131.130943095868 -13.5309430958680 41 119 122.525695537094 -3.52569553709440 42 104.8 117.623823925095 -12.8238239250945 43 134.6 132.166417195884 2.43358280411601 44 140.4 119.048230603437 21.3517693965628 45 143.8 138.581932704193 5.21806729580663 46 153.4 145.264710635081 8.13528936491888 47 153.3 138.301219916399 14.9987800836013 48 127.3 133.650142836679 -6.35014283667909 49 153.6 136.925662615059 16.6743373849411 50 136.9 144.399269514176 -7.49926951417574 51 131.8 141.203526095070 -9.40352609507046 52 144.3 121.999704096223 22.3002959037774 53 107.4 124.418000308068 -17.0180003080676 54 113.6 118.360223117827 -4.76022311782716 55 124.2 124.663739462005 -0.46373946200495 56 102.1 115.023114031500 -12.9231140315002 57 96.4 116.587659568883 -20.1876595688832 58 111.7 110.198042052306 1.50195794769441 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0517249140379958 0.103449828075992 0.948275085962004 20 0.0217229741926588 0.0434459483853176 0.978277025807341 21 0.0102476506618771 0.0204953013237541 0.989752349338123 22 0.00319409762150188 0.00638819524300376 0.996805902378498 23 0.00122580835889825 0.0024516167177965 0.998774191641102 24 0.000408308577141472 0.000816617154282944 0.999591691422859 25 0.000153923004417758 0.000307846008835515 0.999846076995582 26 0.00720553289136122 0.0144110657827224 0.992794467108639 27 0.00732987014190539 0.0146597402838108 0.992670129858095 28 0.00491435581732351 0.00982871163464702 0.995085644182677 29 0.002672551326406 0.005345102652812 0.997327448673594 30 0.00262313447920807 0.00524626895841614 0.997376865520792 31 0.00165669049793232 0.00331338099586464 0.998343309502068 32 0.000801877298457971 0.00160375459691594 0.999198122701542 33 0.00274788983262078 0.00549577966524156 0.99725211016738 34 0.00171160563120588 0.00342321126241176 0.998288394368794 35 0.00229872291849142 0.00459744583698285 0.997701277081509 36 0.00376489421458175 0.0075297884291635 0.996235105785418 37 0.00250308357520116 0.00500616715040231 0.997496916424799 38 0.00143892628193705 0.00287785256387411 0.998561073718063 39 0.00219574019336480 0.00439148038672961 0.997804259806635 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.761904761904762 NOK 5% type I error level 20 0.952380952380952 NOK 10% type I error level 20 0.952380952380952 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/10ncft1258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/10ncft1258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/1rd0w1258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/1rd0w1258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/2xnga1258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/2xnga1258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/3aca01258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/3aca01258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/44dda1258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/44dda1258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/5z1jm1258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/5z1jm1258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/6o7uu1258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/6o7uu1258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/7ks491258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/7ks491258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/8cx871258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/8cx871258748789.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/9r3o41258748789.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258748852d5neeajxj0xca6o/9r3o41258748789.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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