ws 7 1

*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 09:44:49 -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/t1258735581fwj3iip026pmzvr.htm/, Retrieved Fri, 20 Nov 2009 17:46:34 +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/t1258735581fwj3iip026pmzvr.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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29.837 0 29.571 0 30.167 0 30.524 0 30.996 0 31.033 0 31.198 0 30.937 0 31.649 0 33.115 0 34.106 0 33.926 0 33.382 0 32.851 0 32.948 0 36.112 0 36.113 0 35.210 0 35.193 0 34.383 0 35.349 0 37.058 0 38.076 0 36.630 0 36.045 0 35.638 0 35.114 0 35.465 0 35.254 0 35.299 0 35.916 0 36.683 0 37.288 0 38.536 0 38.977 0 36.407 0 34.955 0 34.951 0 32.680 0 34.791 0 34.178 0 35.213 0 34.871 0 35.299 0 35.443 0 37.108 0 36.419 0 34.471 0 33.868 0 34.385 0 33.643 0 34.627 0 32.919 0 35.500 0 36.110 0 37.086 1 37.711 1 40.427 1 39.884 1 38.512 1 38.767 1

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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation saldo_zichtrek[t] = + 34.5166727272727 + 4.21449393939394crisis[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) 34.5166727272727 0.287777 119.9426 0 0 crisis 4.21449393939394 0.917581 4.593 2.3e-05 1.2e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.513210100478173 R-squared 0.263384607232816 Adjusted R-squared 0.250899600575745 F-TEST (value) 21.0960726307380 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 2.34371282262780e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.13420768826542 Sum Squared Residuals 268.735704942424 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 29.837 34.5166727272727 -4.67967272727266 2 29.571 34.5166727272727 -4.94567272727273 3 30.167 34.5166727272727 -4.34967272727273 4 30.524 34.5166727272727 -3.99267272727273 5 30.996 34.5166727272727 -3.52067272727273 6 31.033 34.5166727272727 -3.48367272727273 7 31.198 34.5166727272727 -3.31867272727273 8 30.937 34.5166727272727 -3.57967272727273 9 31.649 34.5166727272727 -2.86767272727273 10 33.115 34.5166727272727 -1.40167272727273 11 34.106 34.5166727272727 -0.410672727272726 12 33.926 34.5166727272727 -0.590672727272726 13 33.382 34.5166727272727 -1.13467272727273 14 32.851 34.5166727272727 -1.66567272727273 15 32.948 34.5166727272727 -1.56867272727273 16 36.112 34.5166727272727 1.59532727272727 17 36.113 34.5166727272727 1.59632727272727 18 35.21 34.5166727272727 0.693327272727273 19 35.193 34.5166727272727 0.67632727272727 20 34.383 34.5166727272727 -0.133672727272725 21 35.349 34.5166727272727 0.832327272727269 22 37.058 34.5166727272727 2.54132727272727 23 38.076 34.5166727272727 3.55932727272727 24 36.63 34.5166727272727 2.11332727272727 25 36.045 34.5166727272727 1.52832727272727 26 35.638 34.5166727272727 1.12132727272727 27 35.114 34.5166727272727 0.597327272727269 28 35.465 34.5166727272727 0.948327272727276 29 35.254 34.5166727272727 0.73732727272727 30 35.299 34.5166727272727 0.782327272727272 31 35.916 34.5166727272727 1.39932727272727 32 36.683 34.5166727272727 2.16632727272727 33 37.288 34.5166727272727 2.77132727272727 34 38.536 34.5166727272727 4.01932727272727 35 38.977 34.5166727272727 4.46032727272727 36 36.407 34.5166727272727 1.89032727272727 37 34.955 34.5166727272727 0.438327272727270 38 34.951 34.5166727272727 0.434327272727273 39 32.68 34.5166727272727 -1.83667272727273 40 34.791 34.5166727272727 0.274327272727269 41 34.178 34.5166727272727 -0.338672727272731 42 35.213 34.5166727272727 0.696327272727273 43 34.871 34.5166727272727 0.354327272727274 44 35.299 34.5166727272727 0.782327272727272 45 35.443 34.5166727272727 0.92632727272727 46 37.108 34.5166727272727 2.59132727272727 47 36.419 34.5166727272727 1.90232727272727 48 34.471 34.5166727272727 -0.0456727272727315 49 33.868 34.5166727272727 -0.648672727272726 50 34.385 34.5166727272727 -0.13167272727273 51 33.643 34.5166727272727 -0.873672727272727 52 34.627 34.5166727272727 0.110327272727274 53 32.919 34.5166727272727 -1.59767272727273 54 35.5 34.5166727272727 0.983327272727272 55 36.11 34.5166727272727 1.59332727272727 56 37.086 38.7311666666667 -1.64516666666667 57 37.711 38.7311666666667 -1.02016666666667 58 40.427 38.7311666666667 1.69583333333333 59 39.884 38.7311666666667 1.15283333333333 60 38.512 38.7311666666667 -0.219166666666666 61 38.767 38.7311666666667 0.0358333333333365 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0626829249541198 0.125365849908240 0.93731707504588 6 0.0425476041584414 0.0850952083168828 0.957452395841559 7 0.0335711086672994 0.0671422173345989 0.9664288913327 8 0.0214191998969079 0.0428383997938157 0.978580800103092 9 0.0314467435808462 0.0628934871616924 0.968553256419154 10 0.195929313880225 0.39185862776045 0.804070686119775 11 0.548227536309479 0.903544927381042 0.451772463690521 12 0.705799848517888 0.588400302964225 0.294200151482112 13 0.749661470500532 0.500677058998937 0.250338529499468 14 0.766007026929998 0.467985946140004 0.233992973070002 15 0.788132348790623 0.423735302418754 0.211867651209377 16 0.952303259761956 0.0953934804760876 0.0476967402380438 17 0.984868633872509 0.0302627322549829 0.0151313661274914 18 0.988424289531949 0.0231514209361024 0.0115757104680512 19 0.98979121083946 0.0204175783210785 0.0102087891605392 20 0.988277652861986 0.0234446942760281 0.0117223471380140 21 0.988634663265912 0.0227306734681756 0.0113653367340878 22 0.99562199029027 0.00875601941945832 0.00437800970972916 23 0.99934114998921 0.00131770002158000 0.000658850010790001 24 0.99948844091538 0.00102311816923886 0.000511559084619431 25 0.99937896943544 0.00124206112911999 0.000621030564559994 26 0.999081993547636 0.00183601290472694 0.00091800645236347 27 0.99848833046609 0.00302333906782059 0.00151166953391029 28 0.997648640820287 0.00470271835942593 0.00235135917971297 29 0.996241276135172 0.00751744772965543 0.00375872386482772 30 0.994088222102705 0.0118235557945894 0.00591177789729468 31 0.991808248211715 0.0163835035765698 0.00819175178828492 32 0.991450428436195 0.0170991431276096 0.00854957156380479 33 0.993668281443066 0.0126634371138674 0.00633171855693368 34 0.998773609428611 0.00245278114277755 0.00122639057138877 35 0.99995832955128 8.33408974416619e-05 4.16704487208309e-05 36 0.999956403416268 8.71931674632327e-05 4.35965837316164e-05 37 0.999897893580323 0.000204212839354634 0.000102106419677317 38 0.999768535888144 0.000462928223711566 0.000231464111855783 39 0.999868165313213 0.000263669373573840 0.000131834686786920 40 0.99969404446581 0.000611911068378223 0.000305955534189112 41 0.999413214068093 0.00117357186381343 0.000586785931906717 42 0.998745920588225 0.00250815882354923 0.00125407941177462 43 0.997353017491479 0.00529396501704243 0.00264698250852122 44 0.994798578598467 0.0104028428030652 0.0052014214015326 45 0.99045595115564 0.0190880976887180 0.00954404884435901 46 0.995017729196032 0.00996454160793606 0.00498227080396803 47 0.99615164102922 0.0076967179415577 0.00384835897077885 48 0.99150163676243 0.0169967264751391 0.00849836323756957 49 0.983814096790247 0.0323718064195052 0.0161859032097526 50 0.967449251517028 0.065101496965944 0.032550748482972 51 0.949798582060281 0.100402835879438 0.0502014179397191 52 0.906582440518672 0.186835118962657 0.0934175594813285 53 0.948127105495879 0.103745789008243 0.0518728945041213 54 0.897294372831929 0.205411254336142 0.102705627168071 55 0.803048279296365 0.39390344140727 0.196951720703635 56 0.804979680346282 0.390040639307436 0.195020319653718 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 20 0.384615384615385 NOK 5% type I error level 34 0.653846153846154 NOK 10% type I error level 39 0.75 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/10mt691258735482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/10mt691258735482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/1m50l1258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/1m50l1258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/2y9zv1258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/2y9zv1258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/3ee5l1258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/3ee5l1258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/4bqcg1258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/4bqcg1258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/5sfo91258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/5sfo91258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/6x6y31258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/6x6y31258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/73o1h1258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/73o1h1258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/8glph1258735481.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/8glph1258735481.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/9czr01258735482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258735581fwj3iip026pmzvr/9czr01258735482.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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