With season influences

*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: Sun, 22 Nov 2009 12:19:48 -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/22/t1258917635667wrkkbenonvqb.htm/, Retrieved Sun, 22 Nov 2009 20:20:47 +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/22/t1258917635667wrkkbenonvqb.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:

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User-defined keywords:

Dataseries X:

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17823.2 1.2218 17872 1.249 17420.4 1.2991 16704.4 1.3408 15991.2 1.3119 16583.6 1.3014 19123.5 1.3201 17838.7 1.2938 17209.4 1.2694 18586.5 1.2165 16258.1 1.2037 15141.6 1.2292 19202.1 1.2256 17746.5 1.2015 19090.1 1.1786 18040.3 1.1856 17515.5 1.2103 17751.8 1.1938 21072.4 1.202 17170 1.2271 19439.5 1.277 19795.4 1.265 17574.9 1.2684 16165.4 1.2811 19464.6 1.2727 19932.1 1.2611 19961.2 1.2881 17343.4 1.3213 18924.2 1.2999 18574.1 1.3074 21350.6 1.3242 18594.6 1.3516 19823.1 1.3511 20844.4 1.3419 19640.2 1.3716 17735.4 1.3622 19813.6 1.3896 22160 1.4227 20664.3 1.4684 17877.4 1.457 20906.5 1.4718 21164.1 1.4748 21374.4 1.5527 22952.3 1.5751 21343.5 1.5557 23899.3 1.5553 22392.9 1.577 18274.1 1.4975 22786.7 1.437 22321.5 1.3322 17842.2 1.2732 16373.5 1.3449 15993.8 1.3239 16446.1 1.2785 17729 1.305 16643 1.319 16196.7 1.365 18252.1 1.4016 17570.4 1.4088 15836.8 1.4268

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 EUDO[t] = + 0.688170796119973 + 4.03585428287288e-05UITV[t] -0.178658012241435M1[t] -0.202300754539509M2[t] -0.153327146619087M3[t] -0.0551540419778964M4[t] -0.0856662083483208M5[t] -0.107639433978710M6[t] -0.159787456091427M7[t] -0.087122734055486M8[t] -0.0833698761445767M9[t] -0.150402045585577M10[t] -0.076462993523277M11[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) 0.688170796119973 0.1097 6.2732 0 0 UITV 4.03585428287288e-05 6e-06 6.502 0 0 M1 -0.178658012241435 0.056103 -3.1845 0.002574 0.001287 M2 -0.202300754539509 0.056526 -3.5789 0.000814 0.000407 M3 -0.153327146619087 0.054512 -2.8127 0.007147 0.003574 M4 -0.0551540419778964 0.052647 -1.0476 0.300173 0.150086 M5 -0.0856662083483208 0.053056 -1.6146 0.113081 0.056541 M6 -0.107639433978710 0.053289 -2.0199 0.049113 0.024556 M7 -0.159787456091427 0.056814 -2.8125 0.007152 0.003576 M8 -0.087122734055486 0.053959 -1.6146 0.113091 0.056545 M9 -0.0833698761445767 0.054202 -1.5381 0.130719 0.065359 M10 -0.150402045585577 0.057166 -2.631 0.011477 0.005739 M11 -0.076462993523277 0.054028 -1.4152 0.16359 0.081795 Multiple Linear Regression - Regression Statistics Multiple R 0.709554219020798 R-squared 0.503467189730214 Adjusted R-squared 0.376692855193248 F-TEST (value) 3.97136527333462 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.000309492120214028 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0830072712216853 Sum Squared Residuals 0.32383973255651 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.2218 1.22883116442354 -0.0070311644235426 2 1.249 1.20715791901550 0.0418420809844953 3 1.2991 1.23790560899447 0.0611943910055266 4 1.3408 1.30718199697029 0.0336180030297057 5 1.3119 1.24788611785442 0.0640138821455796 6 1.3014 1.24982129299577 0.0515787070042294 7 1.3201 1.30017993381374 0.019920066186259 8 1.2938 1.32099200002333 -0.0271920000233316 9 1.2694 1.29934722693212 -0.0299472269321219 10 1.2165 1.28789280682056 -0.0713928068205639 11 1.2037 1.26786102776045 -0.064161027760452 12 1.2292 1.29926370821545 -0.0700637082154532 13 1.2256 1.28448155913007 -0.0588815591300718 14 1.2015 1.2020929218905 -0.000592921890499267 15 1.1786 1.30529226795560 -0.126692267955602 16 1.1856 1.36109697433519 -0.175496974335193 17 1.2103 1.30940464468825 -0.0991046446882518 18 1.1938 1.29696814272829 -0.103168142728291 19 1.202 1.37883469793265 -0.176834697932651 20 1.2271 1.29400424243376 -0.0669042424337606 21 1.277 1.38935081329447 -0.11235081329447 22 1.265 1.33668224924621 -0.0716822492462143 23 1.2684 1.32100515695732 -0.0526051569573221 24 1.2811 1.34058278436351 -0.0594827843635058 25 1.2727 1.29507567662261 -0.0223756766226131 26 1.2611 1.29030055309697 -0.0292005530969688 27 1.2881 1.34044859461371 -0.0523485946137073 28 1.3213 1.33297110583785 -0.011671105837852 29 1.2999 1.36625772397108 -0.0663577239710819 30 1.3074 1.33015497249636 -0.0227549724963552 31 1.3242 1.39006244454760 -0.0658624445476028 32 1.3516 1.35149902254757 0.000100977452432218 33 1.3511 1.40483235032357 -0.0537323503235702 34 1.3419 1.37901836067355 -0.0371183606735506 35 1.3716 1.40435765546150 -0.0327576554614957 36 1.3622 1.40394569660461 -0.0417456966046099 37 1.3896 1.30916080806984 0.0804391919301606 38 1.4227 1.38021535066509 0.0424846493349063 39 1.4684 1.36882468607659 0.0995753139234134 40 1.457 1.35452256770839 0.102477432291607 41 1.4718 1.44626046342047 0.025539536579529 42 1.4748 1.43468359842276 0.0401164015772375 43 1.5527 1.39102297786693 0.161677022133073 44 1.5751 1.52736944463232 0.0477305553676808 45 1.5557 1.46619347884037 0.0895065211596305 46 1.5553 1.50230967316103 0.0529903268389657 47 1.577 1.51545261630614 0.0615473836938626 48 1.4975 1.42568684362645 0.071813156373554 49 1.437 1.42915079175393 0.00784920824606702 50 1.3322 1.38673325533193 -0.0545332553319334 51 1.2732 1.25492884235963 0.018271157640369 52 1.3449 1.29382735514827 0.0510726448517322 53 1.3239 1.24799105006577 0.0759089499342250 54 1.2785 1.24427199335682 0.0342280066431797 55 1.305 1.24389994583908 0.0611000541609212 56 1.319 1.27273529036302 0.0462647096369793 57 1.365 1.25847613060947 0.106523869390532 58 1.4016 1.27439691009864 0.127203089901363 59 1.4088 1.32082354351459 0.0879764564854073 60 1.4268 1.32732096718999 0.0994790328100148 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.228370530517195 0.45674106103439 0.771629469482805 17 0.114355677288704 0.228711354577408 0.885644322711296 18 0.0616233384433073 0.123246676886615 0.938376661556693 19 0.0428238347305316 0.0856476694610631 0.957176165269468 20 0.0787840083789208 0.157568016757842 0.92121599162108 21 0.159916087815819 0.319832175631638 0.840083912184181 22 0.192217414941882 0.384434829883763 0.807782585058118 23 0.233895081461073 0.467790162922146 0.766104918538927 24 0.239647119822482 0.479294239644965 0.760352880177518 25 0.227945581582583 0.455891163165166 0.772054418417417 26 0.206625561295256 0.413251122590512 0.793374438704744 27 0.213079131474725 0.42615826294945 0.786920868525275 28 0.194162118806131 0.388324237612262 0.805837881193869 29 0.199502262655855 0.39900452531171 0.800497737344145 30 0.177367295229509 0.354734590459018 0.822632704770491 31 0.28778294799691 0.57556589599382 0.71221705200309 32 0.283796913494943 0.567593826989885 0.716203086505057 33 0.422469589023444 0.844939178046887 0.577530410976556 34 0.573174551232245 0.85365089753551 0.426825448767755 35 0.699885250627805 0.60022949874439 0.300114749372195 36 0.854323588968354 0.291352822063293 0.145676411031646 37 0.885460079743418 0.229079840513164 0.114539920256582 38 0.927551083783906 0.144897832432189 0.0724489162160944 39 0.96416795257407 0.0716640948518594 0.0358320474259297 40 0.966689453346832 0.0666210933063359 0.0333105466531679 41 0.945075069216602 0.109849861566795 0.0549249307833977 42 0.893480671860287 0.213038656279425 0.106519328139713 43 0.990780253239795 0.0184394935204105 0.00921974676020525 44 0.98745989235521 0.0250802152895798 0.0125401076447899 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 2 0.0689655172413793 NOK 10% type I error level 5 0.172413793103448 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/10ka671258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/10ka671258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/11x7f1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/11x7f1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/2fk2b1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/2fk2b1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/3d4791258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/3d4791258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/4bexj1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/4bexj1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/5z1ti1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/5z1ti1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/6b42g1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/6b42g1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/7izci1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/7izci1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/81mdi1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/81mdi1258917583.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/9rmji1258917583.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917635667wrkkbenonvqb/9rmji1258917583.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly 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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