month

*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: Wed, 01 Dec 2010 15:54:04 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat.htm/, Retrieved Wed, 01 Dec 2010 16:52:56 +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/2010/Dec/01/t1291218776sf44ziitj6dpkat.htm/}, year = {2010}, } @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 = {2010}, 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:

» Textbox « » Textfile « » CSV «

0 8 17 2 6 -2 3 23 3 7 -4 3 24 1 4 -4 7 27 1 3 -7 4 31 0 0 -9 -4 40 1 6 -13 -6 47 -1 3 -8 8 43 2 1 -13 2 60 2 6 -15 -1 64 0 5 -15 -2 65 1 7 -15 0 65 1 4 -10 10 55 3 3 -12 3 57 3 6 -11 6 57 1 6 -11 7 57 1 5 -17 -4 65 -2 2 -18 -5 69 1 3 -19 -7 70 1 -2 -22 -10 71 -1 -4 -24 -21 71 -4 0 -24 -22 73 -2 1 -20 -16 68 -1 4 -25 -25 65 -5 -3 -22 -22 57 -4 -3 -17 -22 41 -5 0 -9 -19 21 0 6 -11 -21 21 -2 -1 -13 -31 17 -4 0 -11 -28 9 -6 -1 -9 -23 11 -2 1 -7 -17 6 -2 -4 -3 -12 -2 -2 -1 -3 -14 0 1 -1 -6 -18 5 -2 0 -4 -16 3 0 3 -8 -22 7 -1 0 -1 -9 4 2 8 -2 -10 8 3 8 -2 -10 9 2 8 -1 0 14 3 8 1 3 12 4 11 2 2 12 5 13 2 4 7 5 5 -1 -3 15 4 12 1 0 14 5 13 -1 -1 19 6 9 -8 -7 39 4 11 1 2 12 6 7 2 3 11 6 12 -2 -3 17 3 11 -2 -5 16 5 10 -2 0 25 5 13 -2 -3 24 5 14 -6 -7 28 3 10 -4 -7 25 5 13 -5 -7 31 5 12 -2 -4 24 6 13 -1 -3 24 6 17 -5 -6 33 5 15

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 11 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation werkloosheid[t] = + 0.663907484037988 -3.94105757478893indicator[t] + 1.00077195481772economie[t] + 1.03740674497135finaciën[t] + 0.888119561734234spaarvermogen[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) 0.663907484037988 0.4621 1.4367 0.156462 0.078231 indicator -3.94105757478893 0.030998 -127.1389 0 0 economie 1.00077195481772 0.022989 43.5326 0 0 finaciën 1.03740674497135 0.133596 7.7652 0 0 spaarvermogen 0.888119561734234 0.059123 15.0216 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998682888644061 R-squared 0.997367512070446 Adjusted R-squared 0.997176058402843 F-TEST (value) 5209.44583905243 F-TEST (DF numerator) 4 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.22777002196627 Sum Squared Residuals 82.9080574761474 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17 16.0736139829279 0.926386017072126 2 23 20.8773956651227 2.1226043348773 3 24 24.0203386395551 -0.0203386395551447 4 27 27.1353068970918 -0.135306897091792 5 31 32.2543983268314 -1.25439832683136 6 40 38.4964619532442 1.50353804675578 7 47 47.5199761676191 -0.519976167619087 8 43 43.1614767725681 -0.161476772568104 9 60 61.3027307262776 -1.3027307262776 10 64 63.2195969597254 0.780403040274641 11 65 65.0324708733474 -0.0324708733474565 12 65 64.3696560977802 0.630343902219805 13 55 55.8587817002212 -0.85878170022122 14 57 59.3998518512777 -2.39985185127774 15 57 56.3862966509993 0.613703349000727 16 57 56.4989490440828 0.50105095591724 17 65 63.3602240697046 1.63977593029535 18 69 70.3008494863241 -1.30084948632414 19 70 67.7997653428065 2.20023465719354 20 71 72.7695695893089 -1.76956958930891 21 71 70.0834512479147 0.91654875208526 22 73 72.045612344774 0.954387655226047 23 68 65.9877792046986 2.01222079530139 24 65 66.3196555732587 -1.31965557325874 25 57 58.5362054583165 -1.53620545831646 26 41 40.4578695246032 0.542130475396828 27 21 22.4474758860071 -1.44747588600706 28 21 20.0363967038671 0.963603296132854 29 17 16.7240983770593 0.275901622940664 30 9 8.8813660402577 0.118633959742295 31 11 11.9289767681223 -0.928976768122318 32 6 5.6108955387796 0.389104461220395 33 -2 -2.48511630108480 0.485116301084804 34 0 -1.37443997580619 1.37443997580619 35 5 4.22154425610989 0.778455743890106 36 3 3.08014519131288 -0.0801451913128798 37 7 9.13797833138822 -2.13797833138822 38 4 4.77778744928401 -0.777787449284007 39 8 8.75547981422656 -0.755479814226565 40 9 7.71807306925522 1.28192693074478 41 14 14.8221417876148 -0.822141787614839 42 12 13.6441079326642 -1.64410793266419 43 12 11.5159242714974 0.484075728502639 44 7 6.41251168725893 0.587488312741074 45 15 16.4097109150700 -1.40971091506997 46 14 13.4554379366508 0.54456206334915 47 19 17.8217096294454 1.17829037055460 48 39 39.1059065575873 -0.105906557587347 49 12 11.1656712208522 0.834328779147766 50 11 12.6659834095522 -1.66598340955220 51 17 18.4252421831533 -1.42524218315331 52 16 17.6103922017263 -1.61039220172633 53 25 25.2786106610176 -0.278610661017636 54 24 23.1644143582987 0.83558564170129 55 28 29.2982651013039 -1.29826510130391 56 25 26.1553221268715 -1.15532212687145 57 31 29.2082601399261 1.79173986007385 58 24 22.3129295867181 1.68707041328189 59 24 22.9251222136838 1.07487778631616 60 33 32.8733907799466 0.126609220053431 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.00700877725256325 0.0140175545051265 0.992991222747437 9 0.0793967049220026 0.158793409844005 0.920603295077997 10 0.306307986825659 0.612615973651319 0.69369201317434 11 0.197311547925028 0.394623095850055 0.802688452074972 12 0.180442034373561 0.360884068747122 0.819557965626439 13 0.116141486241762 0.232282972483524 0.883858513758238 14 0.410780302190069 0.821560604380138 0.589219697809931 15 0.328756526339701 0.657513052679402 0.671243473660299 16 0.253187880015859 0.506375760031717 0.746812119984142 17 0.324530108140531 0.649060216281061 0.675469891859469 18 0.263561894853235 0.52712378970647 0.736438105146765 19 0.650184735938608 0.699630528122784 0.349815264061392 20 0.736190956386892 0.527618087226216 0.263809043613108 21 0.676523929042788 0.646952141914423 0.323476070957212 22 0.608442561827064 0.783114876345872 0.391557438172936 23 0.690281371224178 0.619437257551645 0.309718628775822 24 0.746021505985733 0.507956988028535 0.253978494014267 25 0.778811726498088 0.442376547003824 0.221188273501912 26 0.737426603249652 0.525146793500697 0.262573396750348 27 0.822543781264088 0.354912437471825 0.177456218735912 28 0.804938715315615 0.39012256936877 0.195061284684385 29 0.74879692494669 0.50240615010662 0.25120307505331 30 0.717767801278603 0.564464397442795 0.282232198721397 31 0.677319349851182 0.645361300297636 0.322680650148818 32 0.625319022726602 0.749361954546796 0.374680977273398 33 0.611949980070961 0.776100039858078 0.388050019929039 34 0.609086366618211 0.781827266763577 0.390913633381789 35 0.706301418800306 0.587397162399388 0.293698581199694 36 0.68254169695653 0.63491660608694 0.31745830304347 37 0.723326672437418 0.553346655125164 0.276673327562582 38 0.666379040875011 0.667241918249978 0.333620959124989 39 0.648657076016113 0.702685847967774 0.351342923983887 40 0.763519220596442 0.472961558807117 0.236480779403558 41 0.726393564856685 0.547212870286631 0.273606435143315 42 0.701109720521819 0.597780558956363 0.298890279478181 43 0.65880220736831 0.682395585263379 0.341197792631690 44 0.637672188462444 0.724655623075112 0.362327811537556 45 0.562288329343428 0.875423341313145 0.437711670656572 46 0.553552469562316 0.892895060875369 0.446447530437684 47 0.491477009698664 0.982954019397327 0.508522990301336 48 0.400365678447675 0.80073135689535 0.599634321552325 49 0.436742023079982 0.873484046159963 0.563257976920018 50 0.431078685454058 0.862157370908116 0.568921314545942 51 0.367369502670951 0.734739005341901 0.632630497329049 52 0.380106748074403 0.760213496148806 0.619893251925597 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 1 0.0222222222222222 OK 10% type I error level 1 0.0222222222222222 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/10cb2v1291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/10cb2v1291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/1g1441291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/1g1441291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/2g1441291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/2g1441291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/39bl71291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/39bl71291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/49bl71291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/49bl71291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/59bl71291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/59bl71291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/6kkla1291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/6kkla1291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/7kkla1291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/7kkla1291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/8cb2v1291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/8cb2v1291218830.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/9cb2v1291218830.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t1291218776sf44ziitj6dpkat/9cb2v1291218830.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 3 ; 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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