Model 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: Thu, 19 Nov 2009 14:05:35 -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/19/t1258665033sijkujlazqqa5gk.htm/, Retrieved Thu, 19 Nov 2009 22:10:46 +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/19/t1258665033sijkujlazqqa5gk.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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4.3 96.2 4.1 96.8 3.9 109.9 3.8 88 3.7 91.1 3.7 106.4 4.1 68.6 4.1 100.1 3.8 108 3.7 106 3.5 108.6 3.6 91.5 4.1 99.2 3.8 98 3.7 96.6 3.6 102.8 3.3 96.9 3.4 110 3.7 70.5 3.7 101.9 3.4 109.6 3.3 107.8 3 113 3 93.8 3.3 108 3 102.8 2.9 116.3 2.8 89.2 2.5 106.7 2.6 112.1 2.8 74.2 2.7 108.8 2.4 111.5 2.2 118.8 2.1 118.9 2.1 97.6 2.3 116.4 2.1 107.9 2 121.2 1.9 97.9 1.7 113.4 1.8 117.6 2.1 79.6 2 115.9 1.8 115.7 1.7 129.1 1.6 123.3 1.6 96.7 1.8 121.2 1.7 118.2 1.7 102.1 1.5 125.4 1.5 116.7 1.5 121.3 1.8 85.3 1.8 114.2 1.7 124.4 1.7 131 1.8 118.3 2 99.6

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation unempl[t] = + 6.34498582502308 -0.0343223938350794proman[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) 6.34498582502308 0.775903 8.1775 0 0 proman -0.0343223938350794 0.007272 -4.7196 1.5e-05 8e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.526765475227061 R-squared 0.277481865891191 Adjusted R-squared 0.265024656682419 F-TEST (value) 22.2748017827126 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.53518107681716e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.771732380728758 Sum Squared Residuals 34.543110312986 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.3 3.04317153808848 1.25682846191152 2 4.1 3.0225781017874 1.0774218982126 3 3.9 2.57295474254786 1.32704525745214 4 3.8 3.3246151675361 0.475384832463903 5 3.7 3.21821574664735 0.48178425335265 6 3.7 2.69308312097064 1.00691687902936 7 4.1 3.99046960793664 0.109530392063363 8 4.1 2.90931420213164 1.19068579786836 9 3.8 2.63816729083451 1.16183270916549 10 3.7 2.70681207850467 0.993187921495333 11 3.5 2.61757385453346 0.882426145466539 12 3.6 3.20448678911332 0.395513210886681 13 4.1 2.94020435658321 1.15979564341679 14 3.8 2.98139122918530 0.818608770814697 15 3.7 3.02944258055441 0.670557419445586 16 3.6 2.81664373877692 0.783356261223078 17 3.3 3.01914586240389 0.28085413759611 18 3.4 2.56952250316435 0.83047749683565 19 3.7 3.92525705964999 -0.225257059649985 20 3.7 2.84753389322849 0.852466106771507 21 3.4 2.58325146069838 0.816748539301618 22 3.3 2.64503176960153 0.654968230398475 23 3 2.46655532165911 0.533444678340888 24 3 3.12554528329264 -0.125545283292636 25 3.3 2.63816729083451 0.661832709165491 26 3 2.81664373877692 0.183356261223078 27 2.9 2.35329142200335 0.54670857799665 28 2.8 3.283428294934 -0.483428294934001 29 2.5 2.68278640282011 -0.182786402820112 30 2.6 2.49744547611068 0.102554523889316 31 2.8 3.79826420246019 -0.998264202460192 32 2.7 2.61070937576645 0.0892906242335546 33 2.4 2.51803891241173 -0.118038912411731 34 2.2 2.26748543741565 -0.0674854374156516 35 2.1 2.26405319803214 -0.164053198032144 36 2.1 2.99512018671933 -0.895120186719335 37 2.3 2.34985918261984 -0.0498591826198422 38 2.1 2.64159953021802 -0.541599530218017 39 2 2.18511169221146 -0.185111692211461 40 1.9 2.98482346856881 -1.08482346856881 41 1.7 2.45282636412508 -0.75282636412508 42 1.8 2.30867231001775 -0.508672310017747 43 2.1 3.61292327575076 -1.51292327575076 44 2 2.36702037953738 -0.367020379537382 45 1.8 2.3738848583044 -0.573884858304398 46 1.7 1.91396478091433 -0.213964780914334 47 1.6 2.11303466515779 -0.513034665157795 48 1.6 3.02601034117091 -1.42601034117091 49 1.8 2.18511169221146 -0.385111692211461 50 1.7 2.2880788737167 -0.588078873716699 51 1.7 2.84066941446148 -1.14066941446148 52 1.5 2.04095763810413 -0.540957638104128 53 1.5 2.33956246446932 -0.839562464469318 54 1.5 2.18167945282795 -0.681679452827953 55 1.8 3.41728563089081 -1.61728563089081 56 1.8 2.42536844905702 -0.625368449057017 57 1.7 2.07528003193921 -0.375280031939207 58 1.7 1.84875223262768 -0.148752232627683 59 1.8 2.28464663433319 -0.484646634333191 60 2 2.92647539904918 -0.926475399049176 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0601185455434243 0.120237091086849 0.939881454456576 6 0.0310502255480759 0.0621004510961519 0.968949774451924 7 0.0100946007740844 0.0201892015481689 0.989905399225916 8 0.00444119130951941 0.00888238261903883 0.99555880869048 9 0.00172251221872773 0.00344502443745546 0.998277487781272 10 0.000834466059716255 0.00166893211943251 0.999165533940284 11 0.000791550987824027 0.00158310197564805 0.999208449012176 12 0.000636336560918593 0.00127267312183719 0.999363663439081 13 0.000544990926566207 0.00108998185313241 0.999455009073434 14 0.000282590650565044 0.000565181301130088 0.999717409349435 15 0.000178893052934601 0.000357786105869202 0.999821106947065 16 0.000150168636967412 0.000300337273934823 0.999849831363033 17 0.000530819270962282 0.00106163854192456 0.999469180729038 18 0.000680099901896652 0.00136019980379330 0.999319900098103 19 0.00058460634850077 0.00116921269700154 0.9994153936515 20 0.000745058283442473 0.00149011656688495 0.999254941716557 21 0.00149805404667782 0.00299610809335564 0.998501945953322 22 0.00390107034022948 0.00780214068045896 0.99609892965977 23 0.0161524113334009 0.0323048226668018 0.9838475886666 24 0.0612735137302406 0.122547027460481 0.93872648626976 25 0.145128214564565 0.29025642912913 0.854871785435435 26 0.315433495910572 0.630866991821144 0.684566504089428 27 0.57301517624601 0.85396964750798 0.42698482375399 28 0.818683504551558 0.362632990896884 0.181316495448442 29 0.934381942841008 0.131236114317985 0.0656180571589924 30 0.979511639932452 0.0409767201350955 0.0204883600675477 31 0.995971514876337 0.00805697024732632 0.00402848512366316 32 0.999755875011052 0.000488249977895505 0.000244124988947752 33 0.999973620774971 5.27584500577641e-05 2.63792250288821e-05 34 0.99999401756403 1.19648719388081e-05 5.98243596940403e-06 35 0.999997676692855 4.64661428971882e-06 2.32330714485941e-06 36 0.9999990731145 1.85377099789933e-06 9.26885498949664e-07 37 0.999999954124439 9.17511221767421e-08 4.58755610883710e-08 38 0.99999998499648 3.0007040832486e-08 1.5003520416243e-08 39 0.99999999335469 1.32906183940950e-08 6.64530919704749e-09 40 0.999999991780198 1.64396043389184e-08 8.21980216945918e-09 41 0.99999998230691 3.53861790864548e-08 1.76930895432274e-08 42 0.999999952221764 9.55564715849425e-08 4.77782357924712e-08 43 0.999999962928547 7.41429059961183e-08 3.70714529980592e-08 44 0.999999980140883 3.97182336250761e-08 1.98591168125381e-08 45 0.999999936745103 1.26509794023391e-07 6.32548970116956e-08 46 0.999999718930445 5.62139110298224e-07 2.81069555149112e-07 47 0.9999988218131 2.35637379921237e-06 1.17818689960619e-06 48 0.999998373777977 3.25244404644458e-06 1.62622202322229e-06 49 0.999994533579498 1.09328410050819e-05 5.46642050254096e-06 50 0.99997272785451 5.45442909808006e-05 2.72721454904003e-05 51 0.999896509367403 0.000206981265193635 0.000103490632596818 52 0.999655081540936 0.000689836918129079 0.000344918459064539 53 0.99941294764271 0.00117410471458223 0.000587052357291115 54 0.999452014459324 0.00109597108135271 0.000547985540676356 55 0.999871154076202 0.000257691847595972 0.000128845923797986 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 40 0.784313725490196 NOK 5% type I error level 43 0.843137254901961 NOK 10% type I error level 44 0.862745098039216 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/10q17p1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/10q17p1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/1qj5i1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/1qj5i1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/2h5nm1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/2h5nm1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/3liy51258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/3liy51258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/4xgte1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/4xgte1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/5ni9a1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/5ni9a1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/67lgf1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/67lgf1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/7prth1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/7prth1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/8n60m1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/8n60m1258664730.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/9dv4z1258664730.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258665033sijkujlazqqa5gk/9dv4z1258664730.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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