*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 09:09:48 -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/20/t12587339308pcyzpci2m9lvz4.htm/, Retrieved Fri, 20 Nov 2009 17:19:02 +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/t12587339308pcyzpci2m9lvz4.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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2253 14.9 2218 18.6 1855 19.1 2187 18.8 1852 18.2 1570 18 1851 19 1954 20.7 1828 21.2 2251 20.7 2277 19.6 2085 18.6 2282 18.7 2266 23.8 1878 24.9 2267 24.8 2069 23.8 1746 22.3 2299 21.7 2360 20.7 2214 19.7 2825 18.4 2355 17.4 2333 17 3016 18 2155 23.8 2172 25.5 2150 25.6 2533 23.7 2058 22 2160 21.3 2260 20.7 2498 20.4 2695 20.3 2799 20.4 2946 19.8 2930 19.5 2318 23.1 2540 23.5 2570 23.5 2669 22.9 2450 21.9 2842 21.5 3440 20.5 2678 20.2 2981 19.4 2260 19.2 2844 18.8 2546 18.8 2456 22.6 2295 23.3 2379 23 2479 21.4 2057 19.9 2280 18.8 2351 18.6 2276 18.4 2548 18.6 2311 19.9 2201 19.2

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 wngbw[t] = + 2613.94906149101 -12.6101596071860`<25`[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) 2613.94906149101 412.470458 6.3373 0 0 `<25` -12.6101596071860 19.859534 -0.635 0.527947 0.263973 Multiple Linear Regression - Regression Statistics Multiple R 0.0830870362217952 R-squared 0.0069034555881219 Adjusted R-squared -0.010218898625876 F-TEST (value) 0.403183785467899 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.527946632050694 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 351.478594444187 Sum Squared Residuals 7165157.73644276 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2253 2426.05768334395 -173.057683343953 2 2218 2379.40009279735 -161.400092797350 3 1855 2373.09501299376 -518.095012993757 4 2187 2376.87806087591 -189.878060875913 5 1852 2384.44415664022 -532.444156640224 6 1570 2386.96618856166 -816.966188561661 7 1851 2374.35602895448 -523.356028954475 8 1954 2352.91875762226 -398.918757622259 9 1828 2346.61367781867 -518.613677818666 10 2251 2352.91875762226 -101.918757622259 11 2277 2366.78993319016 -89.7899331901638 12 2085 2379.40009279735 -294.400092797350 13 2282 2378.13907683663 -96.1390768366312 14 2266 2313.82726283998 -47.8272628399827 15 1878 2299.95608727208 -421.956087272078 16 2267 2301.21710323280 -34.2171032327968 17 2069 2313.82726283998 -244.827262839983 18 1746 2332.74250225076 -586.742502250762 19 2299 2340.30859801507 -41.3085980150733 20 2360 2352.91875762226 7.08124237774075 21 2214 2365.52891722945 -151.528917229445 22 2825 2381.92212471879 443.077875281213 23 2355 2394.53228432597 -39.5322843259730 24 2333 2399.57634816885 -66.5763481688473 25 3016 2386.96618856166 629.033811438339 26 2155 2313.82726283998 -158.827262839983 27 2172 2292.38999150777 -120.389991507767 28 2150 2291.12897554705 -141.128975547048 29 2533 2315.0882788007 217.911721199299 30 2058 2336.52555013292 -278.525550132917 31 2160 2345.35266185795 -185.352661857948 32 2260 2352.91875762226 -92.9187576222592 33 2498 2356.70180550442 141.298194495585 34 2695 2357.96282146513 337.037178534866 35 2799 2356.70180550442 442.298194495585 36 2946 2364.26790126873 581.732098731273 37 2930 2368.05094915088 561.949050849118 38 2318 2322.65437456501 -4.6543745650129 39 2540 2317.61031072214 222.389689277861 40 2570 2317.61031072214 252.389689277861 41 2669 2325.17640648645 343.82359351355 42 2450 2337.78656609364 112.213433906364 43 2842 2342.83062993651 499.16937006349 44 3440 2355.44078954370 1084.55921045630 45 2678 2359.22383742585 318.776162574148 46 2981 2369.3119651116 611.688034888399 47 2260 2371.83399703304 -111.833997033038 48 2844 2376.87806087591 467.121939124087 49 2546 2376.87806087591 169.121939124087 50 2456 2328.95945436861 127.040545631394 51 2295 2320.13234264358 -25.1323426435757 52 2379 2323.91539052573 55.0846094742685 53 2479 2344.09164589723 134.908354102771 54 2057 2363.00688530801 -306.006885308008 55 2280 2376.87806087591 -96.8780608759126 56 2351 2379.40009279735 -28.4000927973498 57 2276 2381.92212471879 -105.922124718787 58 2548 2379.40009279735 168.599907202650 59 2311 2363.00688530801 -52.006885308008 60 2201 2371.83399703304 -170.833997033038 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.207839818832369 0.415679637664738 0.792160181167631 6 0.454012919930295 0.90802583986059 0.545987080069705 7 0.347709200508832 0.695418401017664 0.652290799491168 8 0.263198055867246 0.526396111734492 0.736801944132754 9 0.193882235358464 0.387764470716928 0.806117764641536 10 0.241899933757092 0.483799867514184 0.758100066242908 11 0.245644646769369 0.491289293538738 0.754355353230631 12 0.194393408750014 0.388786817500027 0.805606591249986 13 0.185181824501644 0.370363649003287 0.814818175498356 14 0.162359998125086 0.324719996250172 0.837640001874914 15 0.140769766322356 0.281539532644713 0.859230233677644 16 0.117568097867365 0.23513619573473 0.882431902132635 17 0.0833647133935547 0.166729426787109 0.916635286606445 18 0.134753192579063 0.269506385158125 0.865246807420937 19 0.124787794113495 0.24957558822699 0.875212205886505 20 0.126113564955799 0.252227129911599 0.8738864350442 21 0.107761661506590 0.215523323013179 0.89223833849341 22 0.342045053022936 0.684090106045872 0.657954946977064 23 0.314685243237196 0.629370486474393 0.685314756762804 24 0.291787004198378 0.583574008396755 0.708212995801622 25 0.607562789619745 0.784874420760511 0.392437210380255 26 0.554438801836922 0.891122396326156 0.445561198163078 27 0.497447360647517 0.994894721295034 0.502552639352483 28 0.448206524978479 0.896413049956958 0.551793475021521 29 0.446079122302722 0.892158244605443 0.553920877697278 30 0.450372057301580 0.900744114603161 0.54962794269842 31 0.431186305205987 0.862372610411975 0.568813694794013 32 0.394942303428656 0.789884606857311 0.605057696571344 33 0.361696642275936 0.723393284551872 0.638303357724064 34 0.385015635265692 0.770031270531384 0.614984364734308 35 0.449692675461490 0.899385350922981 0.55030732453851 36 0.591503797645891 0.816992404708217 0.408496202354109 37 0.696957092074947 0.606085815850106 0.303042907925053 38 0.648314949604726 0.703370100790548 0.351685050395274 39 0.595065267243269 0.809869465513462 0.404934732756731 40 0.539915121185559 0.920169757628882 0.460084878814441 41 0.502621167801569 0.994757664396863 0.497378832198431 42 0.427658806038659 0.855317612077318 0.572341193961341 43 0.458929084984229 0.917858169968458 0.541070915015771 44 0.959681129979137 0.0806377400417252 0.0403188700208626 45 0.954090220407863 0.0918195591842745 0.0459097795921372 46 0.992986789452645 0.0140264210947109 0.00701321054735543 47 0.987521529380139 0.0249569412397227 0.0124784706198613 48 0.998515365343213 0.00296926931357366 0.00148463465678683 49 0.998446655863819 0.00310668827236221 0.00155334413618111 50 0.996498193340148 0.00700361331970463 0.00350180665985231 51 0.990875103529559 0.0182497929408829 0.00912489647044144 52 0.97634045553675 0.0473190889264997 0.0236595444632498 53 0.985113729707891 0.0297725405842175 0.0148862702921087 54 0.97560019499622 0.0487996100075617 0.0243998050037809 55 0.928827687724035 0.142344624551930 0.0711723122759651 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.0588235294117647 NOK 5% type I error level 9 0.176470588235294 NOK 10% type I error level 11 0.215686274509804 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/10jlqf1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/10jlqf1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/11im31258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/11im31258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/2azhw1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/2azhw1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/3ev2e1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/3ev2e1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/40gwy1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/40gwy1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/5kgoy1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/5kgoy1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/6t6ke1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/6t6ke1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/7mbzs1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/7mbzs1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/8b5bk1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/8b5bk1258733380.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/93iss1258733380.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587339308pcyzpci2m9lvz4/93iss1258733380.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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