verbetering ws7

*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, 27 Nov 2009 02:33:35 -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/27/t12593145383a5vrwijpa9eqe0.htm/, Retrieved Fri, 27 Nov 2009 10:35:50 +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/27/t12593145383a5vrwijpa9eqe0.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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200237 536662 204045 209465 213587 216234 203666 542722 200237 204045 209465 213587 241476 593530 203666 200237 204045 209465 260307 610763 241476 203666 200237 204045 243324 612613 260307 241476 203666 200237 244460 611324 243324 260307 241476 203666 233575 594167 244460 243324 260307 241476 237217 595454 233575 244460 243324 260307 235243 590865 237217 233575 244460 243324 230354 589379 235243 237217 233575 244460 227184 584428 230354 235243 237217 233575 221678 573100 227184 230354 235243 237217 217142 567456 221678 227184 230354 235243 219452 569028 217142 221678 227184 230354 256446 620735 219452 217142 221678 227184 265845 628884 256446 219452 217142 221678 248624 628232 265845 256446 219452 217142 241114 612117 248624 265845 256446 219452 229245 595404 241114 248624 265845 256446 231805 597141 229245 241114 248624 265845 219277 593408 231805 229245 241114 248624 219313 590072 219277 231805 229245 241114 212610 579799 219313 219277 231805 229245 214771 574205 212610 219313 219277 etc...

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 yt[t] = -36966.1549603009 + 0.22521259944289xt[t] + 0.78008644395922`yt-1`[t] + 0.264724289662743`yt-2`[t] -0.254204822249017`yt-3`[t] -0.203055482260318`yt-4`[t] -347.98479049917M1[t] + 6078.80639576497M2[t] + 23777.7731454625M3[t] -879.960276448189M4[t] -26288.3288224235M5[t] -13027.8366627345M6[t] + 837.100853143718M7[t] + 10420.6488383957M8[t] + 2634.58407307717M9[t] -3258.91930047984M10[t] -2450.69027059630M11[t] -133.111522809557t + 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) -36966.1549603009 22937.83424 -1.6116 0.113749 0.056875 xt 0.22521259944289 0.090142 2.4984 0.01603 0.008015 `yt-1` 0.78008644395922 0.165544 4.7123 2.2e-05 1.1e-05 `yt-2` 0.264724289662743 0.194979 1.3577 0.181041 0.09052 `yt-3` -0.254204822249017 0.193207 -1.3157 0.194652 0.097326 `yt-4` -0.203055482260318 0.148286 -1.3693 0.177398 0.088699 M1 -347.98479049917 2866.797343 -0.1214 0.903904 0.451952 M2 6078.80639576497 2785.121722 2.1826 0.034096 0.017048 M3 23777.7731454625 5409.373049 4.3957 6.3e-05 3.1e-05 M4 -879.960276448189 6192.846762 -0.1421 0.887614 0.443807 M5 -26288.3288224235 5194.015973 -5.0613 7e-06 3e-06 M6 -13027.8366627345 6464.106182 -2.0154 0.0496 0.0248 M7 837.100853143718 3603.229911 0.2323 0.817298 0.408649 M8 10420.6488383957 3475.914187 2.998 0.004333 0.002167 M9 2634.58407307717 3462.062074 0.761 0.450466 0.225233 M10 -3258.91930047984 3240.768223 -1.0056 0.319757 0.159878 M11 -2450.69027059630 2985.771893 -0.8208 0.41591 0.207955 t -133.111522809557 57.949216 -2.297 0.026118 0.013059 Multiple Linear Regression - Regression Statistics Multiple R 0.991167080004474 R-squared 0.982412180484596 Adjusted R-squared 0.976050628744981 F-TEST (value) 154.429645579548 F-TEST (DF numerator) 17 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4551.16421878486 Sum Squared Residuals 973515500.07834 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 200237 199836.660039697 400.339960302505 2 203666 204675.073366061 -1009.07336606120 3 241476 237565.161501211 3910.83849878931 4 260307 249126.785995017 11180.2140049834 5 243324 248602.549394501 -5278.54939450088 6 244460 242868.684433672 1591.31556632780 7 233575 236662.444655061 -3087.44465506060 8 237217 238705.638293358 -1488.63829335779 9 235243 232872.226899459 2370.77310054110 10 230354 228471.529765229 1882.47023477076 11 227184 224979.656281923 2204.34371807677 12 221678 220741.187876437 936.81212356349 13 217142 215498.298591159 1643.70140884093 14 219452 218948.535951556 503.464048444141 15 256446 250804.006995541 5641.99300445873 16 265845 259589.547099676 6255.4529003237 17 248624 251360.317802748 -2736.31780274849 18 241114 240039.560988423 1074.43901157729 19 229245 229689.036985530 -444.036985529751 20 231805 230752.885080671 1052.11491932911 21 219277 226253.895536446 -6976.89553644604 22 219313 214922.846327002 4390.15367299793 23 212610 211755.273185077 854.726814922845 24 214771 210258.479270699 4512.52072930115 25 211142 211901.376540130 -759.376540129599 26 211457 217670.427118698 -6213.42711869812 27 240048 245833.606853983 -5785.60685398271 28 240636 245319.085028244 -4683.08502824393 29 230580 227134.663931852 3445.33606814812 30 208795 217968.880572155 -9173.88057215511 31 197922 201161.096884328 -3239.09688432764 32 194596 196892.421568183 -2296.42156818325 33 194581 191823.760551609 2757.23944839119 34 185686 189452.561993850 -3766.56199384959 35 178106 182368.769543950 -4262.76954394974 36 172608 175801.872799113 -3193.87279911266 37 167302 167938.060135102 -636.060135101805 38 168053 169582.966918991 -1529.96691899087 39 202300 202036.373041012 263.626958988097 40 202388 208703.653392679 -6315.65339267872 41 182516 188018.312490783 -5502.31249078323 42 173476 173523.687647982 -47.6876479823754 43 166444 164087.458165350 2356.54183464975 44 171297 171686.546509356 -389.54650935584 45 169701 172624.685030377 -2923.68503037744 46 164182 168152.733873489 -3970.73387348871 47 161914 160929.833455853 984.166544147243 48 159612 158901.664660917 710.335339083216 49 151001 153047.657276374 -2046.65727637441 50 158114 155563.740204767 2550.25979523312 51 186530 188885.093622934 -2355.09362293394 52 187069 191974.143242376 -4905.14324237641 53 174330 170792.435920744 3537.56407925560 54 169362 162806.186357768 6555.81364223239 55 166827 162412.963309732 4414.03669026824 56 178037 174914.508548432 3122.49145156777 57 186412 181639.431982109 4772.56801789119 58 189226 187761.328040430 1464.67195956961 59 191563 191343.467533197 219.532466802877 60 188906 191871.795392835 -2965.79539283518 61 186005 184606.947417538 1398.05258246237 62 195309 189610.256439927 5698.74356007292 63 223532 225207.757985319 -1675.75798531948 64 226899 228430.785242008 -1531.78524200806 65 214126 207591.720459371 6534.27954062887 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.29853286404638 0.59706572809276 0.70146713595362 22 0.247783725264127 0.495567450528253 0.752216274735873 23 0.172149322133961 0.344298644267922 0.827850677866039 24 0.817262937336885 0.365474125326231 0.182737062663115 25 0.751549066285133 0.496901867429733 0.248450933714867 26 0.648369200790014 0.703261598419972 0.351630799209986 27 0.71018323221065 0.579633535578701 0.289816767789351 28 0.966200897002074 0.0675982059958511 0.0337991029979256 29 0.97918786842403 0.0416242631519391 0.0208121315759695 30 0.962789086859814 0.0744218262803726 0.0372109131401863 31 0.97614484143197 0.0477103171360581 0.0238551585680290 32 0.987862045034491 0.0242759099310172 0.0121379549655086 33 0.991493182240934 0.0170136355181316 0.00850681775906582 34 0.986047630552244 0.0279047388955117 0.0139523694477558 35 0.976385401547123 0.0472291969057548 0.0236145984528774 36 0.955203432925047 0.0895931341499057 0.0447965670749528 37 0.981753983601425 0.0364920327971499 0.0182460163985749 38 0.99304304634829 0.0139139073034196 0.00695695365170981 39 0.996901988089753 0.00619602382049388 0.00309801191024694 40 0.992430623197616 0.0151387536047672 0.00756937680238358 41 0.98015860699283 0.0396827860143406 0.0198413930071703 42 0.95778545435407 0.0844290912918588 0.0422145456459294 43 0.951291811910326 0.0974163761793488 0.0487081880896744 44 0.972403867737687 0.0551922645246269 0.0275961322623135 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0416666666666667 NOK 5% type I error level 11 0.458333333333333 NOK 10% type I error level 17 0.708333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/1015k11259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/1015k11259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/1jwu41259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/1jwu41259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/2wmu71259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/2wmu71259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/37ygy1259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/37ygy1259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/4a3a21259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/4a3a21259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/5jsbk1259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/5jsbk1259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/6syfv1259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/6syfv1259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/7b8ui1259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/7b8ui1259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/8vf9z1259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/8vf9z1259314409.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/9v4gy1259314409.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t12593145383a5vrwijpa9eqe0/9v4gy1259314409.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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