Model 3

*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 07:25:51 -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/t12587272196f2rwdynalh9ul3.htm/, Retrieved Fri, 20 Nov 2009 15:27:12 +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/t12587272196f2rwdynalh9ul3.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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20,3 3016 20 2155 19,2 2172 21,8 2150 21,3 2533 21,5 2058 19,5 2160 19,5 2260 19,7 2498 18,7 2695 19,7 2799 20 2946 19,7 2930 19,2 2318 19,7 2540 22 2570 21,8 2669 22,8 2450 21 2842 25 3440 23,3 2678 25 2981 26,8 2260 25,3 2844 26,5 2546 27,8 2456 22 2295 22,3 2379 28 2479 25 2057 27,3 2280 25,8 2351 27,3 2276 23,5 2548 24,5 2311 18 2201 21,3 2725 21,8 2408 20,5 2139 22,3 1898 18,7 2537 22,3 2068 17,7 2063 19,7 2520 20,5 2434 18,5 2190 10 2794 14,2 2070 15,5 2615 16,5 2265 20,5 2139 15,7 2428 11,7 2137 7,5 1823 3,5 2063 4,5 1806 2,2 1758 5 2243 2,3 1993 6,1 1932 3,3 2465

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 Y[t] = + 7.22953434391532 + 0.00648463349489511X[t] -1.85480014419147M1[t] + 3.16277160464226M2[t] + 3.06233004168446M3[t] + 3.48919297729325M4[t] + 1.93148382070862M5[t] + 4.08278029553563M6[t] + 0.996538751573457M7[t] + 1.00824945372864M8[t] + 1.82732939754611M9[t] + 0.221975324946219M10[t] -0.441128652098417M11[t] -0.168432673465854t + 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) 7.22953434391532 8.554042 0.8452 0.402304 0.201152 X 0.00648463349489511 0.002947 2.2008 0.032702 0.016351 M1 -1.85480014419147 3.524316 -0.5263 0.601164 0.300582 M2 3.16277160464226 3.636444 0.8697 0.388861 0.194431 M3 3.06233004168446 3.65728 0.8373 0.406648 0.203324 M4 3.48919297729325 3.635122 0.9599 0.34204 0.17102 M5 1.93148382070862 3.589874 0.538 0.593091 0.296545 M6 4.08278029553563 3.741828 1.0911 0.280783 0.140391 M7 0.996538751573457 3.615776 0.2756 0.784057 0.392028 M8 1.00824945372864 3.583742 0.2813 0.779685 0.389842 M9 1.82732939754611 3.591465 0.5088 0.613274 0.306637 M10 0.221975324946219 3.594739 0.0618 0.951024 0.475512 M11 -0.441128652098417 3.578791 -0.1233 0.902425 0.451213 t -0.168432673465854 0.049605 -3.3955 0.001402 0.000701 Multiple Linear Regression - Regression Statistics Multiple R 0.671765465070318 R-squared 0.45126884006114 Adjusted R-squared 0.299492136248264 F-TEST (value) 2.97324180012174 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0.00308389822045241 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.65710184469093 Sum Squared Residuals 1504.13166021666 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 20.3 24.7639561468617 -4.46395614686168 2 20 24.0298257831248 -4.02982578312484 3 19.2 23.8711903161144 -4.67119031611441 4 21.8 23.9869586413697 -2.18695864136965 5 21.3 24.744431439864 -3.44443143986399 6 21.5 23.6470943311500 -2.14709433114998 7 19.5 21.0538527302012 -1.55385273020125 8 19.5 21.5455941083801 -2.04559410838008 9 19.7 23.7395841505167 -4.03958415051675 10 18.7 23.2432702029453 -4.54327020294533 11 19.7 23.0861354359039 -3.38613543590394 12 20 24.3120725382861 -4.31207253828608 13 19.7 22.1850855847104 -2.48508558471044 14 19.2 23.0656289612025 -3.8656289612025 15 19.7 24.2363433606456 -4.53634336064556 16 22 24.6893126276354 -2.68931262763535 17 21.8 23.6051495135795 -1.80514951357948 18 22.8 24.1678785795586 -1.36787857955861 19 21 23.4551806921295 -2.45518069212947 20 25 27.1762695507661 -2.17626955076607 21 23.3 22.8856260980076 0.414373901992383 22 25 23.0766833008951 1.92331669910491 23 26.8 17.5697259005652 9.23027409943477 24 25.3 21.6294478402165 3.67055215978347 25 26.5 17.6737942410805 8.82620575891953 26 27.8 21.9393163019078 5.86068369809222 27 22 20.626416072806 1.37358392719399 28 22.3 21.4295555485201 0.870444451479859 29 28 20.3518770679592 7.64812293204083 30 25 19.5982255344746 5.40177446552541 31 27.3 17.7896245864082 9.51037541359183 32 25.8 18.0933115932351 7.70668840676495 33 27.3 18.2576113514695 9.04238864853047 34 23.5 18.2476449160153 5.25235508398474 35 24.5 15.8792501272146 8.62074987278537 36 18 15.4386364214087 2.56136357859127 37 21.3 16.8133515550764 4.48664844492355 38 21.8 19.6068618125626 2.19313818743744 39 20.5 17.5936211660121 2.90637883398787 40 22.3 16.2892547558853 6.01074524411465 41 18.7 18.7067937290728 -0.00679372907283377 42 22.3 17.6483644213282 4.65163557867181 43 17.7 14.3612670364257 3.33873296357432 44 19.7 17.1680225722821 2.53197742771792 45 20.5 17.2609913620727 3.23900863792728 46 18.5 13.9049540432526 4.59504595674744 47 10 16.9901360236587 -6.99013602365872 48 14.2 12.5679573519872 1.63204264801278 49 15.5 14.0788497890477 1.42115021095226 50 16.5 16.6583671412023 -0.158367141202320 51 20.5 15.5724290844219 4.92757091557812 52 15.7 17.7049184265895 -2.00491842658951 53 11.7 14.0917482495245 -2.39174824952454 54 7.5 14.0384371334886 -6.53843713348864 55 3.5 12.3400749548354 -8.84007495483544 56 4.5 10.5168021753367 -6.01680217533672 57 2.2 10.8561870379334 -8.65618703793338 58 5 12.2274475368918 -7.22744753689176 59 2.3 9.77475251265749 -7.47475251265749 60 6.1 9.65188584810145 -3.55188584810145 61 3.3 11.0849626832232 -7.78496268322322 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00108213959810282 0.00216427919620565 0.998917860401897 18 0.00026909844887223 0.00053819689774446 0.999730901551128 19 3.04499007893721e-05 6.08998015787441e-05 0.99996955009921 20 3.46076640413878e-05 6.92153280827757e-05 0.999965392335959 21 0.000619679027915768 0.00123935805583154 0.999380320972084 22 0.00670450794536656 0.0134090158907331 0.993295492054633 23 0.0270725304580489 0.0541450609160978 0.972927469541951 24 0.0210055601559515 0.042011120311903 0.978994439844048 25 0.0115740888951302 0.0231481777902603 0.98842591110487 26 0.0113669541333130 0.0227339082666260 0.988633045866687 27 0.0224958214700794 0.0449916429401587 0.97750417852992 28 0.0742557070366955 0.148511414073391 0.925744292963305 29 0.050965236059017 0.101930472118034 0.949034763940983 30 0.0389279404471108 0.0778558808942216 0.96107205955289 31 0.0299962383768130 0.0599924767536259 0.970003761623187 32 0.0176669128401980 0.0353338256803960 0.982333087159802 33 0.0104610767395953 0.0209221534791906 0.989538923260405 34 0.00869626769017574 0.0173925353803515 0.991303732309824 35 0.0135556599877816 0.0271113199755632 0.986444340012218 36 0.0816591408327444 0.163318281665489 0.918340859167255 37 0.093024557336325 0.18604911467265 0.906975442663675 38 0.108602638282769 0.217205276565538 0.891397361717231 39 0.347733910043891 0.695467820087782 0.652266089956109 40 0.327419748409598 0.654839496819195 0.672580251590402 41 0.5288169040724 0.9423661918552 0.4711830959276 42 0.433495899337197 0.866991798674394 0.566504100662803 43 0.380654231295054 0.761308462590109 0.619345768704946 44 0.297119779894153 0.594239559788305 0.702880220105847 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.178571428571429 NOK 5% type I error level 14 0.5 NOK 10% type I error level 17 0.607142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/10u01t1258727147.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/10u01t1258727147.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/15udr1258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/15udr1258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/2lplt1258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/2lplt1258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/3lwmk1258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/3lwmk1258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/426261258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/426261258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/534q61258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/534q61258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/618xw1258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/618xw1258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/7dm231258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/7dm231258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/8r8pc1258727146.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/8r8pc1258727146.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/9m0lf1258727147.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587272196f2rwdynalh9ul3/9m0lf1258727147.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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