ws7verbetering

*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, 26 Nov 2009 10:21:31 -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/26/t12592561371ardc4hh0liqygx.htm/, Retrieved Thu, 26 Nov 2009 18:22:29 +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/26/t12592561371ardc4hh0liqygx.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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29.837 0 29.571 0 30.167 0 30.524 0 30.996 0 31.033 0 31.198 0 30.937 0 31.649 0 33.115 0 34.106 0 33.926 0 33.382 0 32.851 0 32.948 0 36.112 0 36.113 0 35.210 0 35.193 0 34.383 0 35.349 0 37.058 0 38.076 0 36.630 0 36.045 0 35.638 0 35.114 0 35.465 0 35.254 0 35.299 0 35.916 0 36.683 0 37.288 0 38.536 0 38.977 0 36.407 0 34.955 0 34.951 0 32.680 0 34.791 0 34.178 0 35.213 0 34.871 0 35.299 0 35.443 0 37.108 0 36.419 0 34.471 0 33.868 0 34.385 0 33.643 1 34.627 1 32.919 1 35.500 1 36.110 1 37.086 1 37.711 1 40.427 1 39.884 1 38.512 1 38.767 1

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 saldo_zichtrek[t] = + 32.8617038709678 -0.55318467741935crisis[t] -1.08223211917562M1[t] -1.72115552867384M2[t] -2.26926673387097M3[t] -0.965814874551973M4[t] -1.46756301523298M5[t] -0.99851115591398M6[t] -0.881859296594985M7[t] -0.751807437275987M8[t] -0.231355577956992M9[t] + 1.43949628136201M10[t] + 1.593148140681M11[t] + 0.0899481406810033t + 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) 32.8617038709678 0.908805 36.1592 0 0 crisis -0.55318467741935 0.761496 -0.7264 0.471169 0.235585 M1 -1.08223211917562 1.019167 -1.0619 0.293716 0.146858 M2 -1.72115552867384 1.07 -1.6086 0.114411 0.057206 M3 -2.26926673387097 1.072621 -2.1156 0.039704 0.019852 M4 -0.965814874551973 1.070408 -0.9023 0.371505 0.185753 M5 -1.46756301523298 1.068451 -1.3735 0.1761 0.08805 M6 -0.99851115591398 1.066752 -0.936 0.354043 0.177022 M7 -0.881859296594985 1.065313 -0.8278 0.411971 0.205986 M8 -0.751807437275987 1.064134 -0.7065 0.483367 0.241683 M9 -0.231355577956992 1.063216 -0.2176 0.828683 0.414341 M10 1.43949628136201 1.06256 1.3547 0.181976 0.090988 M11 1.593148140681 1.062166 1.4999 0.140327 0.070163 t 0.0899481406810033 0.016703 5.3852 2e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.797953120470371 R-squared 0.636729182468403 Adjusted R-squared 0.53625002017243 F-TEST (value) 6.33692765663034 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.07600384402495e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.67922394172143 Sum Squared Residuals 132.530273183172 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 29.837 31.8694198924731 -2.03241989247307 2 29.571 31.3204446236559 -1.74944462365591 3 30.167 30.8622815591398 -0.695281559139789 4 30.524 32.2556815591398 -1.73168155913979 5 30.996 31.8438815591398 -0.847881559139785 6 31.033 32.4028815591398 -1.36988155913979 7 31.198 32.6094815591398 -1.41148155913979 8 30.937 32.8294815591398 -1.89248155913979 9 31.649 33.4398815591398 -1.79088155913979 10 33.115 35.2006815591398 -2.08568155913979 11 34.106 35.4442815591398 -1.33828155913979 12 33.926 33.9410815591398 -0.0150815591397881 13 33.382 32.9487975806452 0.433202419354821 14 32.851 32.3998223118280 0.451177688172041 15 32.948 31.9416592473118 1.00634075268817 16 36.112 33.3350592473118 2.77694075268817 17 36.113 32.9232592473118 3.18974075268817 18 35.21 33.4822592473118 1.72774075268817 19 35.193 33.6888592473118 1.50414075268817 20 34.383 33.9088592473118 0.474140752688173 21 35.349 34.5192592473118 0.829740752688169 22 37.058 36.2800592473118 0.77794075268817 23 38.076 36.5236592473118 1.55234075268817 24 36.63 35.0204592473118 1.60954075268817 25 36.045 34.0281752688172 2.01682473118279 26 35.638 33.4792 2.15880000000000 27 35.114 33.0210369354839 2.09296306451613 28 35.465 34.4144369354839 1.05056306451613 29 35.254 34.0026369354839 1.25136306451613 30 35.299 34.5616369354839 0.73736306451613 31 35.916 34.7682369354839 1.14776306451613 32 36.683 34.9882369354839 1.69476306451613 33 37.288 35.5986369354839 1.68936306451613 34 38.536 37.3594369354839 1.17656306451613 35 38.977 37.6030369354839 1.37396306451613 36 36.407 36.0998369354839 0.307163064516126 37 34.955 35.1075529569893 -0.152552956989258 38 34.951 34.5585776881720 0.392422311827960 39 32.68 34.1004146236559 -1.42041462365591 40 34.791 35.4938146236559 -0.702814623655914 41 34.178 35.0820146236559 -0.904014623655912 42 35.213 35.6410146236559 -0.42801462365591 43 34.871 35.8476146236559 -0.976614623655907 44 35.299 36.0676146236559 -0.76861462365591 45 35.443 36.6780146236559 -1.23501462365591 46 37.108 38.4388146236559 -1.33081462365591 47 36.419 38.6824146236559 -2.26341462365591 48 34.471 37.1792146236559 -2.70821462365591 49 33.868 36.1869306451613 -2.31893064516129 50 34.385 35.6379553763441 -1.25295537634408 51 33.643 34.6266076344086 -0.9836076344086 52 34.627 36.0200076344086 -1.3930076344086 53 32.919 35.6082076344086 -2.6892076344086 54 35.5 36.1672076344086 -0.667207634408602 55 36.11 36.3738076344086 -0.263807634408602 56 37.086 36.5938076344086 0.492192365591397 57 37.711 37.2042076344086 0.506792365591399 58 40.427 38.9650076344086 1.46199236559140 59 39.884 39.2086076344086 0.675392365591399 60 38.512 37.7054076344086 0.806592365591398 61 38.767 36.713123655914 2.05387634408602 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.489899798911477 0.979799597822953 0.510100201088523 18 0.315387349780485 0.63077469956097 0.684612650219515 19 0.186831139465138 0.373662278930276 0.813168860534862 20 0.138858373743986 0.277716747487972 0.861141626256014 21 0.0930179676372175 0.186035935274435 0.906982032362782 22 0.0701561253674618 0.140312250734924 0.929843874632538 23 0.041612937670937 0.083225875341874 0.958387062329063 24 0.0446804160789222 0.0893608321578443 0.955319583921078 25 0.0566661668457133 0.113332333691427 0.943333833154287 26 0.0531180056332661 0.106236011266532 0.946881994366734 27 0.103689006871131 0.207378013742262 0.896310993128869 28 0.298718316038996 0.597436632077991 0.701281683961004 29 0.531941376633085 0.93611724673383 0.468058623366915 30 0.552113505662196 0.89577298867561 0.447886494337804 31 0.502359413908198 0.995281172183604 0.497640586091802 32 0.409320843457601 0.818641686915201 0.5906791565424 33 0.333403638696861 0.666807277393721 0.66659636130314 34 0.25190679485651 0.50381358971302 0.74809320514349 35 0.224930816438897 0.449861632877793 0.775069183561103 36 0.270129221339302 0.540258442678603 0.729870778660698 37 0.324613667354081 0.649227334708161 0.67538633264592 38 0.295645457340919 0.591290914681838 0.70435454265908 39 0.408630463586316 0.817260927172633 0.591369536413684 40 0.478383060735909 0.956766121471817 0.521616939264091 41 0.76456317278457 0.47087365443086 0.23543682721543 42 0.843714599731485 0.312570800537029 0.156285400268515 43 0.866565821229026 0.266868357541947 0.133434178770974 44 0.865850339329847 0.268299321340306 0.134149660670153 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 0 0 OK 10% type I error level 2 0.0714285714285714 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/10sljs1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/10sljs1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/15wiw1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/15wiw1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/2cpf61259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/2cpf61259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/3bh8r1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/3bh8r1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/4ba7v1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/4ba7v1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/50hkb1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/50hkb1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/6a9w71259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/6a9w71259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/71hsq1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/71hsq1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/8ipgy1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/8ipgy1259256087.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/9rhvf1259256087.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t12592561371ardc4hh0liqygx/9rhvf1259256087.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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