SHW 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, 20 Nov 2009 04:20:18 -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/t1258716079w93fq2xga4vo18o.htm/, Retrieved Fri, 20 Nov 2009 12:21:32 +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/t1258716079w93fq2xga4vo18o.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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8.6 0 8.5 0 8.3 0 7.8 0 7.8 0 8 0 8.6 0 8.9 0 8.9 0 8.6 0 8.3 0 8.3 0 8.3 0 8.4 0 8.5 0 8.4 0 8.6 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.5 0 8.6 0 8.4 0 8.1 0 8 0 8 0 8 0 8 0 7.9 0 7.8 0 7.8 0 7.9 0 8.1 0 8 0 7.6 0 7.3 0 7 0 6.8 0 7 0 7.1 0 7.2 0 7.1 1 6.9 1 6.7 1 6.7 1 6.6 1 6.9 1 7.3 1 7.5 1 7.3 1 7.1 1 6.9 1 7.1 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 8.02708333333333 -1.13541666666667X[t] + 0.259999999999997M1[t] + 0.220000000000001M2[t] + 0.18M3[t] + 0.0999999999999998M4[t] + 0.12M5[t] + 0.0799999999999999M6[t] + 0.16M7[t] + 0.18M8[t] + 0.1M9[t] + 0.0399999999999998M10[t] -0.0399999999999999M11[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) 8.02708333333333 0.234435 34.2401 0 0 X -1.13541666666667 0.167454 -6.7805 0 0 M1 0.259999999999997 0.328141 0.7923 0.432142 0.216071 M2 0.220000000000001 0.328141 0.6704 0.505856 0.252928 M3 0.18 0.328141 0.5485 0.585914 0.292957 M4 0.0999999999999998 0.328141 0.3047 0.761904 0.380952 M5 0.12 0.328141 0.3657 0.716232 0.358116 M6 0.0799999999999999 0.328141 0.2438 0.808448 0.404224 M7 0.16 0.328141 0.4876 0.628103 0.314051 M8 0.18 0.328141 0.5485 0.585914 0.292957 M9 0.1 0.328141 0.3047 0.761904 0.380952 M10 0.0399999999999998 0.328141 0.1219 0.903499 0.451749 M11 -0.0399999999999999 0.328141 -0.1219 0.903499 0.451749 Multiple Linear Regression - Regression Statistics Multiple R 0.709316796716717 R-squared 0.503130318104464 Adjusted R-squared 0.37626997379071 F-TEST (value) 3.96601728322691 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.000313507766686705 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.518835814913426 Sum Squared Residuals 12.6519583333333 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.6 8.28708333333335 0.312916666666653 2 8.5 8.24708333333333 0.252916666666669 3 8.3 8.20708333333333 0.0929166666666671 4 7.8 8.12708333333333 -0.327083333333333 5 7.8 8.14708333333333 -0.347083333333333 6 8 8.10708333333333 -0.107083333333333 7 8.6 8.18708333333333 0.412916666666667 8 8.9 8.20708333333333 0.692916666666668 9 8.9 8.12708333333333 0.772916666666667 10 8.6 8.06708333333333 0.532916666666666 11 8.3 7.98708333333333 0.312916666666667 12 8.3 8.02708333333333 0.272916666666667 13 8.3 8.28708333333333 0.0129166666666705 14 8.4 8.24708333333333 0.152916666666666 15 8.5 8.20708333333333 0.292916666666666 16 8.4 8.12708333333333 0.272916666666667 17 8.6 8.14708333333333 0.452916666666666 18 8.5 8.10708333333333 0.392916666666667 19 8.5 8.18708333333333 0.312916666666667 20 8.5 8.20708333333333 0.292916666666667 21 8.5 8.12708333333333 0.372916666666667 22 8.5 8.06708333333333 0.432916666666667 23 8.5 7.98708333333333 0.512916666666667 24 8.5 8.02708333333333 0.472916666666667 25 8.5 8.28708333333333 0.21291666666667 26 8.5 8.24708333333333 0.252916666666666 27 8.5 8.20708333333333 0.292916666666666 28 8.5 8.12708333333333 0.372916666666667 29 8.6 8.14708333333333 0.452916666666666 30 8.4 8.10708333333333 0.292916666666667 31 8.1 8.18708333333333 -0.0870833333333334 32 8 8.20708333333333 -0.207083333333333 33 8 8.12708333333333 -0.127083333333333 34 8 8.06708333333333 -0.0670833333333332 35 8 7.98708333333333 0.0129166666666665 36 7.9 8.02708333333333 -0.127083333333333 37 7.8 8.28708333333333 -0.48708333333333 38 7.8 8.24708333333333 -0.447083333333334 39 7.9 8.20708333333333 -0.307083333333333 40 8.1 8.12708333333333 -0.0270833333333337 41 8 8.14708333333333 -0.147083333333333 42 7.6 8.10708333333333 -0.507083333333334 43 7.3 8.18708333333333 -0.887083333333333 44 7 8.20708333333333 -1.20708333333333 45 6.8 8.12708333333333 -1.32708333333333 46 7 8.06708333333333 -1.06708333333333 47 7.1 7.98708333333333 -0.887083333333334 48 7.2 8.02708333333333 -0.827083333333333 49 7.1 7.15166666666666 -0.051666666666664 50 6.9 7.11166666666667 -0.211666666666667 51 6.7 7.07166666666667 -0.371666666666667 52 6.7 6.99166666666667 -0.291666666666667 53 6.6 7.01166666666667 -0.411666666666667 54 6.9 6.97166666666667 -0.0716666666666665 55 7.3 7.05166666666667 0.248333333333333 56 7.5 7.07166666666667 0.428333333333333 57 7.3 6.99166666666667 0.308333333333333 58 7.1 6.93166666666667 0.168333333333333 59 6.9 6.85166666666667 0.0483333333333333 60 7.1 6.89166666666667 0.208333333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.152018031571261 0.304036063142523 0.847981968428739 17 0.230788219019074 0.461576438038149 0.769211780980926 18 0.181243436813180 0.362486873626359 0.81875656318682 19 0.105369833305929 0.210739666611857 0.894630166694071 20 0.0771174828396537 0.154234965679307 0.922882517160346 21 0.0606001696887962 0.121200339377592 0.939399830311204 22 0.0383549250736142 0.0767098501472284 0.961645074926386 23 0.0267775826575941 0.0535551653151882 0.973222417342406 24 0.0186772322236841 0.0373544644473683 0.981322767776316 25 0.0106632082864103 0.0213264165728205 0.98933679171359 26 0.00652882284021741 0.0130576456804348 0.993471177159783 27 0.00444200267448736 0.00888400534897472 0.995557997325513 28 0.00492057682153077 0.00984115364306153 0.99507942317847 29 0.00728236690360236 0.0145647338072047 0.992717633096398 30 0.00651889979392655 0.0130377995878531 0.993481100206073 31 0.00709090130086208 0.0141818026017242 0.992909098699138 32 0.014011642477936 0.028023284955872 0.985988357522064 33 0.0274795912944455 0.054959182588891 0.972520408705555 34 0.0372981750562411 0.0745963501124822 0.962701824943759 35 0.0462477476595275 0.092495495319055 0.953752252340473 36 0.0483811293457619 0.0967622586915237 0.951618870654238 37 0.050921848212433 0.101843696424866 0.949078151787567 38 0.05690725395533 0.11381450791066 0.94309274604467 39 0.0787574776741498 0.157514955348300 0.92124252232585 40 0.168044457869602 0.336088915739204 0.831955542130398 41 0.634515865950173 0.730968268099655 0.365484134049827 42 0.877698865358447 0.244602269283106 0.122301134641553 43 0.843466168235085 0.313067663529829 0.156533831764915 44 0.869982635595988 0.260034728808023 0.130017364404012 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0689655172413793 NOK 5% type I error level 9 0.310344827586207 NOK 10% type I error level 15 0.517241379310345 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/10p9kk1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/10p9kk1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/1wcdu1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/1wcdu1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/2v88g1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/2v88g1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/32bq91258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/32bq91258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/4zk3o1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/4zk3o1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/5vkrm1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/5vkrm1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/609fc1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/609fc1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/7mvnm1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/7mvnm1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/8b7zw1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/8b7zw1258716014.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/923yv1258716014.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258716079w93fq2xga4vo18o/923yv1258716014.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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