w7

*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 07:37:35 -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/t1259332784fkcreymvm1l8d7k.htm/, Retrieved Fri, 27 Nov 2009 15:39:56 +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/t1259332784fkcreymvm1l8d7k.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:

» Textbox « » Textfile « » CSV «

286602 0 283042 0 276687 0 277915 0 277128 0 277103 0 275037 0 270150 0 267140 0 264993 0 287259 0 291186 0 292300 0 288186 0 281477 0 282656 0 280190 0 280408 0 276836 0 275216 0 274352 0 271311 0 289802 0 290726 0 292300 0 278506 0 269826 0 265861 0 269034 0 264176 0 255198 0 253353 0 246057 0 235372 0 258556 0 260993 0 254663 0 250643 0 243422 0 247105 0 248541 0 245039 1 237080 1 237085 1 225554 1 226839 1 247934 1 248333 1 246969 1 245098 1 246263 1 255765 1 264319 1 268347 1 273046 1 273963 1 267430 1 271993 1 292710 1 295881 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] = + 271007.512195122 -14710.2490372272X[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) 271007.512195122 2614.882845 103.6404 0 0 X -14710.2490372272 4646.768756 -3.1657 0.002466 0.001233 Multiple Linear Regression - Regression Statistics Multiple R 0.383835800700486 R-squared 0.147329921899383 Adjusted R-squared 0.132628713656269 F-TEST (value) 10.0216199555156 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.00246565947210309 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16743.4197223495 Sum Squared Residuals 16259842031.9281 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 286602 271007.512195122 15594.4878048784 2 283042 271007.512195122 12034.487804878 3 276687 271007.512195122 5679.48780487804 4 277915 271007.512195122 6907.48780487804 5 277128 271007.512195122 6120.48780487804 6 277103 271007.512195122 6095.48780487804 7 275037 271007.512195122 4029.48780487804 8 270150 271007.512195122 -857.512195121959 9 267140 271007.512195122 -3867.51219512196 10 264993 271007.512195122 -6014.51219512196 11 287259 271007.512195122 16251.4878048780 12 291186 271007.512195122 20178.4878048780 13 292300 271007.512195122 21292.4878048780 14 288186 271007.512195122 17178.4878048780 15 281477 271007.512195122 10469.4878048780 16 282656 271007.512195122 11648.4878048780 17 280190 271007.512195122 9182.48780487804 18 280408 271007.512195122 9400.48780487804 19 276836 271007.512195122 5828.48780487804 20 275216 271007.512195122 4208.48780487804 21 274352 271007.512195122 3344.48780487804 22 271311 271007.512195122 303.487804878041 23 289802 271007.512195122 18794.4878048780 24 290726 271007.512195122 19718.4878048780 25 292300 271007.512195122 21292.4878048780 26 278506 271007.512195122 7498.48780487804 27 269826 271007.512195122 -1181.51219512196 28 265861 271007.512195122 -5146.51219512196 29 269034 271007.512195122 -1973.51219512196 30 264176 271007.512195122 -6831.51219512196 31 255198 271007.512195122 -15809.5121951220 32 253353 271007.512195122 -17654.5121951220 33 246057 271007.512195122 -24950.5121951220 34 235372 271007.512195122 -35635.5121951220 35 258556 271007.512195122 -12451.5121951220 36 260993 271007.512195122 -10014.5121951220 37 254663 271007.512195122 -16344.5121951220 38 250643 271007.512195122 -20364.5121951220 39 243422 271007.512195122 -27585.5121951220 40 247105 271007.512195122 -23902.5121951220 41 248541 271007.512195122 -22466.5121951220 42 245039 256297.263157895 -11258.2631578947 43 237080 256297.263157895 -19217.2631578947 44 237085 256297.263157895 -19212.2631578947 45 225554 256297.263157895 -30743.2631578947 46 226839 256297.263157895 -29458.2631578947 47 247934 256297.263157895 -8363.26315789474 48 248333 256297.263157895 -7964.26315789474 49 246969 256297.263157895 -9328.26315789474 50 245098 256297.263157895 -11199.2631578947 51 246263 256297.263157895 -10034.2631578947 52 255765 256297.263157895 -532.263157894737 53 264319 256297.263157895 8021.73684210526 54 268347 256297.263157895 12049.7368421053 55 273046 256297.263157895 16748.7368421053 56 273963 256297.263157895 17665.7368421053 57 267430 256297.263157895 11132.7368421053 58 271993 256297.263157895 15695.7368421053 59 292710 256297.263157895 36412.7368421053 60 295881 256297.263157895 39583.7368421053 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0323721453886792 0.0647442907773584 0.96762785461132 6 0.00925501494068998 0.0185100298813800 0.99074498505931 7 0.00332727446714181 0.00665454893428363 0.996672725532858 8 0.00317082186248486 0.00634164372496972 0.996829178137515 9 0.00379469761209072 0.00758939522418143 0.99620530238791 10 0.0044071329800892 0.0088142659601784 0.99559286701991 11 0.0047117174144863 0.0094234348289726 0.995288282585514 12 0.00746074402227014 0.0149214880445403 0.99253925597773 13 0.0104677252203810 0.0209354504407621 0.989532274779619 14 0.00815882439552617 0.0163176487910523 0.991841175604474 15 0.00420700407353505 0.0084140081470701 0.995792995926465 16 0.00222221956927821 0.00444443913855641 0.997777780430722 17 0.00107860056350060 0.00215720112700121 0.9989213994365 18 0.000518897136586696 0.00103779427317339 0.999481102863413 19 0.000248558347875074 0.000497116695750149 0.999751441652125 20 0.000124110873801592 0.000248221747603184 0.999875889126198 21 6.36529473784436e-05 0.000127305894756887 0.999936347052622 22 4.0296256974876e-05 8.0592513949752e-05 0.999959703743025 23 5.99399760879928e-05 0.000119879952175986 0.999940060023912 24 0.000111180735620443 0.000222361471240886 0.99988881926438 25 0.000308168893983599 0.000616337787967198 0.999691831106016 26 0.000232775175298906 0.000465550350597813 0.999767224824701 27 0.000239095722927884 0.000478191445855769 0.999760904277072 28 0.000337704332587745 0.00067540866517549 0.999662295667412 29 0.000351400219808251 0.000702800439616502 0.999648599780192 30 0.000514143015325288 0.00102828603065058 0.999485856984675 31 0.00168887413991781 0.00337774827983561 0.998311125860082 32 0.00429368573529861 0.00858737147059722 0.995706314264701 33 0.0149757524072456 0.0299515048144911 0.985024247592754 34 0.0764156688437683 0.152831337687537 0.923584331156232 35 0.068204347428761 0.136408694857522 0.931795652571239 36 0.0589458634685662 0.117891726937132 0.941054136531434 37 0.055598866352857 0.111197732705714 0.944401133647143 38 0.056133153325113 0.112266306650226 0.943866846674887 39 0.0685761493484979 0.137152298696996 0.931423850651502 40 0.0677252312043723 0.135450462408745 0.932274768795628 41 0.0613406243195982 0.122681248639196 0.938659375680402 42 0.0446283454644013 0.0892566909288026 0.955371654535599 43 0.0407006569129751 0.0814013138259502 0.959299343087025 44 0.0387043398402938 0.0774086796805876 0.961295660159706 45 0.0809046312718788 0.161809262543758 0.919095368728121 46 0.187254377687168 0.374508755374336 0.812745622312832 47 0.183987544678264 0.367975089356528 0.816012455321736 48 0.184963332511186 0.369926665022371 0.815036667488815 49 0.208866718415123 0.417733436830246 0.791133281584877 50 0.294441330209495 0.58888266041899 0.705558669790505 51 0.47095460796461 0.94190921592922 0.52904539203539 52 0.563897263273629 0.872205473452741 0.436102736726371 53 0.554107976981248 0.891784046037503 0.445892023018752 54 0.499965867094413 0.999931734188826 0.500034132905587 55 0.391860710136905 0.78372142027381 0.608139289863095 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.450980392156863 NOK 5% type I error level 28 0.549019607843137 NOK 10% type I error level 32 0.627450980392157 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/108cjr1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/108cjr1259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/1kx001259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/1kx001259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/28z961259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/28z961259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/30sfh1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/30sfh1259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/4o1uk1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/4o1uk1259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/52zuo1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/52zuo1259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/69o881259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/69o881259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/77t1b1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/77t1b1259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/8bwtl1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/8bwtl1259332650.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/9d3du1259332650.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t1259332784fkcreymvm1l8d7k/9d3du1259332650.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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