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 04:57:21 -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/t12587183057jpyof69yzip8xy.htm/, Retrieved Fri, 20 Nov 2009 12:58:38 +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/t12587183057jpyof69yzip8xy.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:

bhschhwsws7

Dataseries X:

» Textbox « » Textfile « » CSV «

126.51 0 131.02 0 136.51 0 138.04 0 132.92 0 129.61 0 122.96 0 124.04 0 121.29 0 124.56 0 118.53 0 113.14 0 114.15 0 122.17 0 129.23 0 131.19 0 129.12 0 128.28 0 126.83 0 138.13 0 140.52 0 146.83 0 135.14 0 131.84 0 125.7 0 128.98 0 133.25 0 136.76 0 133.24 0 128.54 0 121.08 0 120.23 0 119.08 0 125.75 0 126.89 0 126.6 0 121.89 0 123.44 0 126.46 0 129.49 0 127.78 0 125.29 0 119.02 0 119.96 0 122.86 0 131.89 0 132.73 0 135.01 0 136.71 1 142.73 1 144.43 1 144.93 1 138.75 1 130.22 1 122.19 1 128.4 1 140.43 1 153.5 1 149.33 1 142.97 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] = + 128.461123908783 + 12.5549986638161X[t] -0.0269166221272041t + 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) 128.461123908783 2.14339 59.9336 0 0 X 12.5549986638161 3.290926 3.815 0.000337 0.000169 t -0.0269166221272041 0.076011 -0.3541 0.72456 0.36228 Multiple Linear Regression - Regression Statistics Multiple R 0.549347680748939 R-squared 0.301782874344238 Adjusted R-squared 0.277284027830000 F-TEST (value) 12.3182482966641 F-TEST (DF numerator) 2 F-TEST (DF denominator) 57 p-value 3.57873186478397e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.35190131548342 Sum Squared Residuals 3080.87581829859 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 126.51 128.434207286656 -1.92420728665622 2 131.02 128.407290664529 2.61270933547124 3 136.51 128.380374042402 8.12962595759844 4 138.04 128.353457420274 9.68654257972565 5 132.92 128.326540798147 4.59345920185285 6 129.61 128.29962417602 1.31037582398008 7 122.96 128.272707553893 -5.31270755389274 8 124.04 128.245790931766 -4.20579093176552 9 121.29 128.218874309638 -6.92887430963832 10 124.56 128.191957687511 -3.63195768751112 11 118.53 128.165041065384 -9.63504106538391 12 113.14 128.138124443257 -14.9981244432567 13 114.15 128.111207821130 -13.9612078211295 14 122.17 128.084291199002 -5.9142911990023 15 129.23 128.057374576875 1.17262542312489 16 131.19 128.030457954748 3.1595420452521 17 129.12 128.003541332621 1.11645866737931 18 128.28 127.976624710493 0.303375289506514 19 126.83 127.949708088366 -1.11970808836628 20 138.13 127.922791466239 10.2072085337609 21 140.52 127.895874844112 12.6241251558881 22 146.83 127.868958221985 18.9610417780153 23 135.14 127.842041599857 7.29795840014252 24 131.84 127.815124977730 4.02487502226974 25 125.7 127.788208355603 -2.08820835560306 26 128.98 127.761291733476 1.21870826652414 27 133.25 127.734375111349 5.51562488865135 28 136.76 127.707458489221 9.05254151077854 29 133.24 127.680541867094 5.55945813290577 30 128.54 127.653625244967 0.886374755032955 31 121.08 127.626708622840 -6.54670862283983 32 120.23 127.599792000713 -7.36979200071262 33 119.08 127.572875378585 -8.49287537858543 34 125.75 127.545958756458 -1.79595875645822 35 126.89 127.519042134331 -0.629042134331016 36 126.6 127.492125512204 -0.892125512203818 37 121.89 127.465208890077 -5.57520889007661 38 123.44 127.438292267949 -3.99829226794941 39 126.46 127.411375645822 -0.951375645822206 40 129.49 127.384459023695 2.10554097630501 41 127.78 127.357542401568 0.422457598432209 42 125.29 127.330625779441 -2.04062577944058 43 119.02 127.303709157313 -8.28370915731339 44 119.96 127.276792535186 -7.31679253518619 45 122.86 127.249875913059 -4.38987591305898 46 131.89 127.222959290932 4.66704070906822 47 132.73 127.196042668805 5.53395733119542 48 135.01 127.169126046677 7.84087395332263 49 136.71 139.697208088366 -2.98720808836628 50 142.73 139.670291466239 3.0597085337609 51 144.43 139.643374844112 4.78662515588812 52 144.93 139.616458221985 5.31354177801533 53 138.75 139.589541599857 -0.839541599857476 54 130.22 139.562624977730 -9.34262497773027 55 122.19 139.535708355603 -17.3457083556031 56 128.4 139.508791733476 -11.1087917334759 57 140.43 139.481875111349 0.948124888651347 58 153.5 139.454958489221 14.0450415107785 59 149.33 139.428041867094 9.90195813290576 60 142.97 139.401125244967 3.56887475503295 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.328221775247391 0.656443550494783 0.671778224752609 7 0.411547709169877 0.823095418339754 0.588452290830123 8 0.300960749650611 0.601921499301222 0.699039250349389 9 0.218385290794192 0.436770581588383 0.781614709205808 10 0.136359048162428 0.272718096324855 0.863640951837573 11 0.0963626867242392 0.192725373448478 0.903637313275761 12 0.109083737589500 0.218167475179001 0.8909162624105 13 0.0970191407259977 0.194038281451995 0.902980859274002 14 0.127289723083146 0.254579446166292 0.872710276916854 15 0.307075177197388 0.614150354394776 0.692924822802612 16 0.454922066418515 0.90984413283703 0.545077933581485 17 0.46920026816159 0.93840053632318 0.53079973183841 18 0.444328486910244 0.888656973820488 0.555671513089756 19 0.400226264637466 0.800452529274933 0.599773735362534 20 0.549737647369813 0.900524705260373 0.450262352630187 21 0.679511591139222 0.640976817721556 0.320488408860778 22 0.897407331320771 0.205185337358458 0.102592668679229 23 0.878975721645666 0.242048556708668 0.121024278354334 24 0.843729757478119 0.312540485043762 0.156270242521881 25 0.812496115369735 0.375007769260531 0.187503884630265 26 0.76160976556598 0.47678046886804 0.23839023443402 27 0.729938219670281 0.540123560659437 0.270061780329719 28 0.769580287023121 0.460839425953759 0.230419712976879 29 0.781185382398003 0.437629235203995 0.218814617601997 30 0.766505042245762 0.466989915508476 0.233494957754238 31 0.767934342660876 0.464131314678248 0.232065657339124 32 0.762370104656777 0.475259790686446 0.237629895343223 33 0.759914359379593 0.480171281240814 0.240085640620407 34 0.703234835321377 0.593530329357247 0.296765164678623 35 0.644070271678104 0.711859456643792 0.355929728321896 36 0.580775323261978 0.838449353476043 0.419224676738022 37 0.521896709256079 0.956206581487842 0.478103290743921 38 0.449198220448727 0.898396440897454 0.550801779551273 39 0.373295891647628 0.746591783295256 0.626704108352372 40 0.322439407926381 0.644878815852762 0.677560592073619 41 0.261988868479419 0.523977736958837 0.738011131520581 42 0.199603584463214 0.399207168926429 0.800396415536786 43 0.188044058306229 0.376088116612458 0.811955941693771 44 0.183674480595185 0.367348961190369 0.816325519404815 45 0.170611890879928 0.341223781759856 0.829388109120072 46 0.127857705085191 0.255715410170382 0.872142294914809 47 0.0930385529357288 0.186077105871458 0.906961447064271 48 0.0674655840832005 0.134931168166401 0.9325344159168 49 0.0403599452578215 0.080719890515643 0.959640054742179 50 0.0292040898764956 0.0584081797529912 0.970795910123504 51 0.0313294618388865 0.062658923677773 0.968670538161113 52 0.0780553232264857 0.156110646452971 0.921944676773514 53 0.161557937737595 0.323115875475189 0.838442062262405 54 0.136058689191829 0.272117378383659 0.86394131080817 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 3 0.0612244897959184 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/10a3f01258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/10a3f01258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/15fdb1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/15fdb1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/2jnhb1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/2jnhb1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/3z2qz1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/3z2qz1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/4llbe1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/4llbe1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/5hr5b1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/5hr5b1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/634941258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/634941258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/7iszj1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/7iszj1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/8aria1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/8aria1258718236.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/9mzwz1258718236.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587183057jpyof69yzip8xy/9mzwz1258718236.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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