*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: Mon, 21 Dec 2009 04:47:07 -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/Dec/21/t1261396102grgn1yclaphgf24.htm/, Retrieved Mon, 21 Dec 2009 12:48:34 +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/Dec/21/t1261396102grgn1yclaphgf24.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 «

19169 0 20366 13807 0 22782 29743 0 19169 25591 0 13807 29096 0 29743 26482 0 25591 22405 0 29096 27044 0 26482 17970 0 22405 18730 0 27044 19684 0 17970 19785 0 18730 18479 0 19684 10698 0 19785 31956 0 18479 29506 0 10698 34506 0 31956 27165 0 29506 26736 0 34506 23691 0 27165 18157 0 26736 17328 0 23691 18205 0 18157 20995 0 17328 17382 0 18205 9367 0 20995 31124 0 17382 26551 0 9367 30651 0 31124 25859 0 26551 25100 0 30651 25778 0 25859 20418 0 25100 18688 0 25778 20424 0 20418 24776 0 18688 19814 0 20424 12738 0 24776 31566 0 19814 30111 0 12738 30019 0 31566 31934 1 30111 25826 1 30019 26835 1 31934 20205 1 25826 17789 1 26835 20520 1 20205 22518 1 17789 15572 1 20520 11509 1 22518 25447 1 15572 24090 1 11509 27786 1 25447 26195 1 24090 20516 1 27786 22759 1 26195 19028 1 20516 16971 1 22759

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] = + 12622.3004248372 -964.441332928536X[t] + 0.511778704714425Y2[t] -4797.16832219451M1[t] -12461.6239550917M2[t] + 7962.03262905486M3[t] + 8458.52123356123M4[t] + 2300.66498077665M5[t] + 1028.70823520450M6[t] -4052.67073046471M7[t] -1483.48863957148M8[t] -5815.81330573939M9[t] -7647.52117943388M10[t] -2837.64204936683M11[t] + 11.8947607259918t + 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) 12622.3004248372 2589.192635 4.875 1.5e-05 8e-06 X -964.441332928536 846.849829 -1.1389 0.261068 0.130534 Y2 0.511778704714425 0.128606 3.9794 0.000261 0.000131 M1 -4797.16832219451 1205.962828 -3.9779 0.000262 0.000131 M2 -12461.6239550917 1291.873158 -9.6462 0 0 M3 7962.03262905486 1185.090271 6.7185 0 0 M4 8458.52123356123 1453.515165 5.8194 1e-06 0 M5 2300.66498077665 1925.199183 1.195 0.238626 0.119313 M6 1028.70823520450 1671.008789 0.6156 0.54139 0.270695 M7 -4052.67073046471 1985.499802 -2.0411 0.047405 0.023703 M8 -1483.48863957148 1702.747711 -0.8712 0.388466 0.194233 M9 -5815.81330573939 1421.664046 -4.0908 0.000185 9.3e-05 M10 -7647.52117943388 1504.749652 -5.0823 8e-06 4e-06 M11 -2837.64204936683 1255.725187 -2.2598 0.028958 0.014479 t 11.8947607259918 22.763809 0.5225 0.603984 0.301992 Multiple Linear Regression - Regression Statistics Multiple R 0.965018983561122 R-squared 0.93126163863334 Adjusted R-squared 0.90888170702559 F-TEST (value) 41.6114604349759 F-TEST (DF numerator) 14 F-TEST (DF denominator) 43 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1765.33289651167 Sum Squared Residuals 134005210.126770 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 19169 18259.9119635826 909.08803641736 2 13807 11843.8084420015 1963.19155799852 3 29743 30430.3033267408 -687.303326740833 4 25591 28194.5292772944 -2603.52927729443 5 29096 30204.2732235649 -1108.27322356492 6 26482 26819.3060567445 -337.306056744478 7 22405 23543.6062118253 -1138.60621182532 8 27044 24786.8935293210 2257.10647067896 9 17970 18379.9418447584 -409.941844758405 10 18730 18934.2701429601 -204.270142960129 11 19684 19112.1640671745 571.83593282553 12 19785 22350.6526928503 -2565.65269285026 13 18479 18053.6160156793 425.383984320706 14 10698 10452.7447926842 245.255207315755 15 31956 30219.9131491998 1736.08685080022 16 29506 26746.1464130492 2759.85358695081 17 34506 31479.5766258099 3026.42337419014 18 27165 28965.6568144134 -1800.65681441335 19 26736 26455.0661330423 280.933866957746 20 23691 25279.1755133529 -1588.17551335289 21 18157 20739.1925435885 -2582.19254358848 22 17328 17361.0132747646 -33.0132747645572 23 18205 19350.6038136680 -1145.60381366797 24 20995 21775.8760775525 -780.876077552536 25 17382 17439.4324401186 -57.4324401185665 26 9367 11214.7341541006 -1847.7341541006 27 31124 29801.2290388400 1322.77096116005 28 26551 26207.7060857862 343.293914213805 29 30651 31196.5138721994 -545.513872199357 30 25859 27596.0878706941 -1737.08787069413 31 25100 24624.8963550800 475.103644919953 32 25778 24753.5296537078 1024.47034629225 33 20418 20044.6597113876 373.340288612422 34 18688 18571.8325602155 116.167439784537 35 20424 20650.4725937392 -226.472593739185 36 24776 22614.6322446761 2161.36775532395 37 19814 18717.8065145918 1096.19348540822 38 12738 13292.5065653377 -554.506565337742 39 31566 31188.6119774173 377.388022582666 40 30111 28075.6492280904 2035.35077190958 41 30019 31565.457188395 -1546.45718839504 42 31934 28596.3158552608 3337.68414473916 43 25826 23479.7480094839 2346.25199051610 44 26835 27040.8810806313 -205.88108063125 45 20205 19594.5068467936 610.493153206382 46 17789 18291.0784468820 -502.078446881973 47 20520 19719.7595254184 800.240474581624 48 22518 21332.8389849211 1185.16101507885 49 15572 17945.2330660277 -2373.23306602772 50 11509 11315.2060458759 193.793954124068 51 25447 28195.9425078021 -2748.94250780211 52 24090 26624.9689957798 -2534.96899577976 53 27786 27612.1790900308 173.820909969170 54 26195 25657.6334028872 537.366597112806 55 20516 22479.6832905685 -1963.68329056848 56 22759 24246.5202229871 -1487.52022298707 57 19028 17019.6990534719 2008.30094652808 58 16971 16347.8055751779 623.194424822123 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.922315165795205 0.155369668409589 0.0776848342047946 19 0.851490978042437 0.297018043915126 0.148509021957563 20 0.91193994985954 0.176120100280920 0.0880600501404602 21 0.916322110752124 0.167355778495753 0.0836778892478764 22 0.862566504672812 0.274866990654376 0.137433495327188 23 0.820808667779193 0.358382664441614 0.179191332220807 24 0.789461451453096 0.421077097093808 0.210538548546904 25 0.718996884272188 0.562006231455624 0.281003115727812 26 0.740743436591843 0.518513126816314 0.259256563408157 27 0.679707875166363 0.640584249667274 0.320292124833637 28 0.576213595020098 0.847572809959803 0.423786404979902 29 0.483651125104601 0.967302250209202 0.516348874895399 30 0.610054395057968 0.779891209884064 0.389945604942032 31 0.522163005132934 0.955673989734131 0.477836994867066 32 0.416860675910889 0.833721351821778 0.583139324089111 33 0.426093826145877 0.852187652291754 0.573906173854123 34 0.409741359783341 0.819482719566683 0.590258640216659 35 0.459275096479641 0.918550192959283 0.540724903520359 36 0.488611141677233 0.977222283354466 0.511388858322767 37 0.362112018880892 0.724224037761783 0.637887981119108 38 0.442516987725139 0.885033975450279 0.557483012274861 39 0.364354833021102 0.728709666042205 0.635645166978898 40 0.279805765371553 0.559611530743107 0.720194234628447 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 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/10d9qe1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/10d9qe1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/1jq721261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/1jq721261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/27nfb1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/27nfb1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/3m1vu1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/3m1vu1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/49cwx1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/49cwx1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/58bvy1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/58bvy1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/6sig01261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/6sig01261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/796oa1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/796oa1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/8up1d1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/8up1d1261396021.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/9e8zp1261396021.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261396102grgn1yclaphgf24/9e8zp1261396021.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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Software written by Ed van Stee & Patrick Wessa

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