*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: Sat, 21 Nov 2009 03:59:25 -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/21/t12588012139ln8uzti3v87hag.htm/, Retrieved Sat, 21 Nov 2009 12:00:25 +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/21/t12588012139ln8uzti3v87hag.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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114 1 113.8 1 113.6 1 113.7 1 114.2 1 114.8 0 115.2 1 115.3 1 114.9 1 115.1 0 116 0 116 0 116 0 115.9 1 115.6 1 116.6 1 116.9 0 117.9 1 117.9 1 117.7 0 117.4 1 117.3 0 119 1 119.1 0 119 0 118.5 0 117 1 117.5 1 118.2 1 118.2 1 118.3 0 118.2 1 117.9 1 117.8 0 118.6 0 118.9 0 120.8 1 121.8 1 121.3 0 121.9 1 122 1 121.9 0 122 1 122.2 0 123 1 123.1 0 124.9 1 125.4 0 124.7 0 124.4 1 124 0 125 1 125.1 0 125.4 0 125.7 1 126.4 1 125.7 1 125.4 0 126.4 1 126.2 0

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 CPItot[t] = + 121.12 -1.02727272727274CPIlandbouw[t] -1.80909090909093M1[t] -1.41818181818181M2[t] -2.20363636363636M3[t] -1.15272727272726M4[t] -1.22363636363636M5[t] -1.0690909090909M6[t] -0.47818181818181M7[t] -0.543636363636355M8[t] -0.31272727272726M9[t] -1.38M10[t] + 0.476363636363643M11[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) 121.12 1.937919 62.5 0 0 CPIlandbouw -1.02727272727274 1.460761 -0.7032 0.485372 0.242686 M1 -1.80909090909093 2.802226 -0.6456 0.521684 0.260842 M2 -1.41818181818181 2.97938 -0.476 0.63628 0.31814 M3 -2.20363636363636 2.877366 -0.7659 0.447594 0.223797 M4 -1.15272727272726 3.105621 -0.3712 0.712176 0.356088 M5 -1.22363636363636 2.877366 -0.4253 0.672586 0.336293 M6 -1.0690909090909 2.802226 -0.3815 0.704541 0.35227 M7 -0.47818181818181 2.97938 -0.1605 0.873177 0.436589 M8 -0.543636363636355 2.877366 -0.1889 0.850957 0.425478 M9 -0.31272727272726 3.105621 -0.1007 0.920219 0.46011 M10 -1.38 2.740631 -0.5035 0.616941 0.308471 M11 0.476363636363643 2.877366 0.1656 0.869217 0.434608 Multiple Linear Regression - Regression Statistics Multiple R 0.226426138076746 R-squared 0.0512687960043497 Adjusted R-squared -0.190960447569008 F-TEST (value) 0.211654031726451 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.997170235499864 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.33331835537758 Sum Squared Residuals 882.549454545456 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114 118.283636363636 -4.28363636363644 2 113.8 118.674545454545 -4.87454545454545 3 113.6 117.889090909091 -4.2890909090909 4 113.7 118.94 -5.24 5 114.2 118.869090909091 -4.6690909090909 6 114.8 120.050909090909 -5.25090909090911 7 115.2 119.614545454545 -4.41454545454545 8 115.3 119.549090909091 -4.24909090909091 9 114.9 119.78 -4.88 10 115.1 119.74 -4.64000000000001 11 116 121.596363636364 -5.59636363636364 12 116 121.12 -5.12 13 116 119.310909090909 -3.31090909090908 14 115.9 118.674545454545 -2.77454545454545 15 115.6 117.889090909091 -2.28909090909091 16 116.6 118.94 -2.34000000000001 17 116.9 119.896363636364 -2.99636363636364 18 117.9 119.023636363636 -1.12363636363636 19 117.9 119.614545454545 -1.71454545454545 20 117.7 120.576363636364 -2.87636363636364 21 117.4 119.78 -2.38 22 117.3 119.74 -2.44000000000000 23 119 120.569090909091 -1.56909090909091 24 119.1 121.12 -2.02000000000000 25 119 119.310909090909 -0.310909090909073 26 118.5 119.701818181818 -1.20181818181819 27 117 117.889090909091 -0.889090909090904 28 117.5 118.94 -1.44000000000000 29 118.2 118.869090909091 -0.669090909090905 30 118.2 119.023636363636 -0.82363636363636 31 118.3 120.641818181818 -2.34181818181819 32 118.2 119.549090909091 -1.34909090909090 33 117.9 119.78 -1.88 34 117.8 119.74 -1.94000000000000 35 118.6 121.596363636364 -2.99636363636365 36 118.9 121.12 -2.21999999999999 37 120.8 118.283636363636 2.51636363636366 38 121.8 118.674545454545 3.12545454545455 39 121.3 118.916363636364 2.38363636363636 40 121.9 118.94 2.96000000000000 41 122 118.869090909091 3.13090909090909 42 121.9 120.050909090909 1.84909090909091 43 122 119.614545454545 2.38545454545455 44 122.2 120.576363636364 1.62363636363636 45 123 119.78 3.22000000000000 46 123.1 119.74 3.35999999999999 47 124.9 120.569090909091 4.3309090909091 48 125.4 121.12 4.28000000000001 49 124.7 119.310909090909 5.38909090909093 50 124.4 118.674545454545 5.72545454545455 51 124 118.916363636364 5.08363636363636 52 125 118.94 6.06 53 125.1 119.896363636364 5.20363636363635 54 125.4 120.050909090909 5.34909090909091 55 125.7 119.614545454545 6.08545454545455 56 126.4 119.549090909091 6.8509090909091 57 125.7 119.78 5.92 58 125.4 119.74 5.66000000000001 59 126.4 120.569090909091 5.8309090909091 60 126.2 121.12 5.08000000000001 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.100547587614722 0.201095175229445 0.899452412385278 17 0.0380663931256433 0.0761327862512867 0.961933606874357 18 0.0666291685949199 0.133258337189840 0.93337083140508 19 0.0466789041028267 0.0933578082056533 0.953321095897173 20 0.0261095034003548 0.0522190068007096 0.973890496599645 21 0.0186283265025068 0.0372566530050136 0.981371673497493 22 0.0125622495801014 0.0251244991602028 0.987437750419899 23 0.0131255832336783 0.0262511664673565 0.986874416766322 24 0.0115729077556159 0.0231458155112317 0.988427092244384 25 0.0146041753393795 0.0292083506787591 0.98539582466062 26 0.0133268469391413 0.0266536938782825 0.986673153060859 27 0.0116061167549792 0.0232122335099585 0.98839388324502 28 0.0114959582916885 0.022991916583377 0.988504041708312 29 0.0139351467129258 0.0278702934258517 0.986064853287074 30 0.0164694538862224 0.0329389077724449 0.983530546113778 31 0.0122307307407537 0.0244614614815074 0.987769269259246 32 0.0172843215493617 0.0345686430987234 0.982715678450638 33 0.0252795539956810 0.0505591079913619 0.97472044600432 34 0.0424634759807013 0.0849269519614025 0.957536524019299 35 0.086707417768969 0.173414835537938 0.913292582231031 36 0.259091449689726 0.518182899379453 0.740908550310273 37 0.389740635101265 0.779481270202531 0.610259364898735 38 0.498423578301869 0.996847156603738 0.501576421698131 39 0.559023591391972 0.881952817216055 0.440976408608028 40 0.625881129585552 0.748237740828897 0.374118870414448 41 0.811735810703998 0.376528378592003 0.188264189296002 42 0.846754310817758 0.306491378364484 0.153245689182242 43 0.907807953950998 0.184384092098005 0.0921920460490023 44 0.809440252719386 0.381119494561229 0.190559747280614 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 12 0.413793103448276 NOK 10% type I error level 17 0.586206896551724 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/10a4fl1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/10a4fl1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/1hx9a1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/1hx9a1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/2c3pk1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/2c3pk1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/3974z1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/3974z1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/4fphy1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/4fphy1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/5vw711258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/5vw711258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/636s01258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/636s01258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/7h5fm1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/7h5fm1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/8cpqg1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/8cpqg1258801160.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/9wcwi1258801160.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t12588012139ln8uzti3v87hag/9wcwi1258801160.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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