M4

*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: Thu, 19 Nov 2009 01:28:14 -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/19/t1258620048qke0co73idelqno.htm/, Retrieved Thu, 19 Nov 2009 09:41:01 +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/19/t1258620048qke0co73idelqno.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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23 2497,84 21 25 19 21 23 2645,64 23 21 25 19 19 2756,76 23 23 21 25 18 2849,27 19 23 23 21 19 2921,44 18 19 23 23 19 2981,85 19 18 19 23 22 3080,58 19 19 18 19 23 3106,22 22 19 19 18 20 3119,31 23 22 19 19 14 3061,26 20 23 22 19 14 3097,31 14 20 23 22 14 3161,69 14 14 20 23 15 3257,16 14 14 14 20 11 3277,01 15 14 14 14 17 3295,32 11 15 14 14 16 3363,99 17 11 15 14 20 3494,17 16 17 11 15 24 3667,03 20 16 17 11 23 3813,06 24 20 16 17 20 3917,96 23 24 20 16 21 3895,51 20 23 24 20 19 3801,06 21 20 23 24 23 3570,12 19 21 20 23 23 3701,61 23 19 21 20 23 3862,27 23 23 19 21 23 3970,1 23 23 23 19 27 4138,52 23 23 23 23 26 4199,75 27 23 23 23 17 4290,89 26 27 23 23 24 4443,91 17 26 27 23 26 4502,64 24 17 26 27 24 4356,98 26 24 17 26 27 4591,27 24 26 24 17 27 4696,96 27 24 26 24 26 4621,4 27 27 24 26 24 4562,84 26 27 27 24 23 4202,52 24 26 27 27 23 4296,49 23 24 26 27 24 4435,23 23 23 24 26 17 4105,18 24 23 23 24 21 4116,68 17 24 23 23 19 3844,49 21 17 24 23 22 3720,98 19 21 1 etc...

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Consvertr[t] = -2.30802815173133 + 0.0038777162416759Aand[t] + 0.405105033674106Y1[t] + 0.0408529815879891Y2[t] + 0.108240589410743Y3[t] -0.0693297363740693Y4[t] + 2.15292212174366M1[t] + 1.04935628306701M2[t] + 1.59721168826427M3[t] -1.18333001580422M4[t] -0.48610405600735M5[t] + 2.64584732173502M6[t] + 2.47449066262075M7[t] + 1.4250603170624M8[t] + 0.806606050478028M9[t] -1.59756470173195M10[t] + 0.569132549626265M11[t] -0.0939253055905727t + 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) -2.30802815173133 2.604447 -0.8862 0.380948 0.190474 Aand 0.0038777162416759 0.000973 3.985 0.000286 0.000143 Y1 0.405105033674106 0.155759 2.6008 0.013071 0.006536 Y2 0.0408529815879891 0.161486 0.253 0.801611 0.400805 Y3 0.108240589410743 0.16471 0.6572 0.514938 0.257469 Y4 -0.0693297363740693 0.144338 -0.4803 0.633677 0.316839 M1 2.15292212174366 1.978955 1.0879 0.283313 0.141657 M2 1.04935628306701 1.951773 0.5376 0.593879 0.296939 M3 1.59721168826427 1.959735 0.815 0.420014 0.210007 M4 -1.18333001580422 1.964064 -0.6025 0.550335 0.275167 M5 -0.48610405600735 1.968148 -0.247 0.806215 0.403108 M6 2.64584732173502 1.931276 1.37 0.178523 0.089262 M7 2.47449066262075 1.995588 1.24 0.222392 0.111196 M8 1.4250603170624 2.102686 0.6777 0.501942 0.250971 M9 0.806606050478028 2.077054 0.3883 0.699875 0.349938 M10 -1.59756470173195 2.050219 -0.7792 0.440555 0.220278 M11 0.569132549626265 2.044053 0.2784 0.782151 0.391075 t -0.0939253055905727 0.028977 -3.2413 0.002438 0.001219 Multiple Linear Regression - Regression Statistics Multiple R 0.94131041838606 R-squared 0.886065303762138 Adjusted R-squared 0.8364014618123 F-TEST (value) 17.8412557098805 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 2.44582132324922e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.83762272655424 Sum Squared Residuals 314.032006792028 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 23 19.5660603833341 3.43393961666588 2 23 20.3765968497954 2.62340315020458 3 19 20.4941839654657 -1.49418396546573 4 18 16.8518244749454 1.14817552505456 5 19 17.0278034775393 1.97219652246071 6 19 20.2313720822939 -1.23137208229387 7 22 20.5588683798032 1.44113162019679 8 23 20.907822799898 2.09217720010201 9 20 20.7045367753906 -0.704536775390588 10 14 17.1315989385587 -3.13159893855866 11 14 16.6912247883186 -2.69122478831864 12 14 15.6386449106067 -1.63864491060667 13 15 17.6263929690103 -2.62639296901030 14 11 17.3269579440589 -6.32695794405887 15 17 16.2723218749422 0.727678125057794 16 16 16.0395965047024 -0.0395965047024457 17 20 16.4854190210869 3.5145809789131 18 24 22.7000767578440 1.29992324215603 19 23 24.2606707493043 -1.26067074930427 20 20 23.7846865186020 -3.78468651860205 21 21 21.8847275463379 -0.884727546337865 22 19 18.9173677435141 0.0826322564858539 23 23 19.0698707828108 3.93012921718923 24 23 20.7716378062653 2.22836219373470 25 23 23.3312295249624 -0.331229524962437 26 23 23.1234943534262 -0.123494353426229 27 27 23.9531904769597 3.0468095230403 28 26 22.9365761674749 3.06342383252513 29 17 23.6515987726254 -6.65159877262536 30 24 24.0291570570664 -0.0291570570664328 31 26 26.0741122337550 -0.0741122337550348 32 24 24.5572738049851 -0.557273804985117 33 27 25.406552020142 1.59344797985799 34 27 24.1830739539735 2.81692604602647 35 26 25.7302639537145 0.269736046285519 36 24 24.8984032426914 -0.898403242691367 37 23 24.5011290845854 -1.50112908458539 38 23 23.0729753492876 -0.0729753492876153 39 24 23.876895376229 0.123104623770986 40 17 20.1581120380163 -3.15811203801632 41 21 18.0804539112452 2.91954608875479 42 19 21.5056942525665 -2.50569425256649 43 22 19.2876635516067 2.71233644839328 44 22 20.0155635280283 1.98443647197166 45 18 19.6424179460995 -1.64241794609946 46 16 15.7679593639537 0.232040636046336 47 14 15.5086404751561 -1.50864047515611 48 12 11.6913140404367 0.308685959563333 49 14 12.9751880381077 1.02481196189226 50 16 12.0999755034319 3.90002449656813 51 8 10.4034083064033 -2.40340830640335 52 3 4.01389081486092 -1.01389081486092 53 0 1.75472481750323 -1.75472481750323 54 5 2.53369985022924 2.46630014977076 55 1 3.81868508553076 -2.81868508553076 56 1 0.734653348486506 0.265346651513494 57 3 1.36176571203008 1.63823428796992 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.586479818640423 0.827040362719154 0.413520181359577 22 0.703731528375987 0.592536943248025 0.296268471624013 23 0.593612722588303 0.812774554823394 0.406387277411697 24 0.559645768604505 0.88070846279099 0.440354231395495 25 0.517766723361345 0.96446655327731 0.482233276638655 26 0.393347283208745 0.786694566417489 0.606652716791255 27 0.372219529502066 0.744439059004133 0.627780470497934 28 0.496264574356949 0.992529148713898 0.503735425643051 29 0.877805356451622 0.244389287096755 0.122194643548378 30 0.825960538041127 0.348078923917747 0.174039461958873 31 0.74708934423722 0.505821311525561 0.252910655762780 32 0.648054907354857 0.703890185290287 0.351945092645143 33 0.541170215455234 0.917659569089533 0.458829784544766 34 0.660372912826028 0.679254174347945 0.339627087173972 35 0.698573397286325 0.602853205427349 0.301426602713675 36 0.52329116059955 0.9534176788009 0.47670883940045 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/Nov/19/t1258620048qke0co73idelqno/10p4031258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/10p4031258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/17beh1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/17beh1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/2zevu1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/2zevu1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/3mhgp1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/3mhgp1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/49e9n1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/49e9n1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/5ztgv1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/5ztgv1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/6ia4c1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/6ia4c1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/7v8le1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/7v8le1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/8kepi1258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/8kepi1258619288.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/9l6h81258619288.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620048qke0co73idelqno/9l6h81258619288.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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