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R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 22 Nov 2009 12:23:46 -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/22/t1258917879pt25dq99subbrnd.htm/, Retrieved Sun, 22 Nov 2009 20:24:51 +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/22/t1258917879pt25dq99subbrnd.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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17823.2 1.2218 17872 1.249 17420.4 1.2991 16704.4 1.3408 15991.2 1.3119 16583.6 1.3014 19123.5 1.3201 17838.7 1.2938 17209.4 1.2694 18586.5 1.2165 16258.1 1.2037 15141.6 1.2292 19202.1 1.2256 17746.5 1.2015 19090.1 1.1786 18040.3 1.1856 17515.5 1.2103 17751.8 1.1938 21072.4 1.202 17170 1.2271 19439.5 1.277 19795.4 1.265 17574.9 1.2684 16165.4 1.2811 19464.6 1.2727 19932.1 1.2611 19961.2 1.2881 17343.4 1.3213 18924.2 1.2999 18574.1 1.3074 21350.6 1.3242 18594.6 1.3516 19823.1 1.3511 20844.4 1.3419 19640.2 1.3716 17735.4 1.3622 19813.6 1.3896 22160 1.4227 20664.3 1.4684 17877.4 1.457 20906.5 1.4718 21164.1 1.4748 22786.7 1.437 22321.5 1.3322 17842.2 1.2732 16373.5 1.3449 15993.8 1.3239 16446.1 1.2785 17729 1.305 16643 1.319 16196.7 1.365 18252.1 1.4016 17570.4 1.4088 15836.8 1.4268

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 EUDO[t] = + 0.962415386308849 + 1.98712515138476e-05UITV[t] -0.0531840779040217M1[t] -0.0467402096762419M2[t] -0.0135028975421446M3[t] + 0.0282457901815517M4[t] + 0.0168342620170412M5[t] + 0.0210981792690236M6[t] -0.0605419433507508M7[t] -0.0384205855434918M8[t] -0.0389194261214411M9[t] -0.0459060463579918M10[t] -0.0156144435369607M11[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) 0.962415386308849 0.125251 7.6839 0 0 UITV 1.98712515138476e-05 7e-06 2.7376 0.009111 0.004556 M1 -0.0531840779040217 0.055948 -0.9506 0.347382 0.173691 M2 -0.0467402096762419 0.056097 -0.8332 0.409561 0.20478 M3 -0.0135028975421446 0.055636 -0.2427 0.809447 0.404724 M4 0.0282457901815517 0.053881 0.5242 0.602944 0.301472 M5 0.0168342620170412 0.054685 0.3078 0.759764 0.379882 M6 0.0210981792690236 0.054356 0.3881 0.699914 0.349957 M7 -0.0605419433507508 0.065578 -0.9232 0.361303 0.180651 M8 -0.0384205855434918 0.059074 -0.6504 0.519076 0.259538 M9 -0.0389194261214411 0.058203 -0.6687 0.507445 0.253723 M10 -0.0459060463579918 0.058888 -0.7796 0.440132 0.220066 M11 -0.0156144435369607 0.05642 -0.2768 0.78336 0.39168 Multiple Linear Regression - Regression Statistics Multiple R 0.472525101596154 R-squared 0.223279971638456 Adjusted R-squared -0.00405271958931319 F-TEST (value) 0.98217273737699 F-TEST (DF numerator) 12 F-TEST (DF denominator) 41 p-value 0.48127171157826 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0791341574222953 Sum Squared Residuals 0.256750809708401 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.2218 1.26340059838643 -0.0416005983864301 2 1.249 1.27081418368809 -0.0218141836880902 3 1.2991 1.29507763863853 0.00402236136146588 4 1.3408 1.32259851027832 0.0182014897216845 5 1.3119 1.29701480553413 0.0148851944658712 6 1.3014 1.31305045218291 -0.0116504521829146 7 1.3201 1.28188132128316 0.0382186787168384 8 1.2938 1.27847209514543 0.0153279048545709 9 1.2694 1.26546827598982 0.00393172401018444 10 1.2165 1.28584635621298 -0.0693463562129846 11 1.2037 1.26986973700917 -0.0661697370091729 12 1.2292 1.26329792823092 -0.0340979282309227 13 1.2256 1.29080106709888 -0.065201067098879 14 1.2015 1.26832034162310 -0.0668203416231023 15 1.1786 1.32825666729121 -0.149656667291205 16 1.1856 1.34914451517566 -0.163544515175664 17 1.2103 1.32730455421669 -0.117004554216687 18 1.1938 1.33626404820139 -0.142464048201391 19 1.202 1.3206084033585 -0.118608403358499 20 1.2271 1.26518418925812 -0.0380841892581193 21 1.277 1.30978315399085 -0.0327831539908471 22 1.265 1.30986871216807 -0.0448687121680749 23 1.2684 1.29603620100261 -0.0276362010026074 24 1.2811 1.2836421155308 -0.00254211553079997 25 1.2727 1.29601727062126 -0.023317270621264 26 1.2611 1.31175094893177 -0.0506509489317674 27 1.2881 1.34556651448492 -0.0574665144849179 28 1.3213 1.33529623999566 -0.0139962399956642 29 1.2999 1.35529718622424 -0.0553971862242437 30 1.3074 1.35260417832123 -0.0452041783212281 31 1.3242 1.32613658552965 -0.00193658552965142 32 1.3516 1.29349277416475 0.0581072258352534 33 1.3511 1.31740576607156 0.033694233928441 34 1.3419 1.33071365500610 0.0111863449938992 35 1.3716 1.33707629675416 0.0345237032458432 36 1.3622 1.31483998040754 0.0473600195924595 37 1.3896 1.30295233739960 0.0866476626004032 38 1.4227 1.35602211017947 0.0666778898205315 39 1.4684 1.35953799142430 0.108862008575696 40 1.457 1.34590748830406 0.111092511695941 41 1.4718 1.39468796810014 0.0771120318998562 42 1.4748 1.40407071974209 0.0707292802579069 43 1.437 1.35467368982869 0.0823263101713121 44 1.3322 1.36755094143171 -0.035350941431705 45 1.2732 1.27804280394778 -0.00484280394777827 46 1.3449 1.24187127661284 0.103028723387160 47 1.3239 1.26461776523406 0.0592822347659371 48 1.2785 1.28921997583074 -0.0107199758307369 49 1.305 1.26152872649383 0.0434712735061698 50 1.319 1.24639241557757 0.0726075844224284 51 1.365 1.27076118816104 0.0942388118389612 52 1.4016 1.35335324624630 0.0482467537537027 53 1.4088 1.32839548592480 0.080404514075203 54 1.4268 1.29821060155237 0.128589398447627 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.269195697074848 0.538391394149696 0.730804302925152 17 0.160905952853980 0.321811905707959 0.83909404714602 18 0.131334507196459 0.262669014392918 0.86866549280354 19 0.0958009791514056 0.191601958302811 0.904199020848594 20 0.169314141351803 0.338628282703607 0.830685858648197 21 0.250636497009908 0.501272994019816 0.749363502990092 22 0.284674260570710 0.569348521141419 0.71532573942929 23 0.326504718608931 0.653009437217861 0.67349528139107 24 0.287356282389451 0.574712564778903 0.712643717610549 25 0.283465194589195 0.56693038917839 0.716534805410805 26 0.327799092047586 0.655598184095172 0.672200907952414 27 0.460098698656640 0.920197397313279 0.53990130134336 28 0.482319397964011 0.964638795928023 0.517680602035989 29 0.639774860492202 0.720450279015595 0.360225139507797 30 0.882348232882573 0.235303534234853 0.117651767117427 31 0.923187585753263 0.153624828493475 0.0768124142467374 32 0.945314394976446 0.109371210047109 0.0546856050235543 33 0.934999090540105 0.130001818919791 0.0650009094598955 34 0.959414510633047 0.0811709787339058 0.0405854893669529 35 0.93458378903104 0.130832421937919 0.0654162109689593 36 0.936522027138534 0.126955945722932 0.063477972861466 37 0.93361910330927 0.13276179338146 0.06638089669073 38 0.858159719819853 0.283680560360295 0.141840280180147 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 1 0.0434782608695652 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/10lgz31258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/10lgz31258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/15xbq1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/15xbq1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/2095s1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/2095s1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/39i5y1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/39i5y1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/4hu2i1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/4hu2i1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/5u0b51258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/5u0b51258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/6cowb1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/6cowb1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/7tord1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/7tord1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/8deum1258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/8deum1258917820.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/9jt151258917820.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258917879pt25dq99subbrnd/9jt151258917820.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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