*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: Sun, 20 Dec 2009 10:02:26 -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/20/t126132861944yr56w7wi8ftve.htm/, Retrieved Sun, 20 Dec 2009 18:03:51 +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/Dec/20/t126132861944yr56w7wi8ftve.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:

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User-defined keywords:

Dataseries X:

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5560 611 3922 594 3759 595 4138 591 4634 589 3996 584 4308 573 4143 567 4429 569 5219 621 4929 629 5755 628 5592 612 4163 595 4962 597 5208 593 4755 590 4491 580 5732 574 5731 573 5040 573 6102 620 4904 626 5369 620 5578 588 4619 566 4731 557 5011 561 5299 549 4146 532 4625 526 4736 511 4219 499 5116 555 4205 565 4121 542 5103 527 4300 510 4578 514 3809 517 5526 508 4247 493 3830 490 4394 469 4826 478 4409 528 4569 534 4106 518 4794 506 3914 502 3793 516 4405 528 4022 533 4100 536 4788 537 3163 524 3585 536 3903 4178 3863 4187

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] = + 5545.45096713486 -0.178613936386478X[t] + 43.6242818380755M1[t] -931.523238964048M2[t] -737.34522798693M3[t] -574.60293979709M4[t] -229.603780800123M5[t] -869.626245910535M6[t] -397.169978062677M7[t] -609.621116620415M8[t] -610.078828430576M9[t] + 70.0960717549703M10[t] -371.561402011425M11[t] -12.7493375297899t + 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) 5545.45096713486 299.696813 18.5035 0 0 X -0.178613936386478 0.128893 -1.3857 0.172364 0.086182 M1 43.6242818380755 346.62469 0.1259 0.900384 0.450192 M2 -931.523238964048 363.852446 -2.5602 0.013736 0.006868 M3 -737.34522798693 363.376425 -2.0291 0.048128 0.024064 M4 -574.60293979709 362.949809 -1.5831 0.120095 0.060047 M5 -229.603780800123 362.564867 -0.6333 0.529623 0.264811 M6 -869.626245910535 362.233019 -2.4007 0.020373 0.010186 M7 -397.169978062677 361.961702 -1.0973 0.278113 0.139057 M8 -609.621116620415 361.752756 -1.6852 0.098582 0.049291 M9 -610.078828430576 361.587572 -1.6872 0.098187 0.049094 M10 70.0960717549703 373.569404 0.1876 0.851968 0.425984 M11 -371.561402011425 373.494856 -0.9948 0.324917 0.162458 t -12.7493375297899 4.337665 -2.9392 0.005088 0.002544 Multiple Linear Regression - Regression Statistics Multiple R 0.623840267592213 R-squared 0.389176679469524 Adjusted R-squared 0.220225548258967 F-TEST (value) 2.30348667499899 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0.0186249399858107 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 571.339391925744 Sum Squared Residuals 15342148.9360057 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5560 5467.19279631102 92.8072036889818 2 3922 4482.33237489767 -560.332374897667 3 3759 4663.58243440861 -904.582434408608 4 4138 4814.2898408142 -676.289840814204 5 4634 5146.89689015415 -512.896890154154 6 3996 4495.01815719588 -499.018157195885 7 4308 4956.6898408142 -648.689840814204 8 4143 4732.56104834499 -589.561048344993 9 4429 4718.99677113227 -289.996771132271 10 5219 5377.13440909593 -158.134409095932 11 4929 4921.29868630865 7.701313691347 12 5755 5280.28936472668 474.710635273324 13 5592 5314.02213201714 277.977867982856 14 4163 4329.1617106038 -166.161710603801 15 4962 4510.23315617836 451.766843821644 16 5208 4660.94056258395 547.059437416048 17 4755 4993.72622586029 -238.726225860288 18 4491 4342.74056258395 148.259437416048 19 5732 4803.51917652034 928.480823479661 20 5731 4578.49731436920 1152.50268563080 21 5040 4565.29026502925 474.709734970753 22 6102 5224.32097267484 877.679027325162 23 4904 4768.84247776033 135.157522239666 24 5369 5128.72622586029 240.273774139712 25 5578 5165.31681613294 412.683183867059 26 4619 4181.34946440153 437.65053559847 27 4731 4364.38566327634 366.614336723664 28 5011 4513.66415819084 497.33584180916 29 5299 4848.05734689465 450.942653105345 30 4146 4198.32198117302 -52.3219811730241 31 4625 4659.10059510941 -34.1005951094108 32 4736 4436.57932806768 299.420671932320 33 4219 4425.51564596437 -206.515645964367 34 5116 5082.93882818248 33.0611718175198 35 4205 4626.74587752243 -421.74587752243 36 4121 4989.66606254095 -868.666062540955 37 5103 5023.22021589504 79.7797841049627 38 4300 4038.35979448169 261.640205518306 39 4578 4219.07401218348 358.925987816524 40 3809 4368.53112103437 -559.531121034367 41 5526 4702.38846792902 823.611532070978 42 4247 4052.29587433462 194.704125665382 43 3830 4512.53864646185 -682.538646461845 44 4394 4291.08906303843 102.910936961568 45 4826 4276.27448827100 549.725511728995 46 4409 4934.76935410744 -525.769354107436 47 4569 4479.29085919293 89.7091408070676 48 4106 4840.96074665675 -734.960746656751 49 4794 4873.97905820167 -79.9790582016743 50 3914 3886.79665561531 27.2033443846928 51 3793 4065.72473395322 -272.724733953224 52 4405 4213.57431737664 191.425682623363 53 4022 4544.93106916188 -522.931069161881 54 4100 3891.62342471252 208.376575287479 55 4788 4351.1517410942 436.848258905798 56 3163 4128.2732461797 -965.273246179697 57 3585 4112.92282960311 -527.92282960311 58 3903 4129.83643593931 -226.836435939314 59 3863 3673.82209921565 189.177900784350 60 5560 4671.35760021533 888.64239978467 61 3922 4705.26898144219 -783.268981442186 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00352393339620639 0.00704786679241279 0.996476066603794 18 0.279201108525888 0.558402217051775 0.720798891474112 19 0.32942524247894 0.65885048495788 0.67057475752106 20 0.272934400868930 0.545868801737859 0.72706559913107 21 0.208033987062736 0.416067974125472 0.791966012937264 22 0.176742739248849 0.353485478497698 0.823257260751151 23 0.124501551420967 0.249003102841934 0.875498448579033 24 0.0747654537545707 0.149530907509141 0.92523454624543 25 0.100263693078561 0.200527386157122 0.899736306921439 26 0.121344619319745 0.24268923863949 0.878655380680255 27 0.0943150869319566 0.188630173863913 0.905684913068043 28 0.0646269068686442 0.129253813737288 0.935373093131356 29 0.0496015187771972 0.0992030375543944 0.950398481222803 30 0.0303758244930927 0.0607516489861855 0.969624175506907 31 0.0203143350695838 0.0406286701391677 0.979685664930416 32 0.0136087644662809 0.0272175289325618 0.986391235533719 33 0.00716531117538735 0.0143306223507747 0.992834688824613 34 0.00420281629286002 0.00840563258572004 0.99579718370714 35 0.00266077896085997 0.00532155792171993 0.99733922103914 36 0.00496786663914813 0.00993573327829625 0.995032133360852 37 0.00239552373087841 0.00479104746175681 0.997604476269122 38 0.00160825314455389 0.00321650628910778 0.998391746855446 39 0.000965885060487031 0.00193177012097406 0.999034114939513 40 0.00202659672565713 0.00405319345131426 0.997973403274343 41 0.00668639066215063 0.0133727813243013 0.99331360933785 42 0.00307349105499155 0.00614698210998309 0.996926508945008 43 0.0101851642349802 0.0203703284699604 0.98981483576502 44 0.00819230429600075 0.0163846085920015 0.991807695704 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.321428571428571 NOK 5% type I error level 15 0.535714285714286 NOK 10% type I error level 17 0.607142857142857 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/104oup1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/104oup1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/18xnm1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/18xnm1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/2cbtq1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/2cbtq1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/3xqvb1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/3xqvb1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/4fblf1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/4fblf1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/5lgw41261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/5lgw41261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/6a3pb1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/6a3pb1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/7oisr1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/7oisr1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/85i3o1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/85i3o1261328540.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/9c65i1261328540.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/20/t126132861944yr56w7wi8ftve/9c65i1261328540.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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