WS 7 Multiple Regression analysis

*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 02:48:52 -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/t1258797014adh4355gjyb7fhc.htm/, Retrieved Sat, 21 Nov 2009 10:50:26 +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/t1258797014adh4355gjyb7fhc.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:

WS 7 Multiple Regression analysis

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

13,7 15 14,4 15,3 14,3 14,5 14,2 15,5 13,7 14,4 15,3 14,3 13,5 15,1 14,2 13,7 14,4 15,3 11,9 11,7 13,5 14,2 13,7 14,4 14,6 16,3 11,9 13,5 14,2 13,7 15,6 16,7 14,6 11,9 13,5 14,2 14,1 15 15,6 14,6 11,9 13,5 14,9 14,9 14,1 15,6 14,6 11,9 14,2 14,6 14,9 14,1 15,6 14,6 14,6 15,3 14,2 14,9 14,1 15,6 17,2 17,9 14,6 14,2 14,9 14,1 15,4 16,4 17,2 14,6 14,2 14,9 14,3 15,4 15,4 17,2 14,6 14,2 17,5 17,9 14,3 15,4 17,2 14,6 14,5 15,9 17,5 14,3 15,4 17,2 14,4 13,9 14,5 17,5 14,3 15,4 16,6 17,8 14,4 14,5 17,5 14,3 16,7 17,9 16,6 14,4 14,5 17,5 16,6 17,4 16,7 16,6 14,4 14,5 16,9 16,7 16,6 16,7 16,6 14,4 15,7 16 16,9 16,6 16,7 16,6 16,4 16,6 15,7 16,9 16,6 16,7 18,4 19,1 16,4 15,7 16,9 16,6 16,9 17,8 18,4 16,4 15,7 16,9 16,5 17,2 16,9 18,4 16,4 15,7 18,3 18,6 16,5 16,9 18,4 16,4 15,1 16,3 18,3 16,5 16,9 18,4 15,7 15,1 15,1 18,3 16,5 16,9 18,1 19,2 15,7 15,1 18,3 16,5 16,8 17,7 18,1 15,7 15,1 18,3 18,9 19,1 16,8 18,1 15,7 15,1 19 18 18,9 16,8 18,1 15,7 18,1 17,5 19 18,9 16,8 18,1 17,8 17,8 18, 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -2.89999021216690 + 0.873627997570757X[t] + 0.0245492114319014Y1[t] + 0.145315150831421Y2[t] + 0.110701724083021Y3[t] -0.0225391490188104Y4[t] -0.361337024228986M1[t] -0.0556593556308913M2[t] -0.472649033262836M3[t] + 0.625633258667187M4[t] -0.223770882850354M5[t] + 0.365365125566834M6[t] + 0.360498507612941M7[t] + 0.676362526335275M8[t] + 0.420586202713588M9[t] + 0.0485506948041424M10[t] + 0.465680688403872M11[t] + 0.00673586861246828t + 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) -2.89999021216690 0.61411 -4.7223 1.6e-05 8e-06 X 0.873627997570757 0.048506 18.0107 0 0 Y1 0.0245492114319014 0.05289 0.4642 0.644338 0.322169 Y2 0.145315150831421 0.048801 2.9777 0.004286 0.002143 Y3 0.110701724083021 0.050477 2.1931 0.032469 0.016235 Y4 -0.0225391490188104 0.057631 -0.3911 0.697212 0.348606 M1 -0.361337024228986 0.260616 -1.3865 0.171097 0.085549 M2 -0.0556593556308913 0.27146 -0.205 0.838286 0.419143 M3 -0.472649033262836 0.256836 -1.8403 0.071029 0.035514 M4 0.625633258667187 0.289713 2.1595 0.03511 0.017555 M5 -0.223770882850354 0.316335 -0.7074 0.482261 0.24113 M6 0.365365125566834 0.29628 1.2332 0.222661 0.111331 M7 0.360498507612941 0.243658 1.4795 0.144603 0.072302 M8 0.676362526335275 0.284027 2.3813 0.020674 0.010337 M9 0.420586202713588 0.268535 1.5662 0.12293 0.061465 M10 0.0485506948041424 0.266358 0.1823 0.856025 0.428012 M11 0.465680688403872 0.292402 1.5926 0.116878 0.058439 t 0.00673586861246828 0.004765 1.4137 0.16299 0.081495 Multiple Linear Regression - Regression Statistics Multiple R 0.992141366913702 R-squared 0.98434449194139 Adjusted R-squared 0.979591926995026 F-TEST (value) 207.118577662895 F-TEST (DF numerator) 17 F-TEST (DF denominator) 56 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.385429189322686 Sum Squared Residuals 8.31911695898883 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13.7 13.6828760417325 0.0171239582674963 2 14.2 14.3993450478646 -0.199345047864642 3 13.5 13.4280233392573 0.0719766607427173 4 11.9 11.5609734627314 0.339026537268607 5 14.6 14.6671229011334 -0.0671229011334472 6 15.6 15.3574638253597 0.242536174640255 7 14.1 14.1297202446051 -0.0297202446051058 8 14.9 14.8084059593207 0.0915940406793455 9 14.2 14.1487897696708 0.0512102303291676 10 14.6 14.3055056661928 0.294494333807151 11 17.2 17.0312735038744 0.168726496125587 12 15.4 15.2883181717092 0.111681828290779 13 14.3 14.4537779240526 -0.153777924052595 14 17.5 16.9404988741267 0.559501125873268 15 14.5 14.4438349898349 0.0561650101650815 16 14.4 14.1117565753433 0.288243424656726 17 16.6 16.6168757003131 -0.0168757003130596 18 16.7 16.9353566780576 -0.235356678057576 19 16.6 16.8791074575512 -0.279107457551217 20 16.9 16.8480420484110 0.0519579515890279 21 15.7 15.9417792880156 -0.241779288015571 22 16.4 16.101467851482 0.298532148517988 23 18.4 18.5876744067525 -0.187674406752515 24 16.9 17.0042284049597 -0.104228404959656 25 16.5 16.4837951209964 0.0162048790036415 26 18.3 17.9971214878390 0.302878512161034 27 15.1 15.3524549204894 -0.252454920489449 28 15.7 15.5816573127565 0.118342687243487 29 18.1 18.0788636370471 0.0211363629529026 30 16.8 17.1155847303565 -0.315584730356508 31 18.9 18.7959218760581 0.104078123941907 32 19 18.2723352623792 0.72766473762083 33 18.1 17.6960913475207 0.403908652479327 34 17.8 17.8470918465882 -0.0470918465881536 35 21.5 20.9795196610748 0.520480338925197 36 17.1 17.0587777212295 0.0412222787705488 37 18.7 19.0428824049365 -0.342882404936517 38 19 19.520997339621 -0.520997339620994 39 16.4 16.8581485033708 -0.458148503370805 40 16.9 16.9961490770563 -0.0961490770563166 41 18.6 18.6680562884838 -0.068056288483806 42 19.3 19.4331843720702 -0.133184372070209 43 19.4 19.9005892763920 -0.50058927639203 44 17.6 18.1454924534427 -0.5454924534427 45 18.6 19.3037743825785 -0.703774382578548 46 18.1 18.4346610520408 -0.334661052040824 47 20.4 21.2362088343153 -0.836208834315295 48 18.1 18.4661834245203 -0.366183424520313 49 19.6 19.3598075155473 0.240192484452722 50 19.9 20.5143315604645 -0.614331560464538 51 19.2 18.9746076359026 0.225392364097358 52 17.8 18.6640353271756 -0.864035327175582 53 19.2 19.5192981412791 -0.319298141279137 54 22 21.9585519457555 0.0414480542444776 55 21.1 20.7829531938282 0.317046806171787 56 19.5 19.2307200423467 0.269279957653273 57 22.2 21.7109112248166 0.489088775183414 58 20.9 21.2787374453993 -0.378737445399308 59 22.2 22.0809283150931 0.119071684906883 60 23.5 23.1977498636498 0.302250136350196 61 21.5 21.3740368394466 0.125963160553397 62 24.3 24.2709075785906 0.0290924214094412 63 22.8 22.4429306111449 0.357069388855098 64 20.3 20.0854282449369 0.214571755063078 65 23.7 23.2497833317435 0.450216668256547 66 23.3 22.8998584484004 0.400141551599560 67 19.6 19.2117079515653 0.388292048434659 68 18 18.5950042340998 -0.595004234099776 69 17.3 17.2986539873978 0.00134601260221030 70 16.8 16.6325361382969 0.167463861703146 71 18.2 17.9843952788899 0.215604721110143 72 16.5 16.4847424139316 0.0152575860684450 73 16 15.9028241532881 0.0971758467118542 74 18.4 17.9567981114936 0.44320188850643 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0641971159049376 0.128394231809875 0.935802884095062 22 0.0408515364863914 0.0817030729727828 0.959148463513609 23 0.0157285650222386 0.0314571300444772 0.984271434977761 24 0.0050195195193498 0.0100390390386996 0.99498048048065 25 0.00144717278473316 0.00289434556946631 0.998552827215267 26 0.00057606034223897 0.00115212068447794 0.99942393965776 27 0.000169414978809341 0.000338829957618682 0.99983058502119 28 0.000102782998268891 0.000205565996537782 0.99989721700173 29 3.32736923598257e-05 6.65473847196515e-05 0.99996672630764 30 1.10792150882343e-05 2.21584301764686e-05 0.999988920784912 31 3.28694967846295e-06 6.5738993569259e-06 0.999996713050322 32 2.22459011017784e-05 4.44918022035568e-05 0.999977754098898 33 0.000152991173549370 0.000305982347098741 0.99984700882645 34 7.3674708443508e-05 0.000147349416887016 0.999926325291556 35 0.0109442671875613 0.0218885343751226 0.989055732812439 36 0.0298143017275702 0.0596286034551403 0.97018569827243 37 0.0206038128162947 0.0412076256325893 0.979396187183705 38 0.0812936507326105 0.162587301465221 0.91870634926739 39 0.057572303440749 0.115144606881498 0.942427696559251 40 0.108116788395217 0.216233576790434 0.891883211604783 41 0.113263366731381 0.226526733462763 0.886736633268619 42 0.0762466262504644 0.152493252500929 0.923753373749536 43 0.081599091840301 0.163198183680602 0.9184009081597 44 0.065064337727802 0.130128675455604 0.934935662272198 45 0.114511083756197 0.229022167512393 0.885488916243803 46 0.110343809324946 0.220687618649892 0.889656190675054 47 0.251557976636825 0.50311595327365 0.748442023363175 48 0.225375855934951 0.450751711869902 0.77462414406505 49 0.455539255214478 0.911078510428957 0.544460744785522 50 0.53900790802638 0.921984183947239 0.460992091973620 51 0.457503723804777 0.915007447609554 0.542496276195223 52 0.41383234729944 0.82766469459888 0.58616765270056 53 0.267772766980162 0.535545533960324 0.732227233019838 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.303030303030303 NOK 5% type I error level 14 0.424242424242424 NOK 10% type I error level 16 0.484848484848485 NOK

Charts produced by software:

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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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