WS7 (t-1)

*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: Wed, 18 Nov 2009 09:31:41 -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/18/t1258561955hq9llce776tdpu6.htm/, Retrieved Wed, 18 Nov 2009 17:32:47 +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/18/t1258561955hq9llce776tdpu6.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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Dataseries X:

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7,59 43,14 7,59 7,57 43,39 7,59 7,57 43,46 7,57 7,59 43,54 7,57 7,6 43,62 7,59 7,64 44,01 7,6 7,64 44,5 7,64 7,76 44,73 7,64 7,76 44,89 7,76 7,76 45,09 7,76 7,77 45,17 7,76 7,83 45,24 7,77 7,94 45,42 7,83 7,94 45,67 7,94 7,94 45,68 7,94 8,09 46,56 7,94 8,18 46,72 8,09 8,26 47,01 8,18 8,28 47,26 8,26 8,28 47,49 8,28 8,28 47,51 8,28 8,29 47,52 8,28 8,3 47,66 8,29 8,3 47,71 8,3 8,31 47,87 8,3 8,33 48 8,31 8,33 48 8,33 8,34 48,05 8,33 8,48 48,25 8,34 8,59 48,72 8,48 8,67 48,94 8,59 8,67 49,16 8,67 8,67 49,18 8,67 8,71 49,25 8,67 8,72 49,34 8,71 8,72 49,49 8,72 8,72 49,57 8,72 8,74 49,63 8,72 8,74 49,67 8,74 8,74 49,7 8,74 8,74 49,8 8,74 8,79 50,09 8,74 8,85 50,49 8,79 8,86 50,73 8,85 8,87 51,12 8,86 8,92 51,15 8,87 8,96 51,41 8,92 8,97 51,61 8,96 8,99 52,06 8,97 8,98 52,17 8,99 8,98 52,18 8,98 9,01 52,19 8,98 9,01 52,74 9,01 9,03 53,05 9,01 9,05 53,38 9,03 9,05 53,78 9,05

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] = -0.561884252914864 + 0.0216620241930846X[t] + 0.954028194605642Y1[t] + 0.00892343909083954M1[t] -0.01646835463777M2[t] -0.0161527040647591M3[t] + 0.0240851904459348M4[t] + 0.0300806045896479M5[t] + 0.039492462372239M6[t] + 0.0137159261098806M7[t] + 0.00243905630834483M8[t] -0.0181185568513062M9[t] + 0.005604485378458M10[t] -0.00104613834295537M11[t] -0.00278691959124200t + 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) -0.561884252914864 0.832539 -0.6749 0.503525 0.251762 X 0.0216620241930846 0.01811 1.1961 0.238518 0.119259 Y1 0.954028194605642 0.064319 14.8327 0 0 M1 0.00892343909083954 0.026233 0.3402 0.735472 0.367736 M2 -0.01646835463777 0.026221 -0.6281 0.53345 0.266725 M3 -0.0161527040647591 0.026455 -0.6106 0.544856 0.272428 M4 0.0240851904459348 0.026581 0.9061 0.370173 0.185086 M5 0.0300806045896479 0.026429 1.1382 0.261656 0.130828 M6 0.039492462372239 0.026322 1.5003 0.141189 0.070594 M7 0.0137159261098806 0.026509 0.5174 0.607657 0.303829 M8 0.00243905630834483 0.026793 0.091 0.927908 0.463954 M9 -0.0181185568513062 0.027897 -0.6495 0.519652 0.259826 M10 0.005604485378458 0.027659 0.2026 0.840428 0.420214 M11 -0.00104613834295537 0.02762 -0.0379 0.969971 0.484985 t -0.00278691959124200 0.003377 -0.8254 0.413944 0.206972 Multiple Linear Regression - Regression Statistics Multiple R 0.997625025332438 R-squared 0.995255691169548 Adjusted R-squared 0.993635683276223 F-TEST (value) 614.352371534885 F-TEST (DF numerator) 14 F-TEST (DF denominator) 41 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0390228580478303 Sum Squared Residuals 0.0624341214590656 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.59 7.61982598733123 -0.0298259873312309 2 7.57 7.59706278005965 -0.0270627800596501 3 7.57 7.57702728884282 -0.00702728884282097 4 7.59 7.61621122569772 -0.0262112256977200 5 7.6 7.64023324607775 -0.0402332460777503 6 7.64 7.66484665565046 -0.0248466556504587 7 7.64 7.6850587194357 -0.0450587194356954 8 7.76 7.67597719560733 0.084022804392673 9 7.76 7.77058197008 -0.0105819700800046 10 7.76 7.79585049755714 -0.0358504975571439 11 7.77 7.78814591617993 -0.0181459161799354 12 7.83 7.79746175857122 0.0325382414287795 13 7.94 7.86473913410191 0.0752608658980881 14 7.94 7.94691902823695 -0.00691902823695242 15 7.94 7.94466437946065 -0.00466437946065205 16 8.09 8.00117793567002 0.088822064329981 17 8.18 8.15095658328423 0.0290434167157706 18 8.26 8.24972604600608 0.0102739539939193 19 8.28 8.3029003517692 -0.0229003517692032 20 8.28 8.31289939183295 -0.0328993918329474 21 8.28 8.28998809956592 -0.00998809956591605 22 8.29 8.31114084244637 -0.0211408424463694 23 8.3 8.3142762644668 -0.0142762644668003 24 8.3 8.32315886637423 -0.0231588663742259 25 8.31 8.33276130974472 -0.0227613097447171 26 8.33 8.31693894151602 0.0130610584839767 27 8.33 8.3335482363899 -0.00354823638990462 28 8.34 8.37208231251901 -0.0320823125190109 29 8.48 8.38916349385616 0.0908365061438454 30 8.59 8.53953353066304 0.0504664693369556 31 8.67 8.62067882153854 0.0493211784614574 32 8.67 8.6877029330367 -0.0177029330366948 33 8.67 8.66479164076966 0.00520835923033638 34 8.71 8.6872441051017 0.0227558948982992 35 8.72 8.71791727175065 0.00208272824935013 36 8.72 8.72896607607738 -0.00896607607738213 37 8.72 8.73683555751243 -0.0168355575124263 38 8.74 8.70995656564416 0.0300434343558396 39 8.74 8.72743234148576 0.0125676585142350 40 8.74 8.765533177131 -0.0255331771310095 41 8.74 8.77090787410279 -0.0309078741027889 42 8.79 8.78381479931013 0.00618520068986623 43 8.85 8.81161756286405 0.0383824371359523 44 8.86 8.85999435095395 5.64904605067666e-06 45 8.87 8.85463828958441 0.0153617104155843 46 8.92 8.88576455489479 0.034235445105214 47 8.96 8.92966054760262 0.0303394523973856 48 8.97 8.97041329897717 -0.000413298977171437 49 8.99 8.99583801130971 -0.00583801130971378 50 8.98 8.98912268454322 -0.00912268454321379 51 8.98 8.97732775382086 0.00267224617914272 52 9.01 9.01499534898224 -0.00499534898224064 53 9.01 9.05873880267908 -0.0487388026790768 54 9.03 9.07207896837028 -0.0420789683702824 55 9.05 9.06974454439251 -0.0197445443925112 56 9.05 9.08342612856908 -0.0334261285690814 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0929443758367375 0.185888751673475 0.907055624163262 19 0.136215763971520 0.272431527943041 0.86378423602848 20 0.550926283643784 0.898147432712431 0.449073716356216 21 0.415711210550199 0.831422421100398 0.584288789449801 22 0.441144309928186 0.882288619856372 0.558855690071814 23 0.375231144649479 0.750462289298959 0.62476885535052 24 0.341100139082017 0.682200278164034 0.658899860917983 25 0.57512138820161 0.84975722359678 0.42487861179839 26 0.49581039557606 0.99162079115212 0.50418960442394 27 0.507937804768412 0.984124390463175 0.492062195231588 28 0.897566847501053 0.204866304997894 0.102433152498947 29 0.964568832763564 0.0708623344728717 0.0354311672364359 30 0.986886969401222 0.0262260611975562 0.0131130305987781 31 0.9992224870669 0.00155502586620131 0.000777512933100656 32 0.998228386597077 0.00354322680584651 0.00177161340292326 33 0.995965829671242 0.00806834065751614 0.00403417032875807 34 0.994400418309138 0.0111991633817234 0.00559958169086169 35 0.984462935638214 0.0310741287235725 0.0155370643617862 36 0.9640434052416 0.0719131895167986 0.0359565947583993 37 0.928591154038824 0.142817691922352 0.071408845961176 38 0.896376776526153 0.207246446947694 0.103623223473847 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.142857142857143 NOK 5% type I error level 6 0.285714285714286 NOK 10% type I error level 8 0.380952380952381 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/101wwz1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/101wwz1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/1rppp1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/1rppp1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/2pcbw1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/2pcbw1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/31rxn1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/31rxn1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/4jlxl1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/4jlxl1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/52n4w1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/52n4w1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/6z41q1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/6z41q1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/7hexs1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/7hexs1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/8xkiq1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/8xkiq1258561897.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/9qa6h1258561897.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561955hq9llce776tdpu6/9qa6h1258561897.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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