*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: Fri, 20 Nov 2009 05:53:28 -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/20/t125872170594e0b17jp0rxva1.htm/, Retrieved Fri, 20 Nov 2009 13:55:17 +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/20/t125872170594e0b17jp0rxva1.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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No (this computation is public)

User-defined keywords:

Dataseries X:

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1.4 2 1.2 2 1 2 1.7 2 2.4 2 2 2 2.1 2 2 2 1.8 2 2.7 2 2.3 2 1.9 2 2 2 2.3 2 2.8 2 2.4 2 2.3 2 2.7 2 2.7 2 2.9 2 3 2 2.2 2 2.3 2 2.8 2.21 2.8 2.25 2.8 2.25 2.2 2.45 2.6 2.5 2.8 2.5 2.5 2.64 2.4 2.75 2.3 2.93 1.9 3 1.7 3.17 2 3.25 2.1 3.39 1.7 3.5 1.8 3.5 1.8 3.65 1.8 3.75 1.3 3.75 1.3 3.9 1.3 4 1.2 4 1.4 4 2.2 4 2.9 4 3.1 4 3.5 4 3.6 4 4.4 4 4.1 4 5.1 4 5.8 4 5.9 4.18 5.4 4.25 5.5 4.25 4.8 3.97 3.2 3.42 2.7 2.75

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] = + 2.07836940516536 -0.776305498871819X[t] + 0.482562816317725M1[t] + 0.468406500301148M2[t] + 0.548591569205598M3[t] + 0.577724418155176M4[t] + 0.7635681021386M5[t] + 0.814437505056589M6[t] + 0.820833017952014M7[t] + 0.665491976879028M8[t] + 0.562203937846657M9[t] + 0.47096890085490M10[t] + 0.143839867944373M11[t] + 0.0741563160165766t + 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) 2.07836940516536 0.702856 2.957 0.004889 0.002445 X -0.776305498871819 0.379015 -2.0482 0.046271 0.023135 M1 0.482562816317725 0.675322 0.7146 0.478488 0.239244 M2 0.468406500301148 0.670639 0.6984 0.488413 0.244207 M3 0.548591569205598 0.67155 0.8169 0.418195 0.209097 M4 0.577724418155176 0.669456 0.863 0.392628 0.196314 M5 0.7635681021386 0.665421 1.1475 0.257109 0.128555 M6 0.814437505056589 0.665572 1.2237 0.22731 0.113655 M7 0.820833017952014 0.66731 1.2301 0.224928 0.112464 M8 0.665491976879028 0.667132 0.9975 0.323721 0.16186 M9 0.562203937846657 0.664419 0.8462 0.401844 0.200922 M10 0.47096890085490 0.659911 0.7137 0.479027 0.239514 M11 0.143839867944373 0.654187 0.2199 0.826941 0.41347 t 0.0741563160165766 0.019419 3.8187 4e-04 2e-04 Multiple Linear Regression - Regression Statistics Multiple R 0.624576730303413 R-squared 0.390096092036502 Adjusted R-squared 0.217731944133774 F-TEST (value) 2.26320900711121 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.0211618067216435 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.03215027135484 Sum Squared Residuals 49.0053724022618 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4 1.08247753975602 0.317522460243977 2 1.2 1.14247753975602 0.057522460243976 3 1 1.29681892467705 -0.296818924677053 4 1.7 1.40010808964321 0.299891910356793 5 2.4 1.66010808964321 0.739891910356793 6 2 1.78513380857777 0.214866191422228 7 2.1 1.86568563748977 0.234314362510226 8 2 1.78450091243337 0.215499087566634 9 1.8 1.75536918941757 0.0446308105824288 10 2.7 1.73829046844239 0.961709531557609 11 2.3 1.48531775154844 0.81468224845156 12 1.9 1.41563419962064 0.484365800379356 13 2 1.97235333195495 0.0276466680450549 14 2.3 2.03235333195494 0.267646668045055 15 2.8 2.18669471687597 0.613305283124028 16 2.4 2.28998388184213 0.110016118157873 17 2.3 2.54998388184213 -0.249983881842126 18 2.7 2.67500960077669 0.0249903992233081 19 2.7 2.75556142968869 -0.0555614296886936 20 2.9 2.67437670463229 0.225623295367715 21 3 2.64524498161649 0.35475501838351 22 2.2 2.62816626064131 -0.42816626064131 23 2.3 2.37519354374736 -0.0751935437473595 24 2.8 2.14248583705648 0.657514162943519 25 2.8 2.66815274943591 0.13184725056409 26 2.8 2.72815274943591 0.0718472505640904 27 2.2 2.72723303458257 -0.527233034582573 28 2.6 2.79170692460514 -0.191706924605136 29 2.8 3.05170692460514 -0.251706924605136 30 2.5 3.06804987369765 -0.568049873697647 31 2.4 3.06320809773375 -0.66320809773375 32 2.3 2.84228838288041 -0.542288382880413 33 1.9 2.75881527494359 -0.858815274943591 34 1.7 2.6097646191602 -0.909764619160202 35 2 2.29468746235651 -0.294687462356506 36 2.1 2.11632114058665 -0.0163211405866546 37 1.7 2.58764666804506 -0.887646668045056 38 1.8 2.64764666804506 -0.847646668045056 39 1.8 2.68554222813531 -0.88554222813531 40 1.8 2.71120084321428 -0.911200843214282 41 1.3 2.97120084321428 -1.67120084321428 42 1.3 2.97978073731808 -1.67978073731808 43 1.3 2.98270201634290 -1.68270201634290 44 1.2 2.90151729128649 -1.70151729128649 45 1.4 2.87238556827069 -1.47238556827069 46 2.2 2.85530684729551 -0.655306847295512 47 2.9 2.60233413040156 0.297665869598439 48 3.1 2.53265057847376 0.567349421526235 49 3.5 3.08936971080807 0.410630289191934 50 3.6 3.14936971080807 0.450630289191934 51 4.4 3.30371109572909 1.09628890427091 52 4.1 3.40700026069525 0.692999739304753 53 5.1 3.66700026069525 1.43299973930475 54 5.8 3.79202597962981 2.00797402037019 55 5.9 3.73284281874489 2.16715718125511 56 5.4 3.59731670876745 1.80268329123255 57 5.5 3.56818498575166 1.93181501424834 58 4.8 3.76847180446059 1.03152819553941 59 3.2 3.94246711194613 -0.742467111946135 60 2.7 4.39290824426246 -1.69290824426246 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.126007541566508 0.252015083133016 0.873992458433492 18 0.0478674721117147 0.0957349442234295 0.952132527888285 19 0.0168242297793290 0.0336484595586581 0.98317577022067 20 0.00549332408271554 0.0109866481654311 0.994506675917284 21 0.0021288551483679 0.0042577102967358 0.997871144851632 22 0.00425287941465446 0.00850575882930892 0.995747120585346 23 0.00239300471201978 0.00478600942403955 0.99760699528798 24 0.00121621011116059 0.00243242022232119 0.99878378988884 25 0.000476856907977415 0.00095371381595483 0.999523143092023 26 0.000186682092304966 0.000373364184609931 0.999813317907695 27 0.000108885574722319 0.000217771149444639 0.999891114425278 28 4.14383915722353e-05 8.28767831444706e-05 0.999958561608428 29 1.72779775627681e-05 3.45559551255361e-05 0.999982722022437 30 6.55690455161648e-06 1.31138091032330e-05 0.999993443095448 31 2.50258945404973e-06 5.00517890809946e-06 0.999997497410546 32 1.36765347839472e-06 2.73530695678945e-06 0.999998632346522 33 9.789827975231e-07 1.9579655950462e-06 0.999999021017203 34 8.31081662289104e-07 1.66216332457821e-06 0.999999168918338 35 4.75537323903766e-06 9.51074647807532e-06 0.999995244626761 36 6.07590257616875e-05 0.000121518051523375 0.999939240974238 37 0.000133211746073737 0.000266423492147474 0.999866788253926 38 0.00169484010095296 0.00338968020190592 0.998305159899047 39 0.00479087739616425 0.0095817547923285 0.995209122603836 40 0.0527031416273246 0.105406283254649 0.947296858372675 41 0.0499568126140036 0.0999136252280071 0.950043187385996 42 0.0697986494780453 0.139597298956091 0.930201350521955 43 0.113031365784953 0.226062731569906 0.886968634215047 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.703703703703704 NOK 5% type I error level 21 0.777777777777778 NOK 10% type I error level 23 0.851851851851852 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/10dxmr1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/10dxmr1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/1xmdq1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/1xmdq1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/2pwhz1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/2pwhz1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/35twd1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/35twd1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/4fao31258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/4fao31258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/5v7re1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/5v7re1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/6q4pr1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/6q4pr1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/71cgl1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/71cgl1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/84sbs1258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/84sbs1258721604.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/9y3s01258721604.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872170594e0b17jp0rxva1/9y3s01258721604.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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