Paper interactie met trend

*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, 01 Dec 2010 14:45:13 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/01/t1291214664gxaix2cyhq3q5o6.htm/, Retrieved Wed, 01 Dec 2010 15:44:39 +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/2010/Dec/01/t1291214664gxaix2cyhq3q5o6.htm/}, year = {2010}, } @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 = {2010}, 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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66 4964 4818 4488 5 73 68 54 3132 3132 2916 12 58 54 82 2788 5576 3362 11 68 41 61 3038 3782 2989 6 62 49 65 3185 4225 3185 12 65 49 77 5832 6237 5544 11 81 72 66 5694 4818 5148 12 73 78 66 3712 4224 3828 7 64 58 66 3944 4488 3828 8 68 58 48 1173 2448 1104 13 51 23 57 2652 3876 2223 12 68 39 80 3843 4880 5040 13 61 63 60 3174 4140 2760 12 69 46 70 4234 5110 4060 12 73 58 85 2379 5185 3315 11 61 39 59 2728 3658 2596 12 62 44 72 3087 4536 3528 12 63 49 70 3933 4830 3990 12 69 57 74 3572 3478 5624 11 47 76 70 4158 4620 4410 13 66 63 51 1044 2958 918 9 58 18 70 2520 4410 2800 11 63 40 71 4071 4899 4189 11 69 59 72 3658 4248 4464 11 59 62 50 4130 2950 3500 9 59 70 69 4095 4347 4485 11 63 65 73 3640 4745 4088 12 65 56 66 2925 4290 2970 12 65 45 73 4047 5183 4161 10 71 57 58 3000 3480 2900 12 60 50 78 3240 6318 3120 12 81 40 83 3886 5561 4814 12 67 58 76 3234 5016 3724 9 66 49 77 3038 4774 3773 9 62 49 79 1701 4977 2133 12 63 27 71 3723 5183 3621 14 73 51 79 4125 4345 5925 12 55 7 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 13 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Vrienden_vinden[t] = + 14.6555338816014 -0.189702225408932Groepsgevoel[t] -0.00275923492011103`InteractieNV-U`[t] + 0.00133597925850489InteractieGR_NV[t] + 0.00259417562874793interacteiGR_U[t] + 0.0629106108368074NV[t] -0.0129269007861096Uitingsangst[t] + 0.00139848981226941t + 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) 14.6555338816014 8.601507 1.7038 0.090663 0.045331 Groepsgevoel -0.189702225408932 0.13622 -1.3926 0.165975 0.082987 `InteractieNV-U` -0.00275923492011103 0.001548 -1.7822 0.076921 0.03846 InteractieGR_NV 0.00133597925850489 0.001965 0.6797 0.497804 0.248902 interacteiGR_U 0.00259417562874793 0.001082 2.3978 0.017831 0.008915 NV 0.0629106108368074 0.142443 0.4417 0.659431 0.329716 Uitingsangst -0.0129269007861096 0.099773 -0.1296 0.897101 0.44855 t 0.00139848981226941 0.003446 0.4058 0.685489 0.342744 Multiple Linear Regression - Regression Statistics Multiple R 0.315743758497003 R-squared 0.0996941210298137 Adjusted R-squared 0.0540264315168332 F-TEST (value) 2.18303404645591 F-TEST (DF numerator) 7 F-TEST (DF denominator) 138 p-value 0.039317139078515 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.74734405362109 Sum Squared Residuals 421.343151358047 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 5 10.2325969779217 -5.23259697792173 2 12 10.472152876507 1.52784712349301 3 11 11.3303573241824 -0.330357324182428 4 6 10.7804406469640 -4.78044064696402 5 12 10.9064517691469 1.09354823085308 6 11 10.8446303521547 0.155369647845288 7 12 9.80963333222682 2.19036666777318 8 7 10.7542944423909 -3.75429444239091 9 8 10.7198913983299 -2.71989139832994 10 13 11.3727989523594 1.62720104764058 11 12 11.2592798480090 0.740720151991024 12 13 11.5097743905611 1.49022560943888 13 12 10.9708426653257 1.02915733467427 14 12 10.9152777177670 1.08472228223304 15 11 10.8477449891163 0.152255010883736 16 12 10.9114518545415 1.08854814545846 17 12 11.0451936595845 0.95480634041554 18 12 10.9570193590146 1.04298064098539 19 11 11.9986872198809 -0.9986872198809 20 13 10.8816933641752 2.11830663582481 21 9 11.8788585034405 -2.87885850344049 22 11 11.0555256219415 -0.055525621941502 23 11 10.9761048810772 0.0238951189228415 24 11 11.1031541573789 -0.103154157378949 25 9 9.63734113395408 -0.637341133954082 26 11 10.8688735289264 0.131126471073647 27 12 11.1109103547740 0.889089645225967 28 12 11.0471143834155 0.952885616584494 29 10 11.1297692222719 -1.12976922227186 30 12 10.7176634957873 1.28233650421273 31 12 12.0754207059897 -0.0754207059897278 32 12 11.6156067609111 0.384393239088869 33 9 11.2416133615161 -2.24161336151608 34 9 11.1462848521644 -2.14628485216442 35 12 10.8214341658084 1.17856583419162 36 14 11.2154830027580 2.78451699724203 37 12 12.0048446674931 -0.00484466749312473 38 11 10.4630060668046 0.536993933195374 39 9 11.1230603243114 -2.12306032431137 40 11 11.0342007778956 -0.0342007778956068 41 7 9.12620348486891 -2.12620348486891 42 15 11.1204350599471 3.87956494005288 43 11 10.7848108271458 0.21518917285419 44 12 11.6758060611817 0.324193938818344 45 12 10.6468592136864 1.3531407863136 46 9 11.7640574235604 -2.76405742356042 47 12 10.4368498592081 1.5631501407919 48 11 11.1815111847117 -0.181511184711707 49 11 10.3618691735230 0.638130826476978 50 8 11.241693610293 -3.241693610293 51 7 10.2177645082862 -3.21776450828619 52 12 11.6079987314593 0.392001268540721 53 8 11.7135891042698 -3.71358910426981 54 10 10.3308463521912 -0.330846352191219 55 12 10.2521488427898 1.74785115721019 56 15 10.9055052395586 4.09449476044142 57 12 11.6416171192176 0.358382880782354 58 12 11.1263621305653 0.873637869434698 59 12 10.7427392862920 1.25726071370795 60 12 10.4118310226786 1.58816897732136 61 8 9.23556544128129 -1.23556544128129 62 10 11.2218180057833 -1.22181800578332 63 14 12.2748562304631 1.72514376953694 64 10 10.9540368263685 -0.95403682636847 65 12 10.8342750716084 1.16572492839162 66 14 10.8734446689193 3.12655533108067 67 6 11.1794049388958 -5.17940493889579 68 11 10.7066729987772 0.293327001222756 69 10 11.0813919884717 -1.08139198847166 70 14 12.5912497185057 1.40875028149431 71 12 11.0583645358355 0.941635464164523 72 13 11.1040548613757 1.89594513862431 73 11 10.7798009669830 0.220199033017044 74 11 10.9609495396087 0.0390504603912553 75 12 11.2401027576418 0.759897242358156 76 13 10.8148174410443 2.18518255895565 77 12 10.3034974814023 1.69650251859767 78 8 10.0059609097244 -2.00596090972436 79 12 11.2993251208935 0.700674879106457 80 11 11.2924957867929 -0.292495786792911 81 10 11.0306089400744 -1.03060894007436 82 12 11.0376297965032 0.96237020349675 83 11 11.0261932719207 -0.0261932719206823 84 12 11.0926937343266 0.907306265673434 85 12 11.0603918868395 0.93960811316051 86 10 10.5013877991308 -0.501387799130835 87 12 11.4877972940235 0.512202705976547 88 12 10.9072611388377 1.09273886116228 89 11 11.2981905127119 -0.298190512711885 90 10 10.9570044651397 -0.957004465139702 91 12 11.3923704932709 0.607629506729072 92 11 11.0669985362206 -0.0669985362206418 93 12 10.4558067061026 1.54419329389738 94 12 9.6585289277836 2.34147107221641 95 10 9.62875712873363 0.371242871266372 96 11 11.1142176725471 -0.114217672547086 97 10 11.0207218184545 -1.02072181845446 98 11 11.0081827586954 -0.00818275869543628 99 11 10.5679580826095 0.432041917390542 100 12 11.3086847778349 0.691315222165091 101 11 10.9522010897758 0.0477989102242155 102 11 10.7495803429619 0.250419657038056 103 7 8.82722317841054 -1.82722317841054 104 12 10.6705796169182 1.32942038308181 105 8 10.6133148015045 -2.61331480150449 106 10 11.1028672858380 -1.10286728583804 107 12 11.3458286857496 0.654171314250391 108 11 11.129623558367 -0.129623558367004 109 13 11.2085143967262 1.79148560327376 110 9 11.5214041857192 -2.52140418571916 111 11 11.3422873517222 -0.342287351722197 112 13 11.2402417577831 1.75975824221687 113 8 10.8477899077600 -2.84778990775997 114 12 11.1854637830689 0.814536216931106 115 11 10.6035775595414 0.396422440458647 116 11 10.7206848770669 0.279315122933137 117 12 10.9179641651151 1.08203583488489 118 13 11.4015285878971 1.59847141210286 119 11 11.3532535599571 -0.353253559957125 120 10 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144 9 11.0963491009105 -2.09634910091047 145 12 11.3248368876320 0.67516311236798 146 12 11.3648782142437 0.635121785756275 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.992484878081117 0.0150302438377656 0.00751512191888278 12 0.999658099435383 0.000683801129234448 0.000341900564617224 13 0.999111889539465 0.00177622092106925 0.000888110460534626 14 0.998288690646805 0.00342261870638902 0.00171130935319451 15 0.996618536335 0.00676292733000075 0.00338146366500038 16 0.994119486893567 0.0117610262128658 0.00588051310643291 17 0.990127385687942 0.0197452286241166 0.00987261431205828 18 0.984870514147392 0.0302589717052153 0.0151294858526076 19 0.979546726728177 0.0409065465436452 0.0204532732718226 20 0.969658038197161 0.0606839236056779 0.0303419618028390 21 0.99043768796641 0.0191246240671799 0.00956231203358996 22 0.988285042038482 0.0234299159230369 0.0117149579615185 23 0.986337355594653 0.0273252888106946 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0.990392907820605 0.0192141843587902 0.0096070921793951 59 0.98797753201379 0.0240449359724183 0.0120224679862091 60 0.985803484405416 0.0283930311891679 0.0141965155945839 61 0.984829618188072 0.0303407636238560 0.0151703818119280 62 0.983649574760677 0.0327008504786467 0.0163504252393233 63 0.980950714658332 0.0380985706833364 0.0190492853416682 64 0.977902007749269 0.0441959845014628 0.0220979922507314 65 0.97197118399471 0.0560576320105794 0.0280288160052897 66 0.981423540565539 0.0371529188689219 0.0185764594344609 67 0.999425175777716 0.00114964844456856 0.000574824222284281 68 0.999153803114398 0.00169239377120365 0.000846196885601823 69 0.999097069074945 0.00180586185011023 0.000902930925055114 70 0.998711436885612 0.00257712622877637 0.00128856311438818 71 0.998131486754467 0.00373702649106632 0.00186851324553316 72 0.997900264565974 0.00419947086805108 0.00209973543402554 73 0.996930554067258 0.00613889186548397 0.00306944593274199 74 0.995656410629266 0.00868717874146825 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0.0988341408570816 0.197668281714163 0.901165859142918 127 0.114451623352886 0.228903246705771 0.885548376647114 128 0.107982269057411 0.215964538114821 0.892017730942589 129 0.111140065255560 0.222280130511120 0.88885993474444 130 0.0741752707445548 0.148350541489110 0.925824729255445 131 0.060561570848311 0.121123141696622 0.939438429151689 132 0.0466458063602346 0.0932916127204691 0.953354193639765 133 0.0601151945943809 0.120230389188762 0.939884805405619 134 0.181166821977933 0.362333643955867 0.818833178022067 135 0.100958404795116 0.201916809590231 0.899041595204884 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.104 NOK 5% type I error level 44 0.352 NOK 10% type I error level 55 0.44 NOK

Charts produced by software:

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Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal 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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Software written by Ed van Stee & Patrick Wessa

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