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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 05:04:16 -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/t12587187312ms9diw7g59561h.htm/, Retrieved Fri, 20 Nov 2009 13:05:43 +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/t12587187312ms9diw7g59561h.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}, }

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User-defined keywords:

Ws7 link 5

Dataseries X:

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1.4 8.2 1,2 1,4 1.2 8.0 1 1,2 1.0 7.5 1,7 1 1.7 6.8 7.5 1,7 2.4 6.5 6.8 7.5 2.0 6.6 6.5 6.8 2.1 7.6 6.6 6.5 2.0 8.0 7.6 6.6 1.8 8.1 8.0 7.6 2.7 7.7 8.1 8.0 2.3 7.5 7.7 8.1 1.9 7.6 7.5 7.7 2.0 7.8 7.6 7.5 2.3 7.8 7.8 7.6 2.8 7.8 7.8 7.8 2.4 7.5 7.8 7.8 2.3 7.5 7.5 7.8 2.7 7.1 7.5 7.5 2.7 7.5 7.1 7.5 2.9 7.5 7.5 7.1 3.0 7.6 7.5 7.5 2.2 7.7 7.6 7.5 2.3 7.7 7.7 7.6 2.8 7.9 7.7 7.7 2.8 8.1 7.9 7.7 2.8 8.2 8.1 7.9 2.2 8.2 8.2 8.1 2.6 8.2 8.2 8.2 2.8 7.9 8.2 8.2 2.5 7.3 7.9 8.2 2.4 6.9 7.3 7.9 2.3 6.6 6.9 7.3 1.9 6.7 6.6 6.9 1.7 6.9 6.7 6.6 2.0 7.0 6.9 6.7 2.1 7.1 7.0 6.9 1.7 7.2 7.1 7.0 1.8 7.1 7.2 7.1 1.8 6.9 7.1 7.2 1.8 7.0 6.9 7.1 1.3 6.8 7.0 6.9 1.3 6.4 6.8 7.0 1.3 6.7 6.4 6.8 1.2 6.6 6.7 6.4 1.4 6.4 6.6 6.7 2.2 6.3 6.4 6.6 2.9 6.2 6.3 6.4 3.1 6.5 6.2 6.3 3.5 6.8 6.5 6.2 3.6 6.8 6.8 6.5 4.4 6.4 6.8 6.8 4.1 6.1 6.4 6.8 5.1 5.8 6.1 6.4 5.8 6.1 5.8 6.1 5.9 7.2 6.1 5.8 5.4 7.3 7.2 6.1 5.5 6.9 7.3 7.2 4.8 6.1 6.9 7.3 3.2 5.8 6.1 6.9 2.7 6.2 5.8 6.1 2.1 7.1 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 3.30821434872672 -0.272802800787046X[t] + 0.0702404974155654Y1[t] -0.00157170726815757Y2[t] + 0.00350368217653026M1[t] + 0.00247072088343351M2[t] -0.205170993272856M3[t] -0.266929670970975M4[t] + 0.327279121223635M5[t] + 0.349232147671393M6[t] + 0.495317702851394M7[t] + 0.328181865572133M8[t] + 0.252601193983415M9[t] + 0.183724384734404M10[t] -0.0281642113791766M11[t] + 0.0185621130818993t + 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) 3.30821434872672 2.519453 1.3131 0.195403 0.097701 X -0.272802800787046 0.340819 -0.8004 0.427404 0.213702 Y1 0.0702404974155654 0.232244 0.3024 0.763622 0.381811 Y2 -0.00157170726815757 0.21132 -0.0074 0.994097 0.497048 M1 0.00350368217653026 0.758324 0.0046 0.996333 0.498166 M2 0.00247072088343351 0.761527 0.0032 0.997425 0.498712 M3 -0.205170993272856 0.747499 -0.2745 0.784896 0.392448 M4 -0.266929670970975 0.762942 -0.3499 0.727968 0.363984 M5 0.327279121223635 0.777999 0.4207 0.675875 0.337938 M6 0.349232147671393 0.785483 0.4446 0.658601 0.329301 M7 0.495317702851394 0.76622 0.6464 0.521072 0.260536 M8 0.328181865572133 0.774618 0.4237 0.673699 0.33685 M9 0.252601193983415 0.766335 0.3296 0.743119 0.37156 M10 0.183724384734404 0.768576 0.239 0.812088 0.406044 M11 -0.0281642113791766 0.770074 -0.0366 0.970977 0.485488 t 0.0185621130818993 0.010941 1.6965 0.096268 0.048134 Multiple Linear Regression - Regression Statistics Multiple R 0.454844093786271 R-squared 0.206883149652254 Adjusted R-squared -0.0409658660814167 F-TEST (value) 0.834714429023849 F-TEST (DF numerator) 15 F-TEST (DF denominator) 48 p-value 0.635944893191229 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.20872056478554 Sum Squared Residuals 70.1282593793028 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.4 1.17538538425463 0.224614615745371 2 1.2 1.23374133817136 -0.0337413381713568 3 1 1.23054582713502 -0.230545827135018 4 1.7 1.7846059129923 -0.0846059129923012 5 2.4 2.42093340815871 -0.0209334081587133 6 2 2.41419631347271 -0.414196313472706 7 2.1 2.31353674286956 -0.213536742869565 8 2 2.12592522504613 -0.125925225046134 9 1.8 2.06815087815868 -0.268150878158679 10 2.7 2.13335266914068 0.566647330859321 11 2.3 1.96633337657337 0.333666623426635 12 1.9 1.97236000437989 -0.0723600043798864 13 2 1.94720363067609 0.0527963693239052 14 2.3 1.97862371122119 0.321376288778806 15 2.8 1.78922976869317 1.01077023130683 16 2.4 1.82787404431307 0.572125955686933 17 2.3 2.41957280036491 -0.119572800364906 18 2.7 2.56968057238983 0.130319427610171 19 2.7 2.59711092137069 0.102889078629315 20 2.9 2.47726207904681 0.422737920953188 21 3 2.39233455755403 0.607665442445973 22 2.2 2.32176363104977 -0.121763631049766 23 2.3 2.13530402703283 0.164695972967174 24 2.8 2.12731262060968 0.672687379390324 25 2.8 2.10886595519381 0.69113404480619 26 2.8 2.11284858493339 0.68715141506661 27 2.2 1.93047869214692 0.269521307853076 28 2.6 1.88712495680389 0.712875043196111 29 2.8 2.58173670231651 0.218263297683488 30 2.5 2.76486137309373 -0.264861373093727 31 2.4 2.99695737540155 -0.596957375401555 32 2.3 2.90307131683497 -0.603071316834975 33 1.9 2.79832901193205 -0.898329011932045 34 1.7 2.70094931752953 -1.00094931752953 35 2 2.49423348317544 -0.494233483175439 36 2.1 2.52038923584574 -0.420389235845735 37 1.7 2.5220416300402 -0.822041630040201 38 1.8 2.57371794092245 -0.773717940922449 39 1.8 2.43201767953710 -0.632017679537096 40 1.8 2.34764990608587 -0.547649906085874 41 1.3 3.02231976271498 -1.72231976271498 42 1.3 3.15775075234953 -1.85775075234953 43 1.3 3.21277572286272 -1.91277572286272 44 1.2 3.11318311087600 -1.91318311087600 45 1.4 3.10322955060458 -1.70322955060458 46 2.2 3.06630420575988 -0.866304205759877 47 2.9 2.89354829451898 0.00645170548102447 48 3.1 2.85156689972920 0.248433100270803 49 3.5 2.813021174703 0.686978825297002 50 3.6 2.85115096353602 0.748849036463978 51 4.4 2.77072097059600 1.62927902940400 52 4.1 2.78126904724967 1.31873095275033 53 5.1 3.45543732644489 1.64456267355511 54 5.8 3.39351098869421 2.40648901130579 55 5.9 3.27961923749548 2.62038076250452 56 5.4 3.18055826819608 2.21944173180392 57 5.5 3.23795600175067 2.26204399824933 58 4.8 3.37763017652015 1.42236982347985 59 3.2 3.21058081869939 -0.0105808186993931 60 2.7 3.12837123943551 -0.428371239435507 61 2.1 2.93348222513227 -0.833482225132268 62 1.9 2.84991746121559 -0.949917461215588 63 0.6 2.64700706189179 -2.04700706189179 64 0.7 2.67147613255520 -1.97147613255520 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0266577072731589 0.0533154145463178 0.97334229272684 20 0.00746071889374525 0.0149214377874905 0.992539281106255 21 0.00229984861666121 0.00459969723332242 0.997700151383339 22 0.00208734051999588 0.00417468103999177 0.997912659480004 23 0.000553162622799151 0.00110632524559830 0.9994468373772 24 0.000224377589932822 0.000448755179865644 0.999775622410067 25 5.8722781171827e-05 0.000117445562343654 0.999941277218828 26 1.58471115452228e-05 3.16942230904455e-05 0.999984152888455 27 8.42400272603909e-06 1.68480054520782e-05 0.999991575997274 28 3.23245383325265e-06 6.4649076665053e-06 0.999996767546167 29 1.51974072916483e-06 3.03948145832966e-06 0.999998480259271 30 7.67921468425358e-07 1.53584293685072e-06 0.999999232078532 31 6.8165858847117e-07 1.36331717694234e-06 0.999999318341412 32 5.42688907420529e-07 1.08537781484106e-06 0.999999457311093 33 2.41476903059539e-07 4.82953806119078e-07 0.999999758523097 34 1.11068516516172e-07 2.22137033032345e-07 0.999999888931484 35 2.68833244954545e-08 5.37666489909089e-08 0.999999973116676 36 8.20177681400673e-09 1.64035536280135e-08 0.999999991798223 37 7.44822461663e-09 1.489644923326e-08 0.999999992551775 38 6.5938855544015e-09 1.3187771108803e-08 0.999999993406114 39 4.89932015414293e-09 9.79864030828586e-09 0.99999999510068 40 7.04951851799092e-08 1.40990370359818e-07 0.999999929504815 41 1.82837706763651e-07 3.65675413527302e-07 0.999999817162293 42 2.23198220597419e-06 4.46396441194839e-06 0.999997768017794 43 2.38140371191107e-05 4.76280742382214e-05 0.99997618596288 44 3.03107815266728e-05 6.06215630533456e-05 0.999969689218473 45 0.233879724802656 0.467759449605312 0.766120275197344 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 24 0.888888888888889 NOK 5% type I error level 25 0.925925925925926 NOK 10% type I error level 26 0.962962962962963 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/109j1y1258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/109j1y1258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/1gyi31258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/1gyi31258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/2umi31258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/2umi31258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/36mxq1258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/36mxq1258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/499cn1258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/499cn1258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/5xyy81258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/5xyy81258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/69i7o1258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/69i7o1258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/7x6h71258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/7x6h71258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/8na481258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/8na481258718651.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/913fh1258718651.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187312ms9diw7g59561h/913fh1258718651.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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