regressie tijdreeks1 huwelijken

*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: Sun, 19 Dec 2010 09:25:33 +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/19/t1292750719we3tqdg8vzr23tc.htm/, Retrieved Sun, 19 Dec 2010 10:25:19 +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/19/t1292750719we3tqdg8vzr23tc.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:

» Textbox « » Textfile « » CSV «

3111 3995 5245 5588 10681 10516 7496 9935 10249 6271 3616 3724 2886 3318 4166 6401 9209 9820 7470 8207 9564 5309 3385 3706 2733 3045 3449 5542 10072 9418 7516 7840 10081 4956 3641 3970 2931 3170 3889 4850 8037 12370 6712 7297 10613 5184 3506 3810 2692 3073 3713 4555 7807 10869 9682 7704 9826 5456 3677 3431 2765 3483 3445 6081 8767 9407 6551 12480 9530 5960 3252 3717 2642 2989 3607 5366 8898 9435 7328 8594 11349 5797 3621 3851

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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Huwelijken[t] = + 3744.14285714286 -921.285714285716M1[t] -448.000000000001M2[t] + 186.428571428571M3[t] + 1739.14285714286M4[t] + 5323.14285714286M5[t] + 6518M6[t] + 3792.28571428571M7[t] + 5121.14285714286M8[t] + 6429M9[t] + 1817.71428571429M10[t] -215.857142857143M11[t] + 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) 3744.14285714286 313.066497 11.9596 0 0 M1 -921.285714285716 442.742886 -2.0809 0.041005 0.020502 M2 -448.000000000001 442.742886 -1.0119 0.314987 0.157493 M3 186.428571428571 442.742886 0.4211 0.674954 0.337477 M4 1739.14285714286 442.742886 3.9281 0.000194 9.7e-05 M5 5323.14285714286 442.742886 12.0231 0 0 M6 6518 442.742886 14.7219 0 0 M7 3792.28571428571 442.742886 8.5654 0 0 M8 5121.14285714286 442.742886 11.5669 0 0 M9 6429 442.742886 14.5208 0 0 M10 1817.71428571429 442.742886 4.1056 0.000105 5.3e-05 M11 -215.857142857143 442.742886 -0.4875 0.627353 0.313677 Multiple Linear Regression - Regression Statistics Multiple R 0.962357221655986 R-squared 0.92613142207343 Adjusted R-squared 0.914845944890203 F-TEST (value) 82.0640019945249 F-TEST (DF numerator) 11 F-TEST (DF denominator) 72 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 828.296094784299 Sum Squared Residuals 49397358.2857143 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3111 2822.85714285714 288.142857142857 2 3995 3296.14285714286 698.857142857142 3 5245 3930.57142857143 1314.42857142857 4 5588 5483.28571428571 104.714285714285 5 10681 9067.28571428571 1613.71428571429 6 10516 10262.1428571429 253.857142857146 7 7496 7536.42857142857 -40.4285714285728 8 9935 8865.28571428571 1069.71428571429 9 10249 10173.1428571429 75.8571428571422 10 6271 5561.85714285714 709.142857142857 11 3616 3528.28571428571 87.7142857142862 12 3724 3744.14285714286 -20.1428571428573 13 2886 2822.85714285714 63.1428571428572 14 3318 3296.14285714286 21.8571428571431 15 4166 3930.57142857143 235.428571428571 16 6401 5483.28571428571 917.714285714286 17 9209 9067.28571428571 141.714285714286 18 9820 10262.1428571429 -442.142857142858 19 7470 7536.42857142857 -66.4285714285713 20 8207 8865.28571428571 -658.285714285714 21 9564 10173.1428571429 -609.142857142856 22 5309 5561.85714285714 -252.857142857143 23 3385 3528.28571428571 -143.285714285714 24 3706 3744.14285714286 -38.1428571428573 25 2733 2822.85714285714 -89.8571428571427 26 3045 3296.14285714286 -251.142857142857 27 3449 3930.57142857143 -481.571428571429 28 5542 5483.28571428571 58.7142857142859 29 10072 9067.28571428571 1004.71428571429 30 9418 10262.1428571429 -844.142857142857 31 7516 7536.42857142857 -20.4285714285712 32 7840 8865.28571428571 -1025.28571428571 33 10081 10173.1428571429 -92.1428571428574 34 4956 5561.85714285714 -605.857142857143 35 3641 3528.28571428571 112.714285714286 36 3970 3744.14285714286 225.857142857143 37 2931 2822.85714285714 108.142857142857 38 3170 3296.14285714286 -126.142857142857 39 3889 3930.57142857143 -41.5714285714286 40 4850 5483.28571428571 -633.285714285714 41 8037 9067.28571428571 -1030.28571428571 42 12370 10262.1428571429 2107.85714285714 43 6712 7536.42857142857 -824.428571428571 44 7297 8865.28571428571 -1568.28571428571 45 10613 10173.1428571429 439.857142857143 46 5184 5561.85714285714 -377.857142857143 47 3506 3528.28571428571 -22.2857142857147 48 3810 3744.14285714286 65.8571428571427 49 2692 2822.85714285714 -130.857142857143 50 3073 3296.14285714286 -223.142857142857 51 3713 3930.57142857143 -217.571428571429 52 4555 5483.28571428571 -928.285714285714 53 7807 9067.28571428571 -1260.28571428571 54 10869 10262.1428571429 606.857142857142 55 9682 7536.42857142857 2145.57142857143 56 7704 8865.28571428571 -1161.28571428571 57 9826 10173.1428571429 -347.142857142857 58 5456 5561.85714285714 -105.857142857143 59 3677 3528.28571428571 148.714285714286 60 3431 3744.14285714286 -313.142857142857 61 2765 2822.85714285714 -57.8571428571428 62 3483 3296.14285714286 186.857142857143 63 3445 3930.57142857143 -485.571428571429 64 6081 5483.28571428571 597.714285714286 65 8767 9067.28571428571 -300.285714285715 66 9407 10262.1428571429 -855.142857142857 67 6551 7536.42857142857 -985.428571428571 68 12480 8865.28571428572 3614.71428571428 69 9530 10173.1428571429 -643.142857142856 70 5960 5561.85714285714 398.142857142857 71 3252 3528.28571428571 -276.285714285714 72 3717 3744.14285714286 -27.1428571428573 73 2642 2822.85714285714 -180.857142857143 74 2989 3296.14285714286 -307.142857142857 75 3607 3930.57142857143 -323.571428571429 76 5366 5483.28571428571 -117.285714285714 77 8898 9067.28571428571 -169.285714285714 78 9435 10262.1428571429 -827.142857142857 79 7328 7536.42857142857 -208.428571428571 80 8594 8865.28571428571 -271.285714285714 81 11349 10173.1428571429 1175.85714285714 82 5797 5561.85714285714 235.142857142858 83 3621 3528.28571428571 92.7142857142858 84 3851 3744.14285714286 106.857142857143 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.235286657076699 0.470573314153397 0.764713342923301 16 0.195957374329724 0.391914748659449 0.804042625670276 17 0.312807578285588 0.625615156571176 0.687192421714412 18 0.24144126115285 0.4828825223057 0.75855873884715 19 0.150155473183376 0.300310946366752 0.849844526816624 20 0.270928208226102 0.541856416452203 0.729071791773898 21 0.217232499133059 0.434464998266118 0.782767500866941 22 0.191796033796420 0.383592067592839 0.80820396620358 23 0.130659746172032 0.261319492344064 0.869340253827968 24 0.083962402269435 0.16792480453887 0.916037597730565 25 0.0537449343123651 0.107489868624730 0.946255065687635 26 0.0396876156950839 0.0793752313901678 0.960312384304916 27 0.0537574957328915 0.107514991465783 0.946242504267109 28 0.0369212632619721 0.0738425265239441 0.963078736738028 29 0.0299910867245397 0.0599821734490793 0.97000891327546 30 0.0276193232434885 0.0552386464869769 0.972380676756512 31 0.0165207717226654 0.0330415434453309 0.983479228277335 32 0.0253346607708086 0.0506693215416172 0.974665339229191 33 0.0157037911411956 0.0314075822823912 0.984296208858804 34 0.013989789702135 0.02797957940427 0.986010210297865 35 0.00831587707469414 0.0166317541493883 0.991684122925306 36 0.00496383511638516 0.00992767023277032 0.995036164883615 37 0.0027689880955875 0.005537976191175 0.997231011904413 38 0.00157080348705052 0.00314160697410104 0.99842919651295 39 0.000935348871640524 0.00187069774328105 0.99906465112836 40 0.00101898830933686 0.00203797661867372 0.998981011690663 41 0.00492324021084758 0.00984648042169516 0.995076759789152 42 0.0601738619219452 0.120347723843890 0.939826138078055 43 0.0576295161144967 0.115259032228993 0.942370483885503 44 0.127066066141847 0.254132132283694 0.872933933858153 45 0.102491714080823 0.204983428161647 0.897508285919177 46 0.0784703049330741 0.156940609866148 0.921529695066926 47 0.0546723169285761 0.109344633857152 0.945327683071424 48 0.0370724100017072 0.0741448200034144 0.962927589998293 49 0.0246017465004035 0.0492034930008069 0.975398253499597 50 0.0161218558484203 0.0322437116968406 0.98387814415158 51 0.010684800878866 0.021369601757732 0.989315199121134 52 0.0117408204499125 0.0234816408998249 0.988259179550088 53 0.0188108179611634 0.0376216359223268 0.981189182038837 54 0.0184149316875971 0.0368298633751943 0.981585068312403 55 0.141416555180699 0.282833110361398 0.858583444819301 56 0.412809489658586 0.825618979317172 0.587190510341414 57 0.356930347401577 0.713860694803154 0.643069652598423 58 0.289967912963262 0.579935825926525 0.710032087036738 59 0.223130459092804 0.446260918185608 0.776869540907196 60 0.169609574088078 0.339219148176157 0.830390425911921 61 0.118808413968746 0.237616827937491 0.881191586031254 62 0.0831259735673485 0.166251947134697 0.916874026432651 63 0.055627027407067 0.111254054814134 0.944372972592933 64 0.0400373532949623 0.0800747065899246 0.959962646705038 65 0.0229711429418010 0.0459422858836019 0.9770288570582 66 0.0147420924859567 0.0294841849719135 0.985257907514043 67 0.0104754858911229 0.0209509717822458 0.989524514108877 68 0.712511210506165 0.574977578987671 0.287488789493835 69 0.99029734651091 0.0194053069781812 0.00970265348909059 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.109090909090909 NOK 5% type I error level 20 0.363636363636364 NOK 10% type I error level 27 0.490909090909091 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/10e0om1292750725.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/10e0om1292750725.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/1x7px1292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/1x7px1292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/2x7px1292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/2x7px1292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/37yo01292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/37yo01292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/47yo01292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/47yo01292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/57yo01292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/57yo01292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/6085l1292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/6085l1292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/7th461292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/7th461292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/8th461292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/8th461292750724.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/9th461292750724.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292750719we3tqdg8vzr23tc/9th461292750724.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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