WS7 link 1 zonder seizo

*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: Thu, 19 Nov 2009 11:09:06 -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/19/t1258654192vk0n9kj5bmcjq8i.htm/, Retrieved Thu, 19 Nov 2009 19:10:04 +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/19/t1258654192vk0n9kj5bmcjq8i.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

3.75 0 3.75 0 3.55 0 3.5 0 3.5 0 3.1 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3.21 0 3.25 0 3.25 0 3.45 0 3.5 0 3.5 0 3.64 0 3.75 0 3.93 0 4 0 4.17 0 4.25 0 4.39 0 4.5 0 4.5 0 4.65 0 4.75 0 4.75 0 4.9 0 5 0 5 0 5 0 5 0 5 0 5 0 5 1 5 1 5 1 5 1 5 1 5 1 5.18 1 5.25 1 5.25 1 4.49 1 3.92 1 3.25 1

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 Yt[t] = + 3.57483333333333 + 1.2035Xt[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) 3.57483333333333 0.092193 38.7757 0 0 Xt 1.2035 0.225825 5.3293 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.537244731974702 R-squared 0.288631902034570 Adjusted R-squared 0.278469500635063 F-TEST (value) 28.4019387433954 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 1.14070897239138e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.714121488263727 Sum Squared Residuals 35.697865 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.75 3.57483333333334 0.175166666666661 2 3.75 3.57483333333334 0.175166666666664 3 3.55 3.57483333333333 -0.0248333333333333 4 3.5 3.57483333333333 -0.0748333333333331 5 3.5 3.57483333333333 -0.0748333333333331 6 3.1 3.57483333333333 -0.474833333333333 7 3 3.57483333333333 -0.574833333333333 8 3 3.57483333333333 -0.574833333333333 9 3 3.57483333333333 -0.574833333333333 10 3 3.57483333333333 -0.574833333333333 11 3 3.57483333333333 -0.574833333333333 12 3 3.57483333333333 -0.574833333333333 13 3 3.57483333333333 -0.574833333333333 14 3 3.57483333333333 -0.574833333333333 15 3 3.57483333333333 -0.574833333333333 16 3 3.57483333333333 -0.574833333333333 17 3 3.57483333333333 -0.574833333333333 18 3 3.57483333333333 -0.574833333333333 19 3 3.57483333333333 -0.574833333333333 20 3 3.57483333333333 -0.574833333333333 21 3 3.57483333333333 -0.574833333333333 22 3 3.57483333333333 -0.574833333333333 23 3 3.57483333333333 -0.574833333333333 24 3 3.57483333333333 -0.574833333333333 25 3 3.57483333333333 -0.574833333333333 26 3 3.57483333333333 -0.574833333333333 27 3 3.57483333333333 -0.574833333333333 28 3 3.57483333333333 -0.574833333333333 29 3 3.57483333333333 -0.574833333333333 30 3 3.57483333333333 -0.574833333333333 31 3 3.57483333333333 -0.574833333333333 32 3 3.57483333333333 -0.574833333333333 33 3 3.57483333333333 -0.574833333333333 34 3 3.57483333333333 -0.574833333333333 35 3 3.57483333333333 -0.574833333333333 36 3.21 3.57483333333333 -0.364833333333333 37 3.25 3.57483333333333 -0.324833333333333 38 3.25 3.57483333333333 -0.324833333333333 39 3.45 3.57483333333333 -0.124833333333333 40 3.5 3.57483333333333 -0.0748333333333331 41 3.5 3.57483333333333 -0.0748333333333331 42 3.64 3.57483333333333 0.065166666666667 43 3.75 3.57483333333333 0.175166666666667 44 3.93 3.57483333333333 0.355166666666667 45 4 3.57483333333333 0.425166666666667 46 4.17 3.57483333333333 0.595166666666667 47 4.25 3.57483333333333 0.675166666666667 48 4.39 3.57483333333333 0.815166666666667 49 4.5 3.57483333333333 0.925166666666667 50 4.5 3.57483333333333 0.925166666666667 51 4.65 3.57483333333333 1.07516666666667 52 4.75 3.57483333333333 1.17516666666667 53 4.75 3.57483333333333 1.17516666666667 54 4.9 3.57483333333333 1.32516666666667 55 5 3.57483333333333 1.42516666666667 56 5 3.57483333333333 1.42516666666667 57 5 3.57483333333333 1.42516666666667 58 5 3.57483333333333 1.42516666666667 59 5 3.57483333333333 1.42516666666667 60 5 3.57483333333333 1.42516666666667 61 5 4.77833333333333 0.221666666666667 62 5 4.77833333333333 0.221666666666667 63 5 4.77833333333333 0.221666666666667 64 5 4.77833333333333 0.221666666666667 65 5 4.77833333333333 0.221666666666667 66 5 4.77833333333333 0.221666666666667 67 5.18 4.77833333333333 0.401666666666666 68 5.25 4.77833333333333 0.471666666666667 69 5.25 4.77833333333333 0.471666666666667 70 4.49 4.77833333333333 -0.288333333333333 71 3.92 4.77833333333333 -0.858333333333333 72 3.25 4.77833333333333 -1.52833333333333 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0110676262227507 0.0221352524455015 0.98893237377725 6 0.0283650078684989 0.0567300157369978 0.971634992131501 7 0.0334682115644227 0.0669364231288454 0.966531788435577 8 0.0268595924272703 0.0537191848545406 0.97314040757273 9 0.0185786157194489 0.0371572314388979 0.981421384280551 10 0.0117815321900982 0.0235630643801965 0.988218467809902 11 0.00702596522612478 0.0140519304522496 0.992974034773875 12 0.00399392669503379 0.00798785339006759 0.996006073304966 13 0.00218284109234848 0.00436568218469696 0.997817158907652 14 0.00115422451236561 0.00230844902473122 0.998845775487634 15 0.000593458534654826 0.00118691706930965 0.999406541465345 16 0.000298001835057067 0.000596003670114135 0.999701998164943 17 0.000146730372729542 0.000293460745459084 0.99985326962727 18 7.1116720411098e-05 0.000142233440822196 0.999928883279589 19 3.40602837159122e-05 6.81205674318244e-05 0.999965939716284 20 1.61834320006511e-05 3.23668640013023e-05 0.999983816568 21 7.6604300193506e-06 1.53208600387012e-05 0.99999233956998 22 3.62869907738734e-06 7.25739815477468e-06 0.999996371300923 23 1.72867054987592e-06 3.45734109975184e-06 0.99999827132945 24 8.32797093879814e-07 1.66559418775963e-06 0.999999167202906 25 4.08283839911369e-07 8.16567679822738e-07 0.99999959171616 26 2.05178842445698e-07 4.10357684891396e-07 0.999999794821158 27 1.06596337023033e-07 2.13192674046067e-07 0.999999893403663 28 5.78336990043368e-08 1.15667398008674e-07 0.999999942166301 29 3.31701333634944e-08 6.63402667269888e-08 0.999999966829867 30 2.04144866682267e-08 4.08289733364534e-08 0.999999979585513 31 1.37359432831448e-08 2.74718865662896e-08 0.999999986264057 32 1.03467735929555e-08 2.06935471859109e-08 0.999999989653226 33 8.99790837188508e-09 1.79958167437702e-08 0.999999991002092 34 9.4110320691685e-09 1.8822064138337e-08 0.999999990588968 35 1.25200087193594e-08 2.50400174387188e-08 0.999999987479991 36 1.43239602964578e-08 2.86479205929157e-08 0.99999998567604 37 2.05246112263328e-08 4.10492224526657e-08 0.999999979475389 38 3.88698554926005e-08 7.77397109852011e-08 0.999999961130144 39 1.20975917317664e-07 2.41951834635328e-07 0.999999879024083 40 4.9412817950318e-07 9.8825635900636e-07 0.99999950587182 41 2.40325037085753e-06 4.80650074171506e-06 0.99999759674963 42 1.75482737991849e-05 3.50965475983698e-05 0.9999824517262 43 0.000150998606597814 0.000301997213195627 0.999849001393402 44 0.00138542018951648 0.00277084037903295 0.998614579810484 45 0.00849554261823736 0.0169910852364747 0.991504457381763 46 0.0381503810102342 0.0763007620204683 0.961849618989766 47 0.109310110975255 0.218620221950510 0.890689889024745 48 0.228529383201577 0.457058766403153 0.771470616798423 49 0.368599522020399 0.737199044040799 0.6314004779796 50 0.490026778135532 0.980053556271063 0.509973221864468 51 0.594045538918583 0.811908922162835 0.405954461081417 52 0.671030214757385 0.65793957048523 0.328969785242615 53 0.717822650904098 0.564354698191804 0.282177349095902 54 0.752784443761884 0.494431112476232 0.247215556238116 55 0.775745626479084 0.448508747041832 0.224254373520916 56 0.781184432252928 0.437631135494144 0.218815567747072 57 0.773361838488688 0.453276323022624 0.226638161511312 58 0.753953118700694 0.492093762598612 0.246046881299306 59 0.723231900078203 0.553536199843595 0.276768099921797 60 0.680775429561679 0.638449140876643 0.319224570438321 61 0.591879753829213 0.816240492341575 0.408120246170787 62 0.495888930397792 0.991777860795583 0.504111069602208 63 0.397976475869402 0.795952951738805 0.602023524130598 64 0.304042834185726 0.608085668371451 0.695957165814274 65 0.219716836971512 0.439433673943023 0.780283163028488 66 0.149305211832537 0.298610423665075 0.850694788167462 67 0.115100739260651 0.230201478521302 0.88489926073935 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 33 0.523809523809524 NOK 5% type I error level 38 0.603174603174603 NOK 10% type I error level 42 0.666666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/10vsb31258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/10vsb31258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/1mshj1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/1mshj1258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/23pb81258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/23pb81258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/3anmj1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/3anmj1258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/4j41v1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/4j41v1258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/5fd6o1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/5fd6o1258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/6abz01258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/6abz01258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/79p4d1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/79p4d1258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/8luzm1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/8luzm1258654141.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/9skxn1258654141.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258654192vk0n9kj5bmcjq8i/9skxn1258654141.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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