WS10: MR

*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: Tue, 14 Dec 2010 15:57:38 +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/14/t1292342238taas2sje9nkjap4.htm/, Retrieved Tue, 14 Dec 2010 16:57:18 +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/14/t1292342238taas2sje9nkjap4.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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0.6000 1.0800 1.0100 1.6100 1.7700 1.3900 1.7700 0.6000 1.0900 1.0000 1.5800 1.7700 1.3500 1.9800 0.6000 1.1000 1.0000 1.6900 1.7700 1.3900 1.9400 0.6000 1.1000 1.0000 1.7800 1.7700 1.3700 1.8500 0.6000 1.1100 1.0600 1.7600 1.7400 1.3800 1.8400 0.6000 1.1000 1.2200 1.8300 1.7800 1.5100 1.8200 0.6000 1.1000 1.2400 1.8000 1.7800 1.5100 1.8300 0.6000 1.1100 1.3400 1.5700 1.7800 1.4500 1.9100 0.6100 1.1100 1.3000 1.4500 1.7800 1.3000 1.8500 0.6100 1.1100 1.0500 1.4000 1.8100 1.2900 1.8100 0.6100 1.1100 1.0000 1.5500 1.8400 1.4400 1.8300 0.6100 1.1100 1.0000 1.5800 1.8000 1.4600 1.7900 0.6100 1.1200 1.0100 1.5800 1.7800 1.5000 1.8000 0.6100 1.1100 1.0200 1.5900 1.7600 1.3900 1.8200 0.6200 1.1100 1.0600 1.8000 1.7400 1.4800 1.8800 0.6200 1.1200 1.0900 1.9900 1.7200 1.5200 2.0100 0.6200 1.1200 1.0900 2.0600 1.7300 1.6800 1.9700 0.6300 1.1100 1.1500 2.0600 1.7700 1.7400 1.9200 0.6300 1.1200 1.2500 2.0800 1.8100 1.7200 1.9800 0.6300 1.1100 1.3700 2.0000 1.8300 1.7400 2.0200 0.6300 1.1100 1.5100 1.8 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Vruchtesappen[t] = + 0.779774004422962 + 0.546101358198386Mineraalwater[t] + 0.020559397509594Jonagold[t] -0.0531403430145952Sinaasappelen[t] + 0.0514045587408016Citroenen[t] + 0.00879691427214595Pompelmoezen[t] -0.0290627050312829Bananen[t] + 0.00304698650932119M1[t] + 0.00415091425424844M2[t] + 0.0177519317642119M3[t] + 0.0255370161567975M4[t] + 0.0263002308460657M5[t] + 0.0250378353896985M6[t] + 0.0201921664759066M7[t] + 0.0107364495530763M8[t] -0.00110314307674761M9[t] + 0.00187196514910997M10[t] + 0.00263498573687797M11[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) 0.779774004422962 0.053146 14.6724 0 0 Mineraalwater 0.546101358198386 0.09754 5.5988 1e-06 0 Jonagold 0.020559397509594 0.010587 1.9419 0.057377 0.028688 Sinaasappelen -0.0531403430145952 0.021163 -2.511 0.015063 0.007532 Citroenen 0.0514045587408016 0.040179 1.2794 0.206233 0.103117 Pompelmoezen 0.00879691427214595 0.016926 0.5197 0.605384 0.302692 Bananen -0.0290627050312829 0.0256 -1.1353 0.261271 0.130636 M1 0.00304698650932119 0.007838 0.3887 0.699003 0.349502 M2 0.00415091425424844 0.007931 0.5234 0.60285 0.301425 M3 0.0177519317642119 0.008686 2.0437 0.045876 0.022938 M4 0.0255370161567975 0.009652 2.6457 0.010657 0.005329 M5 0.0263002308460657 0.010111 2.601 0.011967 0.005984 M6 0.0250378353896985 0.010324 2.4253 0.018671 0.009336 M7 0.0201921664759066 0.009607 2.1018 0.040254 0.020127 M8 0.0107364495530763 0.008733 1.2294 0.224248 0.112124 M9 -0.00110314307674761 0.008563 -0.1288 0.897969 0.448984 M10 0.00187196514910997 0.007765 0.2411 0.81042 0.40521 M11 0.00263498573687797 0.008019 0.3286 0.743726 0.371863 Multiple Linear Regression - Regression Statistics Multiple R 0.788425925938382 R-squared 0.621615440691795 Adjusted R-squared 0.502494375724397 F-TEST (value) 5.21835026291886 F-TEST (DF numerator) 17 F-TEST (DF denominator) 54 p-value 1.53782326195451e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0131791131493992 Sum Squared Residuals 0.00937920726385202 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.08 1.09746363698664 -0.0174636369866352 2 1.09 1.09350113641945 -0.00350113641945134 3 1.1 1.10277110096995 -0.00277110096994645 4 1.1 1.10821325965859 -0.008213259658591 5 1.11 1.11010930448954 -0.000109304489537039 6 1.1 1.11219762392932 -0.0121976239293199 7 1.1 1.10906672620585 -0.00906672620584494 8 1.11 1.1110363966685 -0.00103639666849946 9 1.11 1.11063650804308 -0.000636508043082194 10 1.11 1.11374545986302 -0.00374545986302495 11 1.11 1.10778987892554 0.00221012107445575 12 1.11 1.10284294703529 0.00715705296470941 13 1.12 1.10512868586546 0.0148713141345353 14 1.11 1.10332979830996 0.00667020169003581 15 1.11 1.11007460207703 -7.46020770304142e-05 16 1.12 1.10392543696413 0.0160745630358663 17 1.12 1.10405288771458 0.015947112285417 18 1.11 1.1135222021483 -0.0035222021483003 19 1.12 1.10980614788749 0.0101938521125119 20 1.11 1.10711030736598 0.00288969263401576 21 1.11 1.11245551107757 -0.00245551107757197 22 1.1 1.11766543240017 -0.0176654324001702 23 1.1 1.11612035386309 -0.0161203538630898 24 1.1 1.12111071989385 -0.0211107198938528 25 1.11 1.11861593915964 -0.00861593915964032 26 1.1 1.11915105821195 -0.0191510582119503 27 1.1 1.11647273613244 -0.0164727361324386 28 1.09 1.11778464636738 -0.0277846463673784 29 1.1 1.11491014612512 -0.0149101461251176 30 1.1 1.10800360512865 -0.00800360512865321 31 1.11 1.11462420031612 -0.00462420031612207 32 1.13 1.12529997230783 0.00470002769216908 33 1.13 1.12000927087847 0.00999072912153351 34 1.13 1.12411369529289 0.00588630470710852 35 1.13 1.12135937076668 0.00864062923331539 36 1.14 1.12273337885911 0.0172666211408879 37 1.14 1.12162700823632 0.0183729917636822 38 1.14 1.12128878538403 0.0187112146159731 39 1.15 1.12358640449399 0.0264135955060138 40 1.15 1.12691162217525 0.0230883778247523 41 1.15 1.13380662975382 0.0161933702461836 42 1.15 1.13257799568550 0.0174220043145016 43 1.15 1.13957093305875 0.0104290669412504 44 1.15 1.14474158940619 0.00525841059381253 45 1.14 1.13763725662358 0.00236274337641909 46 1.14 1.12630394557236 0.0136960544276440 47 1.14 1.12507315406766 0.0149268459323446 48 1.13 1.1220117988821 0.00798820111789947 49 1.12 1.11955169104657 0.000448308953429246 50 1.13 1.12102747011446 0.00897252988554142 51 1.13 1.12486354226767 0.00513645773233391 52 1.13 1.12958289090757 0.000417109092426707 53 1.12 1.13009216977519 -0.0100921697751864 54 1.13 1.13594424897467 -0.0059442489746687 55 1.12 1.12819853156594 -0.00819853156593815 56 1.12 1.13367206724672 -0.0136720672467200 57 1.11 1.12090537865551 -0.0109053786555067 58 1.11 1.1125430757038 -0.00254307570379909 59 1.11 1.11185492155119 -0.00185492155119041 60 1.11 1.11572882358367 -0.00572882358367172 61 1.14 1.14761303870537 -0.00761303870537125 62 1.15 1.16170175156015 -0.0117017515601486 63 1.15 1.16223161405893 -0.0122316140589322 64 1.16 1.16358214392708 -0.00358214392707593 65 1.15 1.15702886214176 -0.00702886214175953 66 1.16 1.14775432413356 0.0122456758664405 67 1.13 1.12873346096586 0.00126653903414282 68 1.13 1.12813966700478 0.00186033299522211 69 1.12 1.11835607472179 0.00164392527820829 70 1.12 1.11562839116776 0.00437160883224174 71 1.11 1.11780232082584 -0.0078023208258355 72 1.11 1.11557233174597 -0.00557233174597229 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.242500833199019 0.485001666398037 0.757499166800981 22 0.311820448319106 0.623640896638212 0.688179551680894 23 0.364286473756926 0.728572947513852 0.635713526243074 24 0.361057318988073 0.722114637976147 0.638942681011927 25 0.375686860201585 0.75137372040317 0.624313139798415 26 0.362683142200023 0.725366284400046 0.637316857799977 27 0.314008251633891 0.628016503267782 0.685991748366109 28 0.461520316103963 0.923040632207927 0.538479683896037 29 0.406569562061594 0.813139124123188 0.593430437938406 30 0.554869802353559 0.890260395292882 0.445130197646441 31 0.527238856000023 0.945522287999954 0.472761143999977 32 0.469724853148815 0.93944970629763 0.530275146851185 33 0.415525381332801 0.831050762665603 0.584474618667199 34 0.362150646124602 0.724301292249204 0.637849353875398 35 0.293509195449257 0.587018390898514 0.706490804550743 36 0.590117075936303 0.819765848127393 0.409882924063697 37 0.625811731530852 0.748376536938296 0.374188268469148 38 0.718358897413144 0.563282205173712 0.281641102586856 39 0.89218605549867 0.215627889002658 0.107813944501329 40 0.977703629259932 0.0445927414801356 0.0222963707400678 41 0.987702301060152 0.0245953978796962 0.0122976989398481 42 0.984632917487315 0.0307341650253697 0.0153670825126849 43 0.987896445693875 0.0242071086122505 0.0121035543061252 44 0.979118671916982 0.0417626561660367 0.0208813280830184 45 0.969146205140613 0.0617075897187749 0.0308537948593875 46 0.953873469338778 0.0922530613224438 0.0461265306612219 47 0.916374229609445 0.167251540781110 0.0836257703905552 48 0.986537358343332 0.0269252833133354 0.0134626416566677 49 0.97716345262788 0.0456730947442384 0.0228365473721192 50 0.938313116310257 0.123373767379487 0.0616868836897434 51 0.960408619875657 0.0791827602486869 0.0395913801243434 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 7 0.225806451612903 NOK 10% type I error level 10 0.32258064516129 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/1038sw1292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/1038sw1292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/1f7d21292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/1f7d21292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/27yu51292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/27yu51292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/37yu51292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/37yu51292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/47yu51292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/47yu51292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/57yu51292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/57yu51292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/60qtq1292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/60qtq1292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/7bzbt1292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/7bzbt1292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/8bzbt1292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/8bzbt1292342249.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/9bzbt1292342249.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292342238taas2sje9nkjap4/9bzbt1292342249.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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