*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: Fri, 24 Dec 2010 17:45:10 +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/24/t1293212617toaugcr0jgd9k5h.htm/, Retrieved Fri, 24 Dec 2010 18:43:48 +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/24/t1293212617toaugcr0jgd9k5h.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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3010 2590 11290 4700 44,51 2910 2080 11620 4960 45,48 3840 2640 10790 4880 53,1 3580 3000 8380 4090 51,88 3140 2350 9370 3450 48,65 3550 2220 10090 3020 54,35 3250 2540 11130 3070 57,52 2820 2700 10530 3720 63,98 2260 2580 10490 3750 62,91 2060 2420 10570 3910 58,54 2120 2090 11170 4120 55,24 2210 2000 11610 4780 56,86 2190 1860 10920 3070 62,99 2180 1980 11570 4100 60,21 2350 2690 12960 3900 62,06 2440 3040 11190 3020 70,26 2370 2450 11920 3220 69,78 2440 2650 14930 4030 68,56 2610 2710 14520 4210 73,67 3040 3230 12970 4510 73,23 3190 3160 13870 4320 61,96 3120 3040 13250 3890 57,81 3170 2630 12760 7280 58,76 3600 2730 14050 9640 62,47 3420 2830 14660 5680 53,68 3650 2320 15010 6320 57,56 4180 2410 15020 5820 62,05 2960 3080 13090 4890 67,49 2710 2260 13190 3320 67,21 2950 2300 11390 2930 71,05 3030 3600 10110 3530 76,93 3770 3380 8240 3690 70,76 4740 3670 7920 3750 77,17 4450 3040 7700 3330 82,34 5550 2840 7920 4790 92,41 5580 2810 8130 5990 90,93 5890 2980 10510 5290 92, 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 7 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Garnalen[t] = + 1054.73859795288 -0.389954780048043Kabeljauw[t] -0.116309920068412Tong[t] + 0.244707593115734Zeeduivel[t] + 58.4447910371906Olie[t] -15.5934957349337t + 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) 1054.73859795288 1242.574709 0.8488 0.399188 0.199594 Kabeljauw -0.389954780048043 0.313731 -1.243 0.218489 0.109245 Tong -0.116309920068412 0.07456 -1.56 0.123779 0.06189 Zeeduivel 0.244707593115734 0.097637 2.5063 0.014795 0.007397 Olie 58.4447910371906 8.110639 7.2059 0 0 t -15.5934957349337 7.10635 -2.1943 0.031909 0.015954 Multiple Linear Regression - Regression Statistics Multiple R 0.703122021824278 R-squared 0.494380577574261 Adjusted R-squared 0.454252051984917 F-TEST (value) 12.3199287866569 F-TEST (DF numerator) 5 F-TEST (DF denominator) 63 p-value 2.40444402166418e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1090.81058453612 Sum Squared Residuals 74961667.0741697 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3010 2467.52656103045 542.473438969554 2 2910 2732.74315101361 177.256848986392 3 3840 3021.08491236269 818.915087637313 4 3580 2880.79295954853 699.207040451474 5 3140 2658.1337153329 481.8662846671 6 3550 2837.40224242717 712.597757572826 7 3250 2773.5662674494 476.433732550602 8 2820 3301.9792445733 -481.979244573303 9 2260 3282.63802063055 -1022.63802063055 10 2060 3103.88197416382 -1043.88197416382 11 2120 3005.70838793527 -885.708387935272 12 2210 3230.22203051119 -1020.22203051119 13 2190 3289.29263366026 -1099.29263366026 14 2180 3240.87541810091 -1060.87541810091 15 2350 2845.92458443243 -495.924584432433 16 2440 3163.62007876489 -723.620078764891 17 2370 3314.08168053366 -944.081680533656 18 2440 2997.31487474157 -557.314874741566 19 2610 3348.71140839267 -738.711408392674 20 3040 3358.31837301715 -318.318373017154 21 3190 2560.17554614288 629.824453857116 22 3120 2315.71862661202 804.281373387976 23 3170 3402.07974367798 -232.079743677979 24 3600 3991.7910675511 -391.791067551099 25 3420 2383.48126061442 1036.51873938558 26 3650 2909.43487949841 740.565120501589 27 4180 2997.64566955759 1182.35433044241 28 2960 3035.62221856718 -75.6222185671848 29 2710 2927.60518778269 -217.605187782690 30 2950 3234.76339323665 -284.763393236652 31 3030 3351.58530829495 -321.585308294952 32 3770 3317.83026889757 452.169731102431 33 4740 3615.68262750593 1124.31737249407 34 4450 4070.73120616998 379.268793830018 35 5550 5053.35261572309 496.647384276913 36 5580 5232.18350117907 347.816498820933 37 5890 4775.24075668862 1114.75924331138 38 7480 5171.9136344756 2308.08636552440 39 10450 5381.84772913497 5068.15227086503 40 6360 5735.12277705153 624.877222948473 41 6710 6091.69750834594 618.302491654064 42 6200 6502.39279965423 -302.392799654229 43 4490 6215.78202853109 -1725.78202853109 44 3480 5439.15787542671 -1959.15787542671 45 2520 4431.2870302797 -1911.28703027970 46 1920 3359.09295347234 -1439.09295347234 47 2010 2311.36989226247 -301.369892262471 48 1950 2062.29996250834 -112.299962508339 49 2240 3549.44389266759 -1309.44389266759 50 2370 1967.56075122512 402.439248774879 51 2840 2099.38449283507 740.61550716493 52 2700 2548.68506685741 151.314933142592 53 2980 2404.81498668755 575.185013312445 54 3290 2740.45480732615 549.545192673847 55 3300 2380.85120456913 919.148795430874 56 3000 2747.00811955736 252.991880442644 57 2330 2545.66452811223 -215.664528112230 58 2190 3141.39934739862 -951.399347398616 59 1970 3284.82409133136 -1314.82409133136 60 2170 3011.76782380086 -841.767823800859 61 2830 3635.58902190936 -805.589021909361 62 3190 3606.91716935954 -416.917169359536 63 3550 3852.27577345224 -302.275773452241 64 3240 4008.75512930663 -768.755129306631 65 3450 3313.1163402074 136.8836597926 66 3570 2443.12862654237 1126.87137345763 67 3230 2375.58839863059 854.411601369415 68 3260 2914.5347881434 345.4652118566 69 2700 2997.52905261454 -297.529052614543 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.0331486976878987 0.0662973953757974 0.966851302312101 10 0.0247742596325428 0.0495485192650856 0.975225740367457 11 0.0187335726992538 0.0374671453985076 0.981266427300746 12 0.0125243695498434 0.0250487390996868 0.987475630450157 13 0.00466644412099072 0.00933288824198145 0.99533355587901 14 0.00258522023896237 0.00517044047792475 0.997414779761038 15 0.00137405595352234 0.00274811190704468 0.998625944046478 16 0.000481278050025466 0.000962556100050932 0.999518721949975 17 0.000239254789925761 0.000478509579851523 0.999760745210074 18 0.000119828443462665 0.00023965688692533 0.999880171556537 19 8.1652100180557e-05 0.000163304200361114 0.99991834789982 20 8.21371466234157e-05 0.000164274293246831 0.999917862853377 21 4.49547343776795e-05 8.99094687553591e-05 0.999955045265622 22 1.60678323657249e-05 3.21356647314498e-05 0.999983932167634 23 1.24946547448530e-05 2.49893094897060e-05 0.999987505345255 24 6.00202270161522e-06 1.20040454032304e-05 0.999993997977298 25 2.26591177520537e-06 4.53182355041074e-06 0.999997734088225 26 3.31641318283158e-06 6.63282636566316e-06 0.999996683586817 27 2.09230401870338e-05 4.18460803740677e-05 0.999979076959813 28 8.6381985881277e-06 1.72763971762554e-05 0.999991361801412 29 5.37581103993991e-06 1.07516220798798e-05 0.99999462418896 30 8.340662792114e-06 1.6681325584228e-05 0.999991659337208 31 5.4709959470224e-06 1.09419918940448e-05 0.999994529004053 32 2.61306197772195e-06 5.2261239554439e-06 0.999997386938022 33 7.91349219094824e-06 1.58269843818965e-05 0.99999208650781 34 1.18568518216917e-05 2.37137036433834e-05 0.999988143148178 35 9.35449039048027e-05 0.000187089807809605 0.999906455096095 36 8.3988848563861e-05 0.000167977697127722 0.999916011151436 37 0.000195696405531341 0.000391392811062682 0.999804303594469 38 0.00455359148172016 0.00910718296344031 0.99544640851828 39 0.968518718414929 0.0629625631701419 0.0314812815850709 40 0.977152944845296 0.0456941103094084 0.0228470551547042 41 0.993872643428117 0.0122547131437670 0.00612735657188348 42 0.999620639023921 0.000758721952157366 0.000379360976078683 43 0.999909923656288 0.000180152687424874 9.00763437124369e-05 44 0.999977245995835 4.55080083302163e-05 2.27540041651081e-05 45 0.999978542937438 4.29141251237662e-05 2.14570625618831e-05 46 0.999964952668919 7.00946621625266e-05 3.50473310812633e-05 47 0.999915128159147 0.000169743681705112 8.4871840852556e-05 48 0.99979624136774 0.000407517264518308 0.000203758632259154 49 0.999812502233818 0.000374995532363468 0.000187497766181734 50 0.999564441872308 0.000871116255384562 0.000435558127692281 51 0.998940330109947 0.00211933978010635 0.00105966989005318 52 0.998063658143477 0.00387268371304586 0.00193634185652293 53 0.996038032093653 0.00792393581269429 0.00396196790634714 54 0.992932167358912 0.0141356652821753 0.00706783264108767 55 0.985754399858103 0.0284912002837942 0.0142456001418971 56 0.988645421820178 0.0227091563596434 0.0113545781798217 57 0.974100606328388 0.0517987873432241 0.0258993936716120 58 0.940651819890711 0.118696360218578 0.0593481801092891 59 0.906789134714772 0.186421730570457 0.0932108652852283 60 0.943204028844331 0.113591942311338 0.0567959711556689 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 38 0.730769230769231 NOK 5% type I error level 46 0.884615384615385 NOK 10% type I error level 49 0.942307692307692 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/10f3mx1293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/10f3mx1293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/1n2qy1293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/1n2qy1293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/2gb711293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/2gb711293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/3gb711293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/3gb711293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/4gb711293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/4gb711293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/5gb711293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/5gb711293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/6qkp41293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/6qkp41293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/7ulns1293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/7ulns1293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/8ulns1293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/8ulns1293212701.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/9mcmd1293212701.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293212617toaugcr0jgd9k5h/9mcmd1293212701.ps (open in new window)

Parameters (Session):

par1 = 12 ; par2 = -1.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 0 ; par10 = FALSE ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 0 ; par10 = FALSE ;

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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