Paper Interactie effecten

*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, 05 Dec 2010 18:20:46 +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/05/t1291573432yq0cv1deh4w8lv3.htm/, Retrieved Sun, 05 Dec 2010 19:23:55 +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/05/t1291573432yq0cv1deh4w8lv3.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 «

1 24 24 14 14 11 11 12 12 24 26 26 0 25 0 11 0 7 0 8 0 25 23 0 0 17 0 6 0 17 0 8 0 30 25 0 1 18 18 12 12 10 10 8 8 19 23 23 0 18 0 8 0 12 0 9 0 22 19 0 0 16 0 10 0 12 0 7 0 22 29 0 0 20 0 10 0 11 0 4 0 25 25 0 0 16 0 11 0 11 0 11 0 23 21 0 0 18 0 16 0 12 0 7 0 17 22 0 0 17 0 11 0 13 0 7 0 21 25 0 1 23 23 13 13 14 14 12 12 19 24 24 0 30 0 12 0 16 0 10 0 19 18 0 0 23 0 8 0 11 0 10 0 15 22 0 0 18 0 12 0 10 0 8 0 16 15 0 1 15 15 11 11 11 11 8 8 23 22 22 1 12 12 4 4 15 15 4 4 27 28 28 0 21 0 9 0 9 0 9 0 22 20 0 1 15 15 8 8 11 11 8 8 14 12 12 1 20 20 8 8 17 17 7 7 22 24 24 0 31 0 14 0 17 0 11 0 23 20 0 0 27 0 15 0 11 0 9 0 23 21 0 0 21 0 9 0 14 0 13 0 19 21 0 1 31 31 14 14 10 10 8 8 18 23 23 1 19 19 11 11 11 11 8 8 20 28 28 0 16 0 8 0 15 0 9 0 23 24 0 0 20 0 9 0 15 0 6 0 25 24 0 1 21 21 9 9 13 13 9 9 19 24 24 1 22 22 9 9 16 16 9 9 24 23 23 0 17 0 9 0 13 0 6 0 22 23 0 0 25 0 16 0 18 0 16 0 26 24 0 0 26 0 11 0 18 0 5 0 29 18 0 0 25 0 8 0 12 0 7 0 32 25 0 0 17 0 9 0 17 0 9 0 25 21 0 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 8 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation PS[t] = + 7.05980418777325 -0.625089264853433M[t] + 0.296179087537003CM[t] + 0.070642119661626CM_M[t] -0.283469161339915D[t] -0.193290505489402D_M[t] + 0.260800182428789PE[t] -0.27410715866219PE_M[t] -0.0177004220449542PC[t] + 0.114293878489504PC_M[t] + 0.392234889899682O[t] + 0.135450774613635O_M[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) 7.05980418777325 2.88598 2.4462 0.015658 0.007829 M -0.625089264853433 5.017803 -0.1246 0.901037 0.450518 CM 0.296179087537003 0.077759 3.8089 0.000207 0.000104 CM_M 0.070642119661626 0.118583 0.5957 0.552312 0.276156 D -0.283469161339915 0.155718 -1.8204 0.070804 0.035402 D_M -0.193290505489402 0.239269 -0.8078 0.420535 0.210268 PE 0.260800182428789 0.136399 1.912 0.057885 0.028943 PE_M -0.27410715866219 0.22474 -1.2197 0.224614 0.112307 PC -0.0177004220449542 0.161925 -0.1093 0.913109 0.456554 PC_M 0.114293878489504 0.282815 0.4041 0.686726 0.343363 O 0.392234889899682 0.094006 4.1725 5.2e-05 2.6e-05 O_M 0.135450774613635 0.166314 0.8144 0.416763 0.208381 Multiple Linear Regression - Regression Statistics Multiple R 0.624684363935184 R-squared 0.390230554545106 Adjusted R-squared 0.342994893277473 F-TEST (value) 8.2613547492031 F-TEST (DF numerator) 11 F-TEST (DF denominator) 142 p-value 4.33513225317483e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.45563605556431 Sum Squared Residuals 1695.68171788928 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 24 23.2963605761899 0.703639423810088 2 25 22.0515209697938 2.94847903020616 3 30 24.4919056802846 5.50809431971537 4 19 20.092828823572 -1.09282882357201 5 22 20.5460357715548 1.45396422844518 6 22 23.3444890168877 -1.34448901688771 7 25 22.7525668911431 2.24743310885693 8 23 19.5915388657417 3.40846113425826 9 17 19.4903879946245 -2.49038799462446 10 21 22.0490595659149 -1.04905956591486 11 19 22.3110067780938 -3.31100677809376 12 19 23.5995735944097 -4.59957359440972 13 15 22.9251352744651 -7.92513527446513 14 16 17.3393196237838 -1.33931962378381 15 23 18.9281322280587 4.07186777194127 16 27 23.8914985306362 3.10850146936385 17 22 20.7609382154392 1.23906178456077 18 14 15.0815545834135 -1.08155458341351 19 22 23.0714532797215 -1.0714532797215 20 23 24.3563838994501 -1.35638389945009 21 23 21.751033027379 1.24896697262098 22 19 22.386372329303 -3.38637232930304 23 18 23.9079851834956 -5.90798518349556 24 20 23.5615310439331 -3.56153104393315 25 23 22.6972525932656 0.302747406734413 26 25 23.6516010482085 1.34839895179145 27 19 23.2079296379135 -4.20792963791352 28 24 23.0071442518986 0.992855748101375 29 22 21.8492285308403 0.15077146915972 30 26 23.753608683351 2.24639131664902 31 29 23.308428880684 5.69157111931603 32 32 25.0080995678018 6.99190043219816 33 25 22.0548582146212 2.94514178537879 34 29 24.7244641139198 4.27553588608019 35 28 25.3643409972118 2.63565900278825 36 17 15.5076041339478 1.49239586605216 37 28 26.2470708498501 1.75292915014988 38 29 22.5373698650511 6.46263013494893 39 26 26.9548735237134 -0.954873523713413 40 25 23.5378228848192 1.46217711518078 41 14 17.8136992889837 -3.81369928898374 42 25 22.6493295001716 2.3506704998284 43 26 21.7799905716279 4.22000942837209 44 20 20.0344295547694 -0.0344295547693637 45 18 21.9026533709004 -3.90265337090037 46 32 24.9894333257504 7.01056667424956 47 25 24.8237073934257 0.176292606574296 48 25 22.4499877772336 2.55001222276644 49 23 21.3282065337459 1.67179346625409 50 21 22.0241795361065 -1.02417953610648 51 20 24.1192700550911 -4.11927005509109 52 15 16.3867168274286 -1.38671682742862 53 30 25.0578463794026 4.9421536205974 54 24 25.438171986193 -1.43817198619304 55 26 24.3193933402654 1.68060665973459 56 24 20.8589458013546 3.14105419864537 57 22 22.5676753400154 -0.567675340015414 58 14 16.4788312268021 -2.4788312268021 59 24 22.2816374498657 1.71836255013428 60 24 22.4413896017506 1.55861039824945 61 24 23.7954534317768 0.204546568223215 62 24 20.2669622301674 3.73303776983263 63 19 17.8086911540746 1.19130884592543 64 31 28.0219480280792 2.97805197192078 65 22 27.2829218727514 -5.28292187275137 66 27 20.7946367735318 6.20536322646824 67 19 17.1768627080926 1.82313729190742 68 25 22.3982960416233 2.60170395837666 69 20 24.7199321607138 -4.7199321607138 70 21 21.3806855541542 -0.380685554154177 71 27 27.3447604372431 -0.344760437243079 72 23 24.918061415164 -1.918061415164 73 25 25.8209104633302 -0.820910463330248 74 20 22.2621630884043 -2.26216308840432 75 22 22.7834712031622 -0.783471203162157 76 23 23.6257640178505 -0.625764017850549 77 25 24.1480252811678 0.851974718832198 78 25 23.7070951207816 1.29290487921837 79 17 23.6155185631486 -6.61551856314859 80 19 20.6256858519544 -1.62568585195438 81 25 24.1250687037974 0.874931296202566 82 19 22.8209917670929 -3.82099176709295 83 20 22.0213024626656 -2.02130246266558 84 26 22.6388106667354 3.36118933326456 85 23 21.2182668316308 1.7817331683692 86 27 24.5053532907546 2.49464670924538 87 17 20.2345906264913 -3.23459062649129 88 17 22.5483369070821 -5.54833690708213 89 17 19.5594726829038 -2.55947268290383 90 22 22.3915505023439 -0.391550502343912 91 21 24.1805516259676 -3.18055162596756 92 32 28.869170191878 3.13082980812197 93 21 24.0532027303745 -3.05320273037451 94 21 24.6828295359995 -3.68282953599951 95 18 20.7113502933206 -2.71135029332061 96 18 21.4423144034967 -3.44231440349674 97 23 23.0091627916008 -0.00916279160077999 98 19 20.1009913417402 -1.10099134174018 99 20 21.4861209705464 -1.48612097054643 100 21 22.4874405664953 -1.48744056649528 101 20 24.3194094496421 -4.31940944964213 102 17 19.1595363624267 -2.15953636242665 103 18 20.1639037335988 -2.16390373359879 104 19 20.713801336222 -1.71380133622203 105 22 22.4499438992702 -0.449943899270173 106 15 17.4973160883322 -2.49731608833222 107 14 19.1005411779597 -5.10054117795971 108 18 26.4591005600035 -8.45910056000355 109 24 21.8203614645123 2.17963853548773 110 35 23.6476890163372 11.3523109836628 111 29 18.9377002921953 10.0622997078047 112 21 22.2464539165878 -1.24645391658783 113 20 17.9121446884937 2.08785531150626 114 22 22.2648021800946 -0.264802180094649 115 13 15.666614386354 -2.66661438635403 116 26 22.914804118222 3.08519588177802 117 17 17.3696081117779 -0.369608111777926 118 25 20.516136674431 4.48386332556899 119 20 20.499208019904 -0.499208019904032 120 19 17.630964521185 1.369035478815 121 21 21.2100820711648 -0.210082071164784 122 22 21.1855747038094 0.814425296190587 123 24 22.790530931523 1.20946906847698 124 21 23.2463420430303 -2.24634204303034 125 26 25.3982166955846 0.601783304415367 126 24 20.7135090861223 3.28649091387771 127 16 20.4723786369852 -4.47237863698522 128 23 22.4362562648342 0.563743735165762 129 18 20.8287187190179 -2.82871871901795 130 16 22.1201978263597 -6.12019782635974 131 26 22.0830703003735 3.91692969962649 132 19 19.5740261208898 -0.574026120889791 133 21 17.3195499692259 3.68045003077414 134 21 22.2110311335162 -1.21103113351623 135 22 18.6736650672247 3.32633493277529 136 23 19.6557948571746 3.34420514282541 137 29 24.9077129715797 4.09228702842034 138 21 20.0270788662912 0.97292113370882 139 21 20.0408396685598 0.959160331440173 140 23 22.4625319652193 0.537468034780665 141 27 23.289727976947 3.71027202305298 142 25 25.4168956603399 -0.416895660339886 143 21 21.0217603368497 -0.0217603368497115 144 10 17.3167990957428 -7.31679909574281 145 20 22.6268869544151 -2.62688695441514 146 26 22.337767595239 3.662232404761 147 24 24.262148957464 -0.26214895746405 148 29 31.924161423221 -2.92416142322096 149 19 19.2763403620576 -0.276340362057584 150 24 22.6958211237593 1.30417887624075 151 19 21.0647587510409 -2.06475875104092 152 24 23.3219002501796 0.67809974982041 153 22 22.2548111099437 -0.254811109943715 154 17 24.2225438314031 -7.22254383140306 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 15 0.971140216261243 0.0577195674775142 0.0288597837387571 16 0.940991784782653 0.118016430434693 0.0590082152173467 17 0.896452661139316 0.207094677721368 0.103547338860684 18 0.832860848873107 0.334278302253786 0.167139151126893 19 0.821460230990964 0.357079538018073 0.178539769009036 20 0.807845205704287 0.384309588591425 0.192154794295713 21 0.790356195849519 0.419287608300962 0.209643804150481 22 0.743912489475258 0.512175021049483 0.256087510524741 23 0.692254225976114 0.615491548047773 0.307745774023886 24 0.713619459499475 0.572761081001049 0.286380540500525 25 0.638217574462226 0.723564851075547 0.361782425537774 26 0.561282620156937 0.877434759686126 0.438717379843063 27 0.510914653155239 0.978170693689521 0.489085346844761 28 0.482254100413728 0.964508200827455 0.517745899586272 29 0.414570754773301 0.829141509546602 0.585429245226699 30 0.425954283129319 0.851908566258638 0.574045716870681 31 0.517701203376458 0.964597593247084 0.482298796623542 32 0.658302905035247 0.683394189929506 0.341697094964753 33 0.620775651353814 0.758448697292372 0.379224348646186 34 0.690362588758182 0.619274822483635 0.309637411241818 35 0.730989118760846 0.538021762478308 0.269010881239154 36 0.69128590027984 0.617428199440318 0.308714099720159 37 0.639582217967185 0.72083556406563 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0.383263959015615 0.76652791803123 0.616736040984385 74 0.355922472209099 0.711844944418198 0.644077527790901 75 0.312382967271722 0.624765934543445 0.687617032728278 76 0.274569070330402 0.549138140660805 0.725430929669598 77 0.237461167618929 0.474922335237858 0.762538832381071 78 0.207742054432064 0.415484108864127 0.792257945567936 79 0.321939016482694 0.643878032965387 0.678060983517306 80 0.29363788239093 0.58727576478186 0.70636211760907 81 0.255133149625803 0.510266299251606 0.744866850374197 82 0.254141859415932 0.508283718831864 0.745858140584068 83 0.229426734388909 0.458853468777819 0.770573265611091 84 0.228598545190489 0.457197090380979 0.77140145480951 85 0.203055443291978 0.406110886583956 0.796944556708022 86 0.187336753961978 0.374673507923956 0.812663246038022 87 0.180319448691667 0.360638897383333 0.819680551308333 88 0.220392958107721 0.440785916215443 0.779607041892279 89 0.198239747733655 0.39647949546731 0.801760252266345 90 0.169617410516135 0.33923482103227 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0.97077483734225 108 0.128028114095878 0.256056228191755 0.871971885904122 109 0.129923949723467 0.259847899446935 0.870076050276533 110 0.599291445658965 0.80141710868207 0.400708554341035 111 0.883248466929744 0.233503066140512 0.116751533070256 112 0.854287106411135 0.29142578717773 0.145712893588865 113 0.913368899643712 0.173262200712576 0.0866311003562878 114 0.889572288043413 0.220855423913174 0.110427711956587 115 0.865249738163321 0.269500523673358 0.134750261836679 116 0.842605279181801 0.314789441636398 0.157394720818199 117 0.800342220082195 0.39931555983561 0.199657779917805 118 0.818937357586229 0.362125284827542 0.181062642413771 119 0.774151552292186 0.451696895415628 0.225848447707814 120 0.727337472072414 0.545325055855172 0.272662527927586 121 0.66704572179679 0.665908556406421 0.332954278203211 122 0.621859015922598 0.756281968154804 0.378140984077402 123 0.565519320452085 0.868961359095831 0.434480679547915 124 0.501332199145718 0.997335601708565 0.498667800854282 125 0.438203446546686 0.876406893093372 0.561796553453314 126 0.41716359084699 0.83432718169398 0.58283640915301 127 0.421726837149128 0.843453674298256 0.578273162850872 128 0.346024432501256 0.692048865002511 0.653975567498744 129 0.309994553022863 0.619989106045727 0.690005446977137 130 0.260890441305358 0.521780882610716 0.739109558694642 131 0.204484520214074 0.408969040428148 0.795515479785926 132 0.150780454098876 0.301560908197753 0.849219545901124 133 0.141081219639182 0.282162439278363 0.858918780360818 134 0.0979067442508138 0.195813488501628 0.902093255749186 135 0.0718708334502956 0.143741666900591 0.928129166549704 136 0.0509469894092621 0.101893978818524 0.949053010590738 137 0.056307138070233 0.112614276140466 0.943692861929767 138 0.092552016220124 0.185104032440248 0.907447983779876 139 0.0460123624513617 0.0920247249027233 0.953987637548638 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 1 0.008 OK 10% type I error level 8 0.064 OK

Charts produced by software:

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Parameters (Session):

Parameters (R input):

par1 = 10 ; 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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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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