multiple regression trend

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 10 Dec 2008 02:57:09 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/10/t1228903223dnk5tliropuz0nb.htm/, Retrieved Wed, 10 Dec 2008 10:00:33 +0000

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/2008/Dec/10/t1228903223dnk5tliropuz0nb.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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6.4 12.5 6.8 14.8 7.5 15.9 7.5 14.8 7.6 12.9 7.6 14.3 7.4 14.2 7.3 15.9 7.1 15.3 6.9 15.5 6.8 15.1 7.5 15 7.6 12.1 7.8 15.8 8 16.9 8.1 15.1 8.2 13.7 8.3 14.8 8.2 14.7 8 16 7.9 15.4 7.6 15 7.6 15.5 8.2 15.1 8.3 11.7 8.4 16.3 8.4 16.7 8.4 15 8.6 14.9 8.9 14.6 8.8 15.3 8.3 17.9 7.5 16.4 7.2 15.4 7.5 17.9 8.8 15.9 9.3 13.9 9.3 17.8 8.7 17.9 8.2 17.4 8.3 16.7 8.5 16 8.6 16.6 8.6 19.1 8.2 17.8 8.1 17.2 8 18.6 8.6 16.3 8.7 15.1 8.8 19.2 8.5 17.7 8.4 19.1 8.5 18 8.7 17.5 8.7 17.8 8.6 21.1 8.5 17.2 8.3 19.4 8.1 19.8 8.2 17.6 8.1 16.2 8.1 19.5 7.9 19.9 7.9 20 7.9 17.3 8 18.9 8 18.6 7.9 21.4 8 18.6 7.7 19.8 7.2 20.8 7.5 19.6 7.3 17.7 7 19.8 7 22.2 7 20.7 7.2 17.9 7.3 21.2 7.1 21.4 6.8 21.7 6.6 23.2 6.2 21.5 6.2 22.9 6.8 23.2 6.9 18.6

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 9.97086989076777 -0.142539531379827Export[t] + 0.00855033860914675t + 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) 9.97086989076777 0.726316 13.728 0 0 Export -0.142539531379827 0.052939 -2.6925 0.008596 0.004298 t 0.00855033860914675 0.005613 1.5232 0.131544 0.065772 Multiple Linear Regression - Regression Statistics Multiple R 0.320123548276028 R-squared 0.102479086160835 Adjusted R-squared 0.0805883321647575 F-TEST (value) 4.68138677083479 F-TEST (DF numerator) 2 F-TEST (DF denominator) 82 p-value 0.0118803070538149 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.668722269254966 Sum Squared Residuals 36.6695368185959 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.4 8.19767608712908 -1.79767608712908 2 6.8 7.87838550356462 -1.07838550356462 3 7.5 7.73014235765596 -0.230142357655957 4 7.5 7.89548618078291 -0.395486180782913 5 7.6 8.17486162901373 -0.574861629013731 6 7.6 7.98385662369112 -0.38385662369112 7 7.4 8.00666091543825 -0.606660915438249 8 7.3 7.77289405070169 -0.47289405070169 9 7.1 7.86696810813873 -0.766968108138733 10 6.9 7.84701054047191 -0.947010540471914 11 6.8 7.912576691633 -1.11257669163299 12 7.5 7.93538098338012 -0.435380983380121 13 7.6 8.35729596299077 -0.757295962990767 14 7.8 7.83845003549455 -0.0384500354945531 15 8 7.69020688958589 0.30979311041411 16 8.1 7.95532838467873 0.144671615321274 17 8.2 8.16343406721963 0.0365659327803692 18 8.3 8.01519092131097 0.284809078689034 19 8.2 8.0379952130581 0.162004786941903 20 8 7.86124416087347 0.138755839126532 21 7.9 7.95531821831051 -0.0553182183105106 22 7.6 8.02088436947159 -0.420884369471589 23 7.6 7.95816494239082 -0.358164942390822 24 8.2 8.0237310935519 0.1762689064481 25 8.3 8.51691583885246 -0.216915838852457 26 8.4 7.8697843331144 0.5302156668856 27 8.4 7.82131885917162 0.578681140828384 28 8.4 8.07218640112647 0.327813598873531 29 8.6 8.0949906928736 0.505009307126401 30 8.9 8.1463028908967 0.753697109103307 31 8.8 8.05507555753996 0.74492444246004 32 8.3 7.69302311456156 0.606976885438443 33 7.5 7.91538275024045 -0.415382750240445 34 7.2 8.06647262022942 -0.866472620229418 35 7.5 7.718674130389 -0.218674130388998 36 8.8 8.0123035317578 0.787696468242202 37 9.3 8.3059329331266 0.994067066873402 38 9.3 7.75857909935442 1.54142090064558 39 8.7 7.75287548482559 0.947124515174414 40 8.2 7.83269558912465 0.367304410875354 41 8.3 7.94102359969967 0.35897640030033 42 8.5 8.0493516102747 0.450648389725304 43 8.6 7.97237823005595 0.627621769944053 44 8.6 7.62457974021553 0.975420259784474 45 8.2 7.81843146961845 0.381568530381551 46 8.1 7.91250552705549 0.187494472944509 47 8 7.72150052173288 0.278499478267120 48 8.6 8.05789178251563 0.542108217484371 49 8.7 8.23748955878057 0.462510441219431 50 8.8 7.66162781873242 1.13837218126758 51 8.5 7.88398745441131 0.616012545588689 52 8.4 7.6929824490887 0.7070175509113 53 8.5 7.85832627221566 0.641673727784344 54 8.7 7.93814637651472 0.761853623485283 55 8.7 7.90393485570991 0.796065144290084 56 8.6 7.44210474076563 1.15789525923437 57 8.5 8.0065592517561 0.493440748243895 58 8.3 7.70152262132963 0.598477378670368 59 8.1 7.65305714738685 0.446942852613152 60 8.2 7.97519445503161 0.224805544968385 61 8.1 8.18330013757252 -0.0833001375725194 62 8.1 7.72147002262824 0.378529977371763 63 7.9 7.67300454868545 0.226995451314548 64 7.9 7.66730093415662 0.232699065843384 65 7.9 8.0607080074913 -0.160708007491296 66 8 7.84119509589272 0.15880490410728 67 8 7.89250729391581 0.107492706084186 68 7.9 7.50194694466145 0.398053055338555 69 8 7.9096079711341 0.0903920288658921 70 7.7 7.74711087208746 -0.047110872087462 71 7.2 7.61312167931678 -0.413121679316782 72 7.5 7.79271945558172 -0.292719455581721 73 7.3 8.07209490381254 -0.77209490381254 74 7 7.78131222652405 -0.78131222652405 75 7 7.44776768982161 -0.447767689821611 76 7 7.6701273255005 -0.670127325500499 77 7.2 8.07778835197316 -0.877788351973161 78 7.3 7.61595823702888 -0.315958237028879 79 7.1 7.59600066936206 -0.49600066936206 80 6.8 7.56178914855726 -0.761789148557259 81 6.6 7.35653019009666 -0.756530190096665 82 6.2 7.60739773205152 -1.40739773205152 83 6.2 7.4163927267289 -1.21639272672891 84 6.8 7.3821812059241 -0.582181205924105 85 6.9 8.04641338888046 -1.14641338888046 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0526676005862574 0.105335201172515 0.947332399413743 7 0.0805759281450053 0.161151856290011 0.919424071854995 8 0.131563729265621 0.263127458531241 0.86843627073438 9 0.15524586797161 0.31049173594322 0.84475413202839 10 0.194357998456493 0.388715996912985 0.805642001543507 11 0.221155122179742 0.442310244359484 0.778844877820258 12 0.185441936156684 0.370883872313368 0.814558063843316 13 0.17197589624737 0.34395179249474 0.82802410375263 14 0.148553831427125 0.29710766285425 0.851446168572875 15 0.126236045640845 0.252472091281690 0.873763954359155 16 0.109454061877875 0.218908123755751 0.890545938122125 17 0.0935312249279909 0.187062449855982 0.90646877507201 18 0.0727164506305042 0.145432901261008 0.927283549369496 19 0.0504739840210123 0.100947968042025 0.949526015978988 20 0.0380938116454995 0.0761876232909989 0.9619061883545 21 0.0350508890429556 0.0701017780859111 0.964949110957044 22 0.0652166395385898 0.130433279077180 0.93478336046141 23 0.109297771836162 0.218595543672324 0.890702228163838 24 0.0905605932760572 0.181121186552114 0.909439406723943 25 0.0833636910594308 0.166727382118862 0.91663630894057 26 0.0645700673229656 0.129140134645931 0.935429932677034 27 0.0480349389745918 0.0960698779491836 0.951965061025408 28 0.0373666066004979 0.0747332132009959 0.962633393399502 29 0.0276899808959495 0.0553799617918989 0.97231001910405 30 0.0233046119706837 0.0466092239413675 0.976695388029316 31 0.0161145952140103 0.0322291904280207 0.98388540478599 32 0.0143563241704438 0.0287126483408877 0.985643675829556 33 0.157996841619126 0.315993683238252 0.842003158380874 34 0.842745288720052 0.314509422559895 0.157254711279948 35 0.987178036556461 0.025643926887078 0.012821963443539 36 0.985427433648028 0.0291451327039431 0.0145725663519716 37 0.98508361860298 0.0298327627940377 0.0149163813970189 38 0.990394118160623 0.0192117636787537 0.00960588183937687 39 0.985944547082187 0.0281109058356258 0.0140554529178129 40 0.99240526394437 0.0151894721112609 0.00759473605563045 41 0.995682062577705 0.00863587484458988 0.00431793742229494 42 0.996262631546346 0.00747473690730712 0.00373736845365356 43 0.9955606257384 0.0088787485232002 0.0044393742616001 44 0.993368357840622 0.0132632843187550 0.00663164215937752 45 0.99715239187649 0.00569521624702045 0.00284760812351023 46 0.999626477864812 0.000747044270375327 0.000373522135187664 47 0.999989920080111 2.01598397771809e-05 1.00799198885904e-05 48 0.999991868101067 1.62637978663400e-05 8.13189893317001e-06 49 0.999991578447762 1.68431044753529e-05 8.42155223767643e-06 50 0.99998211978695 3.57604261019022e-05 1.78802130509511e-05 51 0.999981613314714 3.6773370572538e-05 1.8386685286269e-05 52 0.999981934609121 3.61307817575095e-05 1.80653908787547e-05 53 0.999975863089178 4.82738216448364e-05 2.41369108224182e-05 54 0.999948747423942 0.000102505152115344 5.12525760576718e-05 55 0.999898599667545 0.000202800664910620 0.000101400332455310 56 0.99985434670309 0.000291306593820994 0.000145653296910497 57 0.99974633171335 0.000507336573299758 0.000253668286649879 58 0.999569397829979 0.000861204340042139 0.000430602170021069 59 0.999424020853166 0.00115195829366882 0.000575979146834409 60 0.999234330555858 0.00153133888828472 0.00076566944414236 61 0.999357381260442 0.00128523747911590 0.000642618739557948 62 0.998951331247558 0.00209733750488401 0.00104866875244200 63 0.998619145235797 0.00276170952840683 0.00138085476420341 64 0.997954488134798 0.00409102373040487 0.00204551186520243 65 0.997777325290894 0.00444534941821269 0.00222267470910634 66 0.99628528528071 0.00742942943858185 0.00371471471929092 67 0.99409857586581 0.0118028482683795 0.00590142413418974 68 0.99340774143784 0.0131845171243213 0.00659225856216063 69 0.993849036759377 0.0123019264812457 0.00615096324062284 70 0.992997214828108 0.0140055703437841 0.00700278517189203 71 0.990124645796592 0.0197507084068151 0.00987535420340756 72 0.985978133214581 0.0280437335708372 0.0140218667854186 73 0.978974813127732 0.0420503737445367 0.0210251868722684 74 0.974339031503334 0.0513219369933316 0.0256609684966658 75 0.954206923720457 0.0915861525590864 0.0457930762795432 76 0.925310613176367 0.149378773647266 0.074689386823633 77 0.887821072334652 0.224357855330697 0.112178927665348 78 0.828060657347897 0.343878685304206 0.171939342652103 79 0.768385591142518 0.463228817714964 0.231614408857482 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 25 0.337837837837838 NOK 5% type I error level 42 0.567567567567568 NOK 10% type I error level 49 0.662162162162162 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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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