Workshop 7: model 3

*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, 20 Nov 2009 04:03:27 -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/20/t1258715128503zh6cn1rom4mz.htm/, Retrieved Fri, 20 Nov 2009 12:05:40 +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/20/t1258715128503zh6cn1rom4mz.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:

WS7M3

Dataseries X:

» Textbox « » Textfile « » CSV «

267413 21.4 267366 26.4 264777 26.4 258863 29.4 254844 34.4 254868 24.4 277267 26.4 285351 25.4 286602 31.4 283042 27.4 276687 27.4 277915 29.4 277128 32.4 277103 26.4 275037 22.4 270150 19.4 267140 21.4 264993 23.4 287259 23.4 291186 25.4 292300 28.4 288186 27.4 281477 21.4 282656 17.4 280190 24.4 280408 26.4 276836 22.4 275216 14.4 274352 18.4 271311 25.4 289802 29.4 290726 26.4 292300 26.4 278506 20.4 269826 26.4 265861 29.4 269034 33.4 264176 32.4 255198 35.4 253353 34.4 246057 36.4 235372 32.4 258556 34.4 260993 31.4 254663 27.4 250643 27.4 243422 30.4 247105 32.4 248541 32.4 245039 27.4 237080 31.4 237085 29.4 225554 27.4 226839 25.4 247934 26.4 248333 23.4 246969 18.4 245098 22.4 246263 17.4 255765 17.4 264319 11.4 268347 9.4 273046 6.4 273963 0 267430 7.8 271993 7.9 292710 12 295881 16.9 293299 12.3

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 Y[t] = + 318115.398162939 -1319.53689420027X[t] + 194.849655232781M1[t] -1514.42482052588M2[t] -5277.09751585129M3[t] -10799.9026082240M4[t] -11679.6684389217M5[t] -14336.1172997772M6[t] + 10431.3901533681M7[t] + 13434.4813255061M8[t] + 11894.5216074274M9[t] + 1914.98881954373M10[t] -3644.97403732826M11[t] -527.851900808109t + 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) 318115.398162939 9097.359536 34.9679 0 0 X -1319.53689420027 216.989243 -6.0811 0 0 M1 194.849655232781 7453.373696 0.0261 0.979238 0.489619 M2 -1514.42482052588 7453.866897 -0.2032 0.83975 0.419875 M3 -5277.09751585129 7455.5949 -0.7078 0.482056 0.241028 M4 -10799.9026082240 7505.211576 -1.439 0.155821 0.077911 M5 -11679.6684389217 7447.882485 -1.5682 0.122574 0.061287 M6 -14336.1172997772 7457.223342 -1.9224 0.059735 0.029867 M7 10431.3901533681 7444.595128 1.4012 0.166771 0.083385 M8 13434.4813255061 7445.459617 1.8044 0.076647 0.038324 M9 11894.5216074274 7448.727847 1.5969 0.116029 0.058014 M10 1914.98881954373 7777.213697 0.2462 0.80642 0.40321 M11 -3644.97403732826 7777.061605 -0.4687 0.641149 0.320574 t -527.851900808109 82.610068 -6.3897 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.772218481857322 R-squared 0.596321383722027 Adjusted R-squared 0.500906438056324 F-TEST (value) 6.24976914844461 F-TEST (DF numerator) 13 F-TEST (DF denominator) 55 p-value 5.13881478925171e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12293.2695635889 Sum Squared Residuals 8311846210.9683 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 267413 289544.306381479 -22131.3063814788 2 267366 280709.495533909 -13343.4955339095 3 264777 276418.970937776 -11641.9709377760 4 258863 266409.703261994 -7546.70326199439 5 254844 258404.401059487 -3560.40105948723 6 254868 268415.469239826 -13547.4692398262 7 277267 290016.051003763 -12749.0510037630 8 285351 293810.827169293 -8459.82716929313 9 286602 283825.794185205 2776.20581479537 10 283042 278596.557073314 4445.44292668605 11 276687 272508.742315634 4178.25768436615 12 277915 272986.790663753 4928.20933624652 13 277128 268695.177735577 8432.82226442265 14 277103 274375.272724212 2727.72727578787 15 275037 275362.895704880 -325.895704879723 16 270150 273270.849394300 -3120.84939429975 17 267140 269224.157874393 -2084.15787439338 18 264993 263400.783324329 1592.21667567078 19 287259 287640.438876666 -381.438876666466 20 291186 287476.604359596 3709.39564040421 21 292300 281450.182058108 10849.8179418919 22 288186 272262.334263617 15923.6657363833 23 281477 274091.740871138 7385.25912886184 24 282656 282487.010584459 168.989415540628 25 280190 272917.250079482 7272.74992051782 26 280408 268041.049914515 12366.9500854851 27 276836 269028.672895182 7807.32710481758 28 275216 273534.311055604 1681.68894439621 29 274352 266848.545747297 7503.45425270312 30 271311 254427.486726231 16883.5132737686 31 289802 273388.994701768 16413.0052982324 32 290726 279822.844655698 10903.1553443018 33 292300 277755.033036811 14544.9669631886 34 278506 275164.869713321 3341.13028667877 35 269826 261159.833590440 8666.16640956048 36 265861 260318.345044359 5542.65495564112 37 269034 254707.195221982 14326.8047780175 38 264176 253789.605739616 10386.3942603840 39 255198 245540.470460882 9657.52953911834 40 253353 240809.350361901 12543.6496380988 41 246057 236762.658841995 9294.3411580052 42 235372 238856.505657132 -3484.50565713222 43 258556 260457.087421069 -1901.08742106894 44 260993 266890.937375000 -5897.93737499959 45 254663 270101.273332914 -15438.2733329138 46 250643 259593.888644222 -8950.88864422206 47 243422 249547.463203941 -6125.46320394116 48 247105 250025.511552061 -2920.51155206077 49 248541 249692.509306485 -1151.50930648545 50 245039 254053.06740092 -9014.06740092001 51 237080 244484.395227985 -7404.39522798543 52 237085 241072.812023205 -3987.81202320519 53 225554 242304.2680801 -16750.2680800999 54 226839 241759.041106837 -14920.0411068368 55 247934 264679.159764974 -16745.1597649738 56 248333 271113.009718904 -22780.0097189044 57 246969 275642.882571019 -28673.8825710189 58 245098 259857.350305526 -14759.3503055261 59 246263 260367.220018847 -14104.2200188473 60 255765 263484.342155367 -7719.34215536748 61 264319 271068.561274994 -6749.56127499374 62 268347 271470.508686827 -3123.50868682751 63 273046 271138.594773295 1907.40522670521 64 273963 273532.973902996 430.02609700428 65 267430 261832.968396728 5597.0316032722 66 271993 258516.713945644 13476.2860543558 67 292710 277346.268231760 15363.7317682397 68 295881 273355.776721509 22525.2232784912 69 293299 277357.834815943 15941.1651840568 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 7.47843944043301e-05 0.000149568788808660 0.999925215605596 18 2.63406380100667e-06 5.26812760201334e-06 0.9999973659362 19 1.35071106191922e-07 2.70142212383843e-07 0.999999864928894 20 5.2095395548097e-06 1.04190791096194e-05 0.999994790460445 21 4.28294247068315e-06 8.5658849413663e-06 0.99999571705753 22 2.06593507308570e-06 4.13187014617140e-06 0.999997934064927 23 1.20956361379888e-06 2.41912722759776e-06 0.999998790436386 24 5.98639773421184e-07 1.19727954684237e-06 0.999999401360227 25 2.96352652829377e-07 5.92705305658754e-07 0.999999703647347 26 8.10486011580455e-08 1.62097202316091e-07 0.999999918951399 27 3.09777816844045e-08 6.1955563368809e-08 0.999999969022218 28 7.12420044664198e-09 1.42484008932840e-08 0.9999999928758 29 1.84553433437688e-09 3.69106866875375e-09 0.999999998154466 30 3.20955429555239e-10 6.41910859110477e-10 0.999999999679045 31 6.80088749581383e-11 1.36017749916277e-10 0.999999999931991 32 2.22432607875999e-10 4.44865215751997e-10 0.999999999777567 33 4.63296993283939e-10 9.26593986567878e-10 0.999999999536703 34 1.53892232041231e-07 3.07784464082462e-07 0.999999846107768 35 2.38665836057452e-06 4.77331672114905e-06 0.99999761334164 36 1.82018466656365e-05 3.64036933312730e-05 0.999981798153334 37 1.90571377646235e-05 3.81142755292471e-05 0.999980942862235 38 3.81477690367156e-05 7.62955380734312e-05 0.999961852230963 39 6.23706668748733e-05 0.000124741333749747 0.999937629333125 40 7.33823314770971e-05 0.000146764662954194 0.999926617668523 41 0.000218251845381548 0.000436503690763096 0.999781748154619 42 0.00104019193423560 0.00208038386847119 0.998959808065764 43 0.00218664766722636 0.00437329533445273 0.997813352332774 44 0.00553775721935601 0.0110755144387120 0.994462242780644 45 0.0443351251374992 0.0886702502749984 0.95566487486250 46 0.195935199010415 0.391870398020829 0.804064800989585 47 0.485459801699326 0.970919603398652 0.514540198300674 48 0.813392907517131 0.373214184965737 0.186607092482869 49 0.950197352722526 0.0996052945549473 0.0498026472774736 50 0.999866800355509 0.000266399288982584 0.000133199644491292 51 0.999809340425946 0.000381319148107593 0.000190659574053796 52 0.999186138913845 0.00162772217230963 0.000813861086154814 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.833333333333333 NOK 5% type I error level 31 0.861111111111111 NOK 10% type I error level 33 0.916666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/10469u1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/10469u1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/13vcg1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/13vcg1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/2iehl1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/2iehl1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/3leyy1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/3leyy1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/4046s1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/4046s1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/5f02p1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/5f02p1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/6j5b91258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/6j5b91258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/7p9v11258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/7p9v11258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/8pjxg1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/8pjxg1258715003.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/9h8mq1258715003.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715128503zh6cn1rom4mz/9h8mq1258715003.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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