*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 06:48:55 -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/t1258724979wft5nws4pqo9opm.htm/, Retrieved Fri, 20 Nov 2009 14:49:51 +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/t1258724979wft5nws4pqo9opm.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

22 0 22 0 20 0 21 0 20 0 21 0 21 0 21 0 19 0 21 0 21 0 22 0 19 0 24 0 22 0 22 0 22 0 24 0 22 0 23 0 24 0 21 0 20 0 22 0 23 0 23 0 22 0 20 0 21 1 21 1 20 1 20 1 17 1 18 1 19 1 19 1 20 1 21 1 20 1 21 1 19 1 22 1 20 1 18 1 16 1 17 1 18 1 19 1 18 1 20 1 21 1 18 1 19 1 19 1 19 1 21 1 19 1 19 1 17 1 16 1 16 1 17 1 16 1 15 1 16 1 16 1 16 1 18 1 19 1 16 1 16 1 16 1

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 21.5714285714286 -3.18506493506494X[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) 21.5714285714286 0.320466 67.3127 0 0 X -3.18506493506494 0.409941 -7.7696 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.680478106956758 R-squared 0.463050454047453 Adjusted R-squared 0.455379746248131 F-TEST (value) 60.3660660999726 F-TEST (DF numerator) 1 F-TEST (DF denominator) 70 p-value 4.85240736480819e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.69574661617556 Sum Squared Residuals 201.288961038961 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 22 21.5714285714285 0.428571428571511 2 22 21.5714285714286 0.428571428571439 3 20 21.5714285714286 -1.57142857142857 4 21 21.5714285714286 -0.571428571428575 5 20 21.5714285714286 -1.57142857142857 6 21 21.5714285714286 -0.571428571428575 7 21 21.5714285714286 -0.571428571428575 8 21 21.5714285714286 -0.571428571428575 9 19 21.5714285714286 -2.57142857142858 10 21 21.5714285714286 -0.571428571428575 11 21 21.5714285714286 -0.571428571428575 12 22 21.5714285714286 0.428571428571425 13 19 21.5714285714286 -2.57142857142858 14 24 21.5714285714286 2.42857142857142 15 22 21.5714285714286 0.428571428571425 16 22 21.5714285714286 0.428571428571425 17 22 21.5714285714286 0.428571428571425 18 24 21.5714285714286 2.42857142857142 19 22 21.5714285714286 0.428571428571425 20 23 21.5714285714286 1.42857142857143 21 24 21.5714285714286 2.42857142857142 22 21 21.5714285714286 -0.571428571428575 23 20 21.5714285714286 -1.57142857142857 24 22 21.5714285714286 0.428571428571425 25 23 21.5714285714286 1.42857142857143 26 23 21.5714285714286 1.42857142857143 27 22 21.5714285714286 0.428571428571425 28 20 21.5714285714286 -1.57142857142857 29 21 18.3863636363636 2.61363636363636 30 21 18.3863636363636 2.61363636363636 31 20 18.3863636363636 1.61363636363636 32 20 18.3863636363636 1.61363636363636 33 17 18.3863636363636 -1.38636363636364 34 18 18.3863636363636 -0.386363636363637 35 19 18.3863636363636 0.613636363636363 36 19 18.3863636363636 0.613636363636363 37 20 18.3863636363636 1.61363636363636 38 21 18.3863636363636 2.61363636363636 39 20 18.3863636363636 1.61363636363636 40 21 18.3863636363636 2.61363636363636 41 19 18.3863636363636 0.613636363636363 42 22 18.3863636363636 3.61363636363636 43 20 18.3863636363636 1.61363636363636 44 18 18.3863636363636 -0.386363636363637 45 16 18.3863636363636 -2.38636363636364 46 17 18.3863636363636 -1.38636363636364 47 18 18.3863636363636 -0.386363636363637 48 19 18.3863636363636 0.613636363636363 49 18 18.3863636363636 -0.386363636363637 50 20 18.3863636363636 1.61363636363636 51 21 18.3863636363636 2.61363636363636 52 18 18.3863636363636 -0.386363636363637 53 19 18.3863636363636 0.613636363636363 54 19 18.3863636363636 0.613636363636363 55 19 18.3863636363636 0.613636363636363 56 21 18.3863636363636 2.61363636363636 57 19 18.3863636363636 0.613636363636363 58 19 18.3863636363636 0.613636363636363 59 17 18.3863636363636 -1.38636363636364 60 16 18.3863636363636 -2.38636363636364 61 16 18.3863636363636 -2.38636363636364 62 17 18.3863636363636 -1.38636363636364 63 16 18.3863636363636 -2.38636363636364 64 15 18.3863636363636 -3.38636363636364 65 16 18.3863636363636 -2.38636363636364 66 16 18.3863636363636 -2.38636363636364 67 16 18.3863636363636 -2.38636363636364 68 18 18.3863636363636 -0.386363636363637 69 19 18.3863636363636 0.613636363636363 70 16 18.3863636363636 -2.38636363636364 71 16 18.3863636363636 -2.38636363636364 72 16 18.3863636363636 -2.38636363636364 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.277476792983213 0.554953585966426 0.722523207016787 6 0.142201363355241 0.284402726710483 0.857798636644759 7 0.066559588035858 0.133119176071716 0.933440411964142 8 0.0287925079534631 0.0575850159069261 0.971207492046537 9 0.0755786837960871 0.151157367592174 0.924421316203913 10 0.0402566834339167 0.0805133668678333 0.959743316566083 11 0.0204192747150891 0.0408385494301782 0.97958072528491 12 0.0171998160001288 0.0343996320002576 0.982800183999871 13 0.0366614930150881 0.0733229860301762 0.963338506984912 14 0.169890235134315 0.339780470268630 0.830109764865685 15 0.133101588991073 0.266203177982146 0.866898411008927 16 0.101307015786341 0.202614031572682 0.898692984213659 17 0.0749425116538495 0.149885023307699 0.92505748834615 18 0.150956792388904 0.301913584777808 0.849043207611096 19 0.111965168280726 0.223930336561452 0.888034831719274 20 0.108009288037406 0.216018576074812 0.891990711962594 21 0.164056766056922 0.328113532113844 0.835943233943078 22 0.124439557540613 0.248879115081227 0.875560442459387 23 0.122046495628497 0.244092991256994 0.877953504371503 24 0.0895288970714947 0.179057794142989 0.910471102928505 25 0.081930430607418 0.163860861214836 0.918069569392582 26 0.0766217939026166 0.153243587805233 0.923378206097383 27 0.0578325923685681 0.115665184737136 0.942167407631432 28 0.0522708413755597 0.104541682751119 0.94772915862444 29 0.0464042644194176 0.0928085288388352 0.953595735580582 30 0.0424877921066112 0.0849755842132223 0.95751220789339 31 0.0354737344910246 0.0709474689820491 0.964526265508975 32 0.0286885708994212 0.0573771417988423 0.971311429100579 33 0.0581075713001036 0.116215142600207 0.941892428699896 34 0.0519085228968702 0.103817045793740 0.94809147710313 35 0.037612952732919 0.075225905465838 0.962387047267081 36 0.026644715449061 0.053289430898122 0.97335528455094 37 0.0219725998853213 0.0439451997706426 0.978027400114679 38 0.0292766159353804 0.0585532318707608 0.97072338406462 39 0.0250851895059591 0.0501703790119183 0.974914810494041 40 0.0355194738198749 0.0710389476397498 0.964480526180125 41 0.0273895184099397 0.0547790368198794 0.97261048159006 42 0.0918616968917439 0.183723393783488 0.908138303108256 43 0.0959181235556707 0.191836247111341 0.90408187644433 44 0.0881971422600426 0.176394284520085 0.911802857739957 45 0.164355155484949 0.328710310969899 0.83564484451505 46 0.169856488917796 0.339712977835592 0.830143511082204 47 0.140490665150458 0.280981330300917 0.859509334849542 48 0.117785023771229 0.235570047542458 0.882214976228771 49 0.0937688537682373 0.187537707536475 0.906231146231763 50 0.106768774594108 0.213537549188216 0.893231225405892 51 0.223520957077580 0.447041914155159 0.77647904292242 52 0.188367145479248 0.376734290958496 0.811632854520752 53 0.179460102160319 0.358920204320637 0.820539897839681 54 0.176783770327925 0.35356754065585 0.823216229672075 55 0.182105894312442 0.364211788624883 0.817894105687558 56 0.578678545855752 0.842642908288496 0.421321454144248 57 0.69659310819282 0.606813783614361 0.303406891807181 58 0.853581963438107 0.292836073123785 0.146418036561893 59 0.826660866026335 0.346678267947329 0.173339133973665 60 0.805322548037741 0.389354903924518 0.194677451962259 61 0.772347944555013 0.455304110889975 0.227652055444987 62 0.711661883889078 0.576676232221843 0.288338116110922 63 0.649237078851546 0.701525842296907 0.350762921148454 64 0.702892119239515 0.594215761520971 0.297107880760485 65 0.622037542280022 0.755924915439957 0.377962457719978 66 0.526899933387857 0.946200133224286 0.473100066612143 67 0.421746462975193 0.843492925950386 0.578253537024807 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 3 0.0476190476190476 OK 10% type I error level 16 0.253968253968254 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/10sanr1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/10sanr1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/190ya1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/190ya1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/2qplq1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/2qplq1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/3xyjt1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/3xyjt1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/40dop1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/40dop1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/53lsz1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/53lsz1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/6skx01258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/6skx01258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/7n0h01258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/7n0h01258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/8mrho1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/8mrho1258724928.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/9zmlt1258724928.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724979wft5nws4pqo9opm/9zmlt1258724928.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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