*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: Wed, 29 Dec 2010 14:32:24 +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/29/t1293633010kln0dx03mezrvwa.htm/, Retrieved Wed, 29 Dec 2010 15:30:13 +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/29/t1293633010kln0dx03mezrvwa.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 «

9 2 3 3 2 14 9 2 5 4 1 18 9 4 3 2 2 11 9 3 3 2 2 12 9 3 4 4 1 16 9 2 5 4 1 18 9 4 4 4 2 14 9 3 4 4 3 14 9 2 4 3 2 15 9 2 4 3 2 15 9 2 4 5 2 17 9 1 5 4 1 19 9 2 2 2 4 10 9 1 4 3 2 16 9 2 5 5 2 18 9 3 4 4 3 14 9 2 4 3 3 14 9 2 4 4 1 17 9 3 4 2 1 14 9 2 5 3 2 16 9 1 4 4 1 18 9 3 3 2 3 11 9 4 3 5 2 14 9 3 3 3 3 12 9 2 5 4 2 17 9 4 2 3 4 9 9 2 4 4 2 16 9 4 4 4 2 14 9 3 4 4 2 15 9 4 3 2 2 11 9 2 4 4 2 16 9 3 3 4 3 13 9 1 4 4 2 17 9 2 4 3 2 15 9 3 4 4 3 14 9 2 4 4 2 16 9 4 2 3 4 9 9 2 4 3 2 15 9 2 5 4 2 17 9 2 3 4 4 13 9 2 4 4 3 15 9 2 4 4 2 16 9 2 5 4 3 16 9 3 3 4 4 12 9 2 4 2 12 9 4 3 3 3 11 9 2 4 4 3 15 9 2 4 3 2 15 9 3 5 4 1 17 9 4 4 3 2 13 9 2 3 4 1 16 9 2 3 3 2 14 9 4 4 2 3 11 9 2 3 3 4 12 9 3 4 4 5 12 9 2 4 4 3 15 9 2 4 4 2 16 9 2 3 4 2 15 9 3 3 3 3 12 9 4 3 3 2 12 9 5 3 2 4 8 9 3 4 3 3 13 9 5 4 2 2 11 9 3 4 3 2 14 9 3 4 4 2 15 10 4 3 2 3 10

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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation PPS [t] = + 12.3626548087475 -0.271598260496139month[t] -0.701955619751661IDT[t] + 1.63777243794683HPP[t] + 0.346154125445987TGYW[t] -0.462987710989521POP[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) 12.3626548087475 1.681136 7.3538 0 0 month -0.271598260496139 0.129694 -2.0941 0.04048 0.02024 IDT -0.701955619751661 0.14758 -4.7564 1.3e-05 6e-06 HPP 1.63777243794683 0.145915 11.2242 0 0 TGYW 0.346154125445987 0.139134 2.4879 0.015644 0.007822 POP -0.462987710989521 0.074714 -6.1968 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.96924657672412 R-squared 0.939438926491427 Adjusted R-squared 0.934392170365712 F-TEST (value) 186.147082024581 F-TEST (DF numerator) 5 F-TEST (DF denominator) 60 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.85137331720911 Sum Squared Residuals 43.4901915153386 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14 13.5401634929783 0.459836507021687 2 18 17.6248502053075 0.375149794692543 3 11 11.790098128029 -0.790098128028981 4 12 12.4920537477806 -0.492053747780642 5 16 15.285122147609 0.714877852391034 6 18 17.6248502053075 0.375149794692546 7 14 14.1201788168678 -0.120178816867783 8 14 14.3591467256299 -0.359146725629923 9 15 15.1779359309251 -0.177935930925119 10 15 15.1779359309251 -0.177935930925119 11 17 15.8702441818171 1.12975581818291 12 19 18.3268058250591 0.673194174940885 13 10 10.6302615076064 -0.630261507606435 14 16 15.8798915506768 0.120108449323221 15 18 17.5080166197639 0.49198338023608 16 14 14.3591467256299 -0.359146725629923 17 14 14.7149482199356 -0.714948219935597 18 17 15.9870777673606 1.01292223263937 19 14 14.592813896717 -0.592813896716992 20 16 16.8157083688719 -0.815708368871946 21 18 16.6890333871123 1.31096661288771 22 11 12.0290660367911 -1.02906603679112 23 14 12.8285605043669 1.17143949563306 24 12 12.3752201622371 -0.375220162237109 25 17 17.1618624943179 -0.161862494317932 26 9 9.5725043935491 -0.5725043935491 27 16 15.5240900563711 0.475909943628894 28 14 14.1201788168678 -0.120178816867783 29 15 14.8221344366194 0.177865563380556 30 11 11.790098128029 -0.790098128028982 31 16 15.5240900563711 0.475909943628894 32 13 12.7213742876831 0.278625712316904 33 17 16.2260456761228 0.773954323877234 34 15 15.1779359309251 -0.177935930925119 35 14 14.3591467256299 -0.359146725629923 36 16 15.5240900563711 0.475909943628894 37 9 9.5725043935491 -0.5725043935491 38 15 15.1779359309251 -0.177935930925119 39 17 17.1618624943179 -0.161862494317932 40 13 12.9603421964452 0.0396578035547641 41 15 15.0611023453816 -0.0611023453815844 42 16 15.5240900563711 0.475909943628894 43 16 16.6988747833284 -0.698874783328411 44 12 12.2583865766936 -0.258386576693575 45 9 10.2019046955839 -1.20190469558392 46 9 10.0293097768016 -1.02930977680163 47 9 9.656372272031 -0.65637227203099 48 9 7.67244570863818 1.32755429136182 49 9 7.06453471891217 1.93546528108783 50 9 8.05522460962494 0.944775390375058 51 9 9.20303192990116 -0.203031929901157 52 9 8.83738903937936 0.162610960620641 53 9 7.68958171910314 1.31041828089686 54 9 10.4556727122504 -1.45567271225038 55 9 11.4660453953954 -2.46604539539539 56 9 9.656372272031 -0.65637227203099 57 9 8.84723043559548 0.152769564404518 58 9 10.0121737663367 -1.01217376633666 59 9 9.83792032630825 -0.83792032630825 60 9 9.22016794036612 -0.220167940366124 61 9 9.85505633677322 -0.855056336773217 62 9 8.67297699556707 0.327023004432932 63 9 7.07182933316102 1.92817066683898 64 9 7.86383515913156 1.13616484086844 65 10 9.03861988608887 0.961380113911135 66 9 8.85452504984433 0.145474950155673 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 6.30843726323881e-46 1.26168745264776e-45 1 10 1.56600253812756e-60 3.13200507625511e-60 1 11 5.42945350840109e-74 1.08589070168022e-73 1 12 6.47433700157358e-88 1.29486740031472e-87 1 13 8.1717521610506e-106 1.63435043221012e-105 1 14 6.40892672985088e-120 1.28178534597018e-119 1 15 1.42331680694578e-134 2.84663361389156e-134 1 16 1.2965924827121e-151 2.59318496542419e-151 1 17 2.54473335424014e-160 5.08946670848027e-160 1 18 5.79938652423535e-173 1.15987730484707e-172 1 19 2.41345175625646e-185 4.82690351251293e-185 1 20 2.91676658846652e-210 5.83353317693305e-210 1 21 4.73730234919608e-226 9.47460469839216e-226 1 22 3.89345701237538e-233 7.78691402475076e-233 1 23 3.68778405845594e-248 7.37556811691189e-248 1 24 1.7457408714983e-265 3.4914817429966e-265 1 25 1.7710601557267e-288 3.5421203114534e-288 1 26 3.05440004426428e-290 6.10880008852856e-290 1 27 8.33938508840224e-311 1.66787701768045e-310 1 28 2.47032822920623e-323 4.94065645841247e-323 1 29 0 0 1 30 0 0 1 31 0 0 1 32 0 0 1 33 0 0 1 34 0 0 1 35 0 0 1 36 0 0 1 37 0 0 1 38 0 0 1 39 0 0 1 40 0 0 1 41 0 0 1 42 0 0 1 43 0 0 1 44 0 0 1 45 0.891494390341428 0.217011219317144 0.108505609658572 46 0.847135275450116 0.305729449099768 0.152864724549884 47 0.863314290724818 0.273371418550364 0.136685709275182 48 0.98063125802567 0.0387374839486599 0.0193687419743299 49 0.998463049817375 0.0030739003652497 0.00153695018262485 50 0.99723684231633 0.00552631536733801 0.002763157683669 51 0.994500825987723 0.0109983480245538 0.00549917401227688 52 0.98748209393324 0.0250358121335211 0.0125179060667606 53 0.973720720052559 0.052558559894882 0.026279279947441 54 0.981639888306056 0.0367202233878885 0.0183601116939443 55 0.98277223820777 0.0344555235844611 0.0172277617922306 56 0.96282266112346 0.0743546777530781 0.0371773388765391 57 0.924217256887076 0.151565486225849 0.0757827431129245 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 38 0.775510204081633 NOK 5% type I error level 43 0.877551020408163 NOK 10% type I error level 45 0.918367346938776 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/10wskd1293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/10wskd1293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/1iim51293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/1iim51293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/2iim51293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/2iim51293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/3iim51293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/3iim51293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/4t9481293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/4t9481293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/5t9481293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/5t9481293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/6t9481293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/6t9481293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/7mj3t1293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/7mj3t1293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/8wskd1293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/8wskd1293633135.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/9wskd1293633135.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293633010kln0dx03mezrvwa/9wskd1293633135.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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