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

Title produced by software: Multiple Regression

Date of computation: Wed, 18 Nov 2009 10:56:09 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn.htm/, Retrieved Wed, 18 Nov 2009 18:58:28 +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/18/t125856709607u38cnus9iypdn.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:

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Dataseries X:

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7.2 2.4 7.5 8.3 8.9 7.4 2 7.2 7.5 8.8 8.8 2.1 7.4 7.2 8.3 9.3 2 8.8 7.4 7.5 9.3 1.8 9.3 8.8 7.2 8.7 2.7 9.3 9.3 7.4 8.2 2.3 8.7 9.3 8.8 8.3 1.9 8.2 8.7 9.3 8.5 2 8.3 8.2 9.3 8.6 2.3 8.5 8.3 8.7 8.5 2.8 8.6 8.5 8.2 8.2 2.4 8.5 8.6 8.3 8.1 2.3 8.2 8.5 8.5 7.9 2.7 8.1 8.2 8.6 8.6 2.7 7.9 8.1 8.5 8.7 2.9 8.6 7.9 8.2 8.7 3 8.7 8.6 8.1 8.5 2.2 8.7 8.7 7.9 8.4 2.3 8.5 8.7 8.6 8.5 2.8 8.4 8.5 8.7 8.7 2.8 8.5 8.4 8.7 8.7 2.8 8.7 8.5 8.5 8.6 2.2 8.7 8.7 8.4 8.5 2.6 8.6 8.7 8.5 8.3 2.8 8.5 8.6 8.7 8 2.5 8.3 8.5 8.7 8.2 2.4 8 8.3 8.6 8.1 2.3 8.2 8 8.5 8.1 1.9 8.1 8.2 8.3 8 1.7 8.1 8.1 8 7.9 2 8 8.1 8.2 7.9 2.1 7.9 8 8.1 8 1.7 7.9 7.9 8.1 8 1.8 8 7.9 8 7.9 1.8 8 8 7.9 8 1.8 7.9 8 7.9 7.7 1.3 8 7.9 8 7.2 1.3 7.7 8 8 7.5 1.3 7.2 7.7 7.9 7.3 1.2 7.5 7.2 8 7 1.4 7.3 7.5 7.7 7 2.2 7 7.3 7.2 7 2.9 7 7 7.5 7.2 3.1 7 7 7.3 7.3 3.5 7.2 7 7 7.1 3.6 7.3 7.2 7 6.8 4.4 7.1 7.3 7 6.4 4.1 6.8 7.1 7.2 6.1 5.1 6.4 6.8 7.3 6.5 5.8 6.1 6.4 7.1 7.7 5.9 6.5 6.1 6.8 7.9 5.4 7.7 6.5 6.4 7.5 5. 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Y(t)[t] = + 1.02127911876007 + 0.0360414799421969`X(t)`[t] + 1.51084525441126`Y(t-1)`[t] -0.906467676335996`Y(t-2)`[t] + 0.274643095535908`Y(t-4) `[t] -0.139705169919118M1[t] -0.114153458576846M2[t] + 0.615649318632783M3[t] -0.411605834814504M4[t] + 0.0603678236443447M5[t] + 0.0912911222967044M6[t] + 0.0221846854736586M7[t] + 0.172456701258676M8[t] + 0.0136298025154064M9[t] -0.0832739587515652M10[t] -0.0109906454536734M11[t] -0.00678674775831158t + 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) 1.02127911876007 0.659961 1.5475 0.129824 0.064912 `X(t)` 0.0360414799421969 0.024169 1.4912 0.143947 0.071974 `Y(t-1)` 1.51084525441126 0.0999 15.1236 0 0 `Y(t-2)` -0.906467676335996 0.111302 -8.1442 0 0 `Y(t-4) ` 0.274643095535908 0.06965 3.9432 0.000324 0.000162 M1 -0.139705169919118 0.102037 -1.3692 0.178784 0.089392 M2 -0.114153458576846 0.10487 -1.0885 0.283045 0.141522 M3 0.615649318632783 0.106117 5.8016 1e-06 0 M4 -0.411605834814504 0.131738 -3.1244 0.003355 0.001678 M5 0.0603678236443447 0.104663 0.5768 0.567402 0.283701 M6 0.0912911222967044 0.108199 0.8437 0.403965 0.201983 M7 0.0221846854736586 0.099983 0.2219 0.825561 0.41278 M8 0.172456701258676 0.10279 1.6778 0.101392 0.050696 M9 0.0136298025154064 0.111607 0.1221 0.903429 0.451714 M10 -0.0832739587515652 0.109081 -0.7634 0.449809 0.224905 M11 -0.0109906454536734 0.105006 -0.1047 0.917176 0.458588 t -0.00678674775831158 0.002399 -2.8285 0.007348 0.003674 Multiple Linear Regression - Regression Statistics Multiple R 0.98592187717783 R-squared 0.972041947897856 Adjusted R-squared 0.960571977804668 F-TEST (value) 84.7466854752484 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.147696720877349 Sum Squared Residuals 0.850758532958944 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.2 7.21326799770914 -0.0132679977091398 2 7.4 7.46207262450806 -0.0620726245080646 3 8.8 8.6254806079687 0.174519392031305 4 9.3 9.30200990324871 -0.00200990324871326 5 9.3 9.16396346963527 0.136036530364729 6 8.7 8.82223213341648 -0.122232133416482 7 8.2 8.20991553796176 -0.00991553796176193 8 8.3 8.26476374037551 0.0352362596244875 9 8.5 8.70707260547728 -0.207072605477277 10 8.6 8.66093096636176 -0.0609309663617573 11 8.5 8.57691771427841 -0.0769177142784081 12 8.2 8.35243803647576 -0.152438036475759 13 8.1 7.89466378122151 0.205336218778488 14 7.9 8.07616542379562 -0.176165423795615 15 8.6 8.56019486044469 0.0398051395553082 16 8.7 8.68985353992184 0.0101464600781625 17 8.7 8.64773744106893 0.0522625589310682 18 8.5 8.49746542126844 0.00253457873155903 19 8.4 8.31525750067419 0.0847424993258116 20 8.5 8.53443682805166 -0.0344368280516576 21 8.7 8.6105544746248 0.089445525375199 22 8.7 8.66345762974099 0.0365423702590125 23 8.6 8.49857146249446 0.101428537505540 24 8.5 8.39357173627917 0.106428263720834 25 8.3 8.24877897588983 0.0512210241101695 26 8 8.04520921224248 -0.0452092122424815 27 8.2 8.46519674308981 -0.26519674308981 28 8.1 7.97419573811945 0.12580426188055 29 8.1 8.0376593770276 0.0623406229723969 30 8 8.06284147090604 -0.0628414709060379 31 7.9 7.9016048239734 -0.00160482397339572 32 7.9 7.9607921726332 -0.0607921726332048 33 8 7.87140870178834 0.128591298211656 34 8 7.89494255664482 0.105057443355185 35 7.9 7.8423280449972 0.057671955002795 36 8 7.69544741725144 0.304552582748558 37 7.7 7.80013036223123 -0.100130362231229 38 7.2 7.27499498185821 -0.0749949818582132 39 7.5 7.48706437745111 0.0129356225488890 40 7.3 7.38337005229626 -0.083370052296258 41 7 7.19926297654141 -0.199262976541412 42 7 6.84295112256509 0.157048877434914 43 7 7.14662020550484 -0.146620205504838 44 7.2 7.2423851504128 -0.042385150412801 45 7.3 7.31096421810958 -0.0109642181095777 46 7.1 7.18066884725244 -0.0806688472524402 47 6.8 6.88218277822993 -0.082182778229927 48 6.4 6.65854280999363 -0.258542809993634 49 6.1 6.24315888294829 -0.143158882948289 50 6.5 6.14155775759563 0.358442242404375 51 7.7 7.66206341104569 0.0379365889543076 52 7.9 7.95057076641374 -0.0505707664137413 53 7.5 7.55137673572678 -0.051376735726782 54 6.9 6.87450985184395 0.0254901481560472 55 6.6 6.52660193188582 0.0733980681141836 56 6.9 6.79762210852682 0.102377891473176 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.141518550155111 0.283037100310222 0.85848144984489 21 0.187330088418773 0.374660176837546 0.812669911581227 22 0.0913923518222707 0.182784703644541 0.90860764817773 23 0.0412536339181306 0.0825072678362611 0.95874636608187 24 0.0513128040527212 0.102625608105442 0.94868719594728 25 0.025754806437852 0.051509612875704 0.974245193562148 26 0.0142921574045855 0.0285843148091711 0.985707842595414 27 0.29278438598914 0.58556877197828 0.70721561401086 28 0.203713673618918 0.407427347237836 0.796286326381082 29 0.163327564149277 0.326655128298555 0.836672435850723 30 0.181587946098373 0.363175892196745 0.818412053901627 31 0.140315433286683 0.280630866573366 0.859684566713317 32 0.124457660262775 0.24891532052555 0.875542339737225 33 0.0895688055319042 0.179137611063808 0.910431194468096 34 0.0598234152168826 0.119646830433765 0.940176584783117 35 0.0323095575509928 0.0646191151019856 0.967690442449007 36 0.199279881873362 0.398559763746724 0.800720118126638 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 0.0588235294117647 NOK 10% type I error level 4 0.235294117647059 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/10bxzm1258566965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/10bxzm1258566965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/1qcwb1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/1qcwb1258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/2ay951258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/2ay951258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/39fuo1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/39fuo1258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/4juhr1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/4juhr1258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/5jj211258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/5jj211258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/67e3n1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/67e3n1258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/7r5it1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/7r5it1258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/8hogv1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/8hogv1258566964.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/9oi7d1258566964.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t125856709607u38cnus9iypdn/9oi7d1258566964.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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