ws7TimDamen

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 28 Jan 2011 13:26:07 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83.htm/, Retrieved Fri, 28 Jan 2011 14:25:38 +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/2011/Jan/28/t1296221130ly5aki24camij83.htm/}, year = {2011}, } @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 = {2011}, 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 «

6654,00 5712,00 3,30 38,60 645,00 3,00 5,00 3,00 1,00 6,60 8,30 4,50 42,00 3,00 1,00 3,00 3,39 44,50 12,50 14,00 60,00 1,00 1,00 1,00 0,92 5,70 16,50 25,00 5,00 2,00 3,00 2547,00 4603,00 3,90 69,00 624,00 3,00 5,00 4,00 10,55 179,50 9,80 27,00 180,00 4,00 4,00 4,00 0,02 0,30 19,70 19,00 35,00 1,00 1,00 1,00 160,00 169,00 6,20 30,40 392,00 4,00 5,00 4,00 3,30 25,60 14,50 28,00 63,00 1,00 2,00 1,00 52,16 440,00 9,70 50,00 230,00 1,00 1,00 1,00 0,43 6,40 12,50 7,00 112,00 5,00 4,00 4,00 465,00 423,00 3,90 30,00 281,00 5,00 5,00 5,00 0,55 2,40 10,30 2,00 1,00 2,00 187,10 419,00 3,10 40,00 365,00 5,00 5,00 5,00 0,08 1,20 8,40 3,50 42,00 1,00 1,00 1,00 3,00 25,00 8,60 50,00 28,00 2,00 2,00 2,00 0,79 3,50 10,70 6,00 42,00 2,00 2,00 2,00 0,20 5,00 10,70 10,40 120,00 2,00 2,00 2,00 1,41 17,50 6,10 34,00 1,00 2,00 1,00 60,00 81,00 18,10 7,00 1,00 1,00 1,00 529,00 680,00 28,00 400,00 5,00 5,00 5,00 27,66 115,00 3,80 20,00 148,00 5,00 5,00 5,00 0,12 1,00 14,40 3,90 16,00 3,00 1,00 2,00 207,00 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 4 seconds R Server 'Gwilym Jenkins' @ www.wessa.org 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 Y[t] = + 52.800717586225 -0.0530935358697208G[t] + 0.141950777249531H[t] + 0.431528586556607J[t] -0.818834514415419Z[t] + 0.95259716088965P[t] -0.0594488639737405B[t] -0.0226698680966238D[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) 52.800717586225 28.461767 1.8551 0.069038 0.034519 G -0.0530935358697208 0.050659 -1.0481 0.299278 0.149639 H 0.141950777249531 0.077015 1.8431 0.070798 0.035399 J 0.431528586556607 0.241703 1.7854 0.079819 0.039909 Z -0.818834514415419 0.269448 -3.0389 0.003655 0.001827 P 0.95259716088965 0.31567 3.0177 0.003881 0.00194 B -0.0594488639737405 0.31613 -0.1881 0.851541 0.42577 D -0.0226698680966238 0.071648 -0.3164 0.752913 0.376457 Multiple Linear Regression - Regression Statistics Multiple R 0.408875273496465 R-squared 0.167178989276809 Adjusted R-squared 0.0592207101089881 F-TEST (value) 1.54855181617826 F-TEST (DF numerator) 7 F-TEST (DF denominator) 54 p-value 0.171002963389419 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 182.895543423538 Sum Squared Residuals 1806342.10942632 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.3 1.34044876007522 1.95955123992478 2 8.3 23.9656612286646 -15.6656612286646 3 12.5 16.7193478629171 -4.21934786291705 4 16.5 4.24172674314297 12.258273256857 5 69 80.068137717384 -11.0681377173841 6 27 122.633492827485 -95.6334928274848 7 19 67.1318552538017 -48.1318552538017 8 30.4 215.042252499552 -184.642252499552 9 28 80.5305609146148 -52.5305609146148 10 50 130.132624990107 -80.1326249901066 11 7 91.5034373172743 -84.5034373172743 12 30 152.514393252062 -122.514393252062 13 2 206.180823869891 -204.180823869891 14 5 100.366922452312 -95.366922452312 15 1 59.3661259449969 -58.3661259449969 16 2 53.647963844569 -51.647963844569 17 2 57.3201846834447 -55.3201846834447 18 2 68.6725469093306 -66.6725469093306 19 2 78.3646023829961 -76.3646023829961 20 1 -272.859447111505 273.859447111505 21 27.66 109.899382101551 -82.2393821015514 22 0.12 44.5812528716985 -44.4612528716985 23 207 240.839402407335 -33.8394024073354 24 85 208.504976758037 -123.504976758037 25 36.33 105.370330869949 -69.0403308699494 26 0.1 50.1112297911574 -50.0112297911574 27 1.04 51.9163069220226 -50.8763069220226 28 521 357.21714595565 163.78285404435 29 100 127.522199265282 -27.5221992652822 30 35 94.8521834667575 -59.8521834667575 31 0.14 74.5259582537691 -74.3859582537691 32 0.25 89.0728927894371 -88.8228927894371 33 1320 237.378672716204 1082.6213272838 34 3 34.5969013183716 -31.5969013183716 35 11.2 71.6149226319296 -60.4149226319296 36 3.2 60.3500525940483 -57.1500525940483 37 2 65.0063980004226 -63.0063980004226 38 5 59.5745511573128 -54.5745511573128 39 6.5 100.068693553696 -93.5686935536956 40 440 32.5814611080996 407.4185388919 41 140 51.5305957986466 88.4694042013534 42 170 56.9958250218699 113.00417497813 43 17 40.3733550891803 -23.3733550891803 44 115 58.0256402388765 56.9743597611235 45 31 56.5926075847301 -25.5926075847301 46 63 55.1526380709374 7.84736192906255 47 21 54.7059697584223 -33.7059697584223 48 52 49.7746511204609 2.22534887953914 49 164 56.3959141081895 107.60408589181 50 225 53.8844550159586 171.115544984041 51 225 52.7726176697146 172.227382330285 52 150 50.6356549039081 99.3643450960919 53 151 57.8978320992938 93.1021679007062 54 90 55.1238092288537 34.8761907711463 55 3 51.9458025976498 -48.9458025976498 56 2 60.9242588746894 -58.9242588746894 57 3 78.3561502006467 -75.3561502006467 58 3 58.7435747037409 -55.7435747037409 59 4 82.0178277023031 -78.0178277023031 60 2 56.7941214610438 -54.7941214610438 61 1 43.2794300633246 -42.2794300633246 62 5 25.1486918117129 -20.1486918117129 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 9.34980396202463e-05 0.000186996079240493 0.99990650196038 12 4.9928729711795e-06 9.985745942359e-06 0.999995007127029 13 3.30321599403568e-07 6.60643198807136e-07 0.9999996696784 14 1.08860532501465e-07 2.17721065002931e-07 0.999999891139468 15 1.86049433880525e-08 3.72098867761051e-08 0.999999981395057 16 1.39983197095132e-09 2.79966394190265e-09 0.999999998600168 17 8.69367004262015e-11 1.73873400852403e-10 0.999999999913063 18 4.412419711157e-12 8.824839422314e-12 0.999999999995588 19 3.20723346722299e-13 6.41446693444597e-13 0.99999999999968 20 2.84434219671068e-11 5.68868439342136e-11 0.999999999971557 21 4.51459447273108e-12 9.02918894546216e-12 0.999999999995485 22 5.02717353043964e-13 1.00543470608793e-12 0.999999999999497 23 7.78612100103067e-07 1.55722420020613e-06 0.9999992213879 24 5.54497019631512e-07 1.10899403926302e-06 0.99999944550298 25 1.48037081216945e-07 2.9607416243389e-07 0.999999851962919 26 3.51756018233107e-08 7.03512036466215e-08 0.999999964824398 27 8.66248803434673e-09 1.73249760686935e-08 0.999999991337512 28 0.00155849559116876 0.00311699118233751 0.998441504408831 29 0.00163148801436101 0.00326297602872202 0.998368511985639 30 0.0009401831530096 0.0018803663060192 0.99905981684699 31 0.000969369374183677 0.00193873874836735 0.999030630625816 32 0.00274540276917166 0.00549080553834331 0.997254597230828 33 0.857034941651878 0.285930116696244 0.142965058348122 34 0.867746408997268 0.264507182005465 0.132253591002733 35 0.816910850332655 0.366178299334691 0.183089149667345 36 0.79611116171582 0.40777767656836 0.20388883828418 37 0.790175839400286 0.419648321199428 0.209824160599714 38 0.790108959718256 0.419782080563487 0.209891040281744 39 0.902043075544243 0.195913848911514 0.0979569244557572 40 0.996872528972364 0.00625494205527127 0.00312747102763563 41 0.99495122421458 0.0100975515708384 0.0050487757854192 42 0.992408158238599 0.0151836835228022 0.0075918417614011 43 0.987799712606006 0.0244005747879873 0.0122002873939936 44 0.976325372412277 0.0473492551754456 0.0236746275877228 45 0.981748660643617 0.0365026787127659 0.0182513393563829 46 0.964850295606266 0.0702994087874686 0.0351497043937343 47 0.95961811972842 0.080763760543162 0.040381880271581 48 0.942610673110816 0.114778653778367 0.0573893268891836 49 0.911787688857485 0.17642462228503 0.088212311142515 50 0.959832272053537 0.0803354558929268 0.0401677279464634 51 0.98568778328918 0.0286244334216423 0.0143122167108212 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.560975609756098 NOK 5% type I error level 29 0.707317073170732 NOK 10% type I error level 32 0.780487804878049 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/106hrc1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/106hrc1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/1ay9r1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/1ay9r1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/2215z1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/2215z1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/36pj21296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/36pj21296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/4escd1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/4escd1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/56m6l1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/56m6l1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/678zf1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/678zf1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/72fpo1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/72fpo1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/856ab1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/856ab1296221163.ps (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/9w0yj1296221163.png (open in new window) http://www.freestatistics.org/blog/date/2011/Jan/28/t1296221130ly5aki24camij83/9w0yj1296221163.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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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