Dummie

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Sat, 13 Dec 2008 08:08:13 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/13/t12291809465bzzk7q9b6ct3mo.htm/, Retrieved Sat, 13 Dec 2008 16:09:07 +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/2008/Dec/13/t12291809465bzzk7q9b6ct3mo.htm/}, year = {2008}, } @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 = {2008}, 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 «

3353 0 3480 0 3098 0 2944 0 3389 0 3497 0 4404 0 3849 0 3734 0 3060 0 3507 0 3287 0 3215 0 3764 0 2734 0 2837 0 2766 0 3851 0 3289 0 3848 0 3348 0 3682 0 4058 0 3655 1 3811 1 3341 1 3032 1 3475 1 3353 1 3186 1 3902 1 4164 1 3499 1 4145 1 3796 1 3711 1 3949 1 3740 1 3243 1 4407 1 4814 1 3908 1 5250 1 3937 1 4004 1 5560 1 3922 1 3759 1 4138 1 4634 1 3996 1 4308 1 4142 1 4429 1 5219 1 4929 1 5754 1 5592 1 4163 1 4962 1 5208 1 4755 1 4491 1 5732 1 5730 1 5024 1 6056 1 4901 1 5353 1 5578 1 4618 1 4724 1 5011 1 5298 1 4143 1 4617 1 4727 1 4207 1 5112 1 4190 1 4098 1 5071 1 4177 1 4598 1 3757 1 5591 1 4218 1 3780 1 4336 1 4870 1 4422 1 4727 1 4459 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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation y[t] = + 3094.41801385681 + 305.913106235566d[t] + 65.9727623584073M1[t] + 320.622776448917M2[t] -400.852209460574M3[t] -23.2021953700650M4[t] + 105.947818720444M5[t] + 54.8478328109534M6[t] + 624.622846901462M7[t] + 220.522860991972M8[t] + 168.047875082481M9[t] + 644.937558424062M10[t] -5.82314177114272M11[t] + 15.4749859094908t + 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) 3094.41801385681 240.053563 12.8905 0 0 d 305.913106235566 202.337807 1.5119 0.134551 0.067276 M1 65.9727623584073 287.102221 0.2298 0.81885 0.409425 M2 320.622776448917 287.120822 1.1167 0.267515 0.133757 M3 -400.852209460574 287.176313 -1.3958 0.166673 0.083337 M4 -23.2021953700650 287.268672 -0.0808 0.935831 0.467915 M5 105.947818720444 287.397864 0.3686 0.713378 0.356689 M6 54.8478328109534 287.563839 0.1907 0.849224 0.424612 M7 624.622846901462 287.766534 2.1706 0.032965 0.016483 M8 220.522860991972 288.00587 0.7657 0.446143 0.223072 M9 168.047875082481 288.281756 0.5829 0.561601 0.280801 M10 644.937558424062 297.152623 2.1704 0.032981 0.01649 M11 -5.82314177114272 297.336955 -0.0196 0.984424 0.492212 t 15.4749859094908 3.254763 4.7546 9e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.75683865096162 R-squared 0.572804743589406 Adjusted R-squared 0.502506790002853 F-TEST (value) 8.14824208053436 F-TEST (DF numerator) 13 F-TEST (DF denominator) 79 p-value 4.29967950132948e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 554.044938250393 Sum Squared Residuals 24250297.6944696 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3353 3175.86576212471 177.134237875288 2 3480 3445.99076212471 34.0092378752888 3 3098 2739.99076212471 358.009237875289 4 2944 3133.11576212471 -189.115762124711 5 3389 3277.74076212471 111.259237875288 6 3497 3242.11576212471 254.884237875289 7 4404 3827.36576212471 576.634237875289 8 3849 3438.74076212471 410.259237875289 9 3734 3401.74076212471 332.259237875289 10 3060 3894.10543137578 -834.105431375783 11 3507 3258.81971709007 248.180282909931 12 3287 3280.1178447707 6.8821552292973 13 3215 3361.5655930386 -146.565593038601 14 3764 3631.6905930386 132.309406961399 15 2734 2925.6905930386 -191.690593038601 16 2837 3318.8155930386 -481.815593038601 17 2766 3463.4405930386 -697.440593038601 18 3851 3427.8155930386 423.184406961399 19 3289 4013.0655930386 -724.065593038601 20 3848 3624.4405930386 223.559406961399 21 3348 3587.4405930386 -239.440593038601 22 3682 4079.80526228967 -397.805262289673 23 4058 3444.51954800396 613.480451996041 24 3655 3771.73078192016 -116.730781920158 25 3811 3853.17853018806 -42.1785301880565 26 3341 4123.30353018806 -782.303530188057 27 3032 3417.30353018806 -385.303530188057 28 3475 3810.42853018806 -335.428530188057 29 3353 3955.05353018806 -602.053530188057 30 3186 3919.42853018806 -733.428530188057 31 3902 4504.67853018806 -602.678530188057 32 4164 4116.05353018806 47.9464698119434 33 3499 4079.05353018806 -580.053530188056 34 4145 4571.41819943913 -426.418199439129 35 3796 3936.13248515341 -140.132485153415 36 3711 3957.43061283405 -246.430612834048 37 3949 4038.87836110195 -89.8783611019463 38 3740 4309.00336110195 -569.003361101946 39 3243 3603.00336110195 -360.003361101947 40 4407 3996.12836110195 410.871638898054 41 4814 4140.75336110195 673.246638898054 42 3908 4105.12836110195 -197.128361101946 43 5250 4690.37836110195 559.621638898054 44 3937 4301.75336110195 -364.753361101946 45 4004 4264.75336110195 -260.753361101947 46 5560 4757.11803035302 802.881969646981 47 3922 4121.83231606730 -199.832316067304 48 3759 4143.13044374794 -384.130443747938 49 4138 4224.57819201584 -86.578192015836 50 4634 4494.70319201584 139.296807984164 51 3996 3788.70319201584 207.296807984164 52 4308 4181.82819201584 126.171807984164 53 4142 4326.45319201584 -184.453192015836 54 4429 4290.82819201584 138.171807984164 55 5219 4876.07819201584 342.921807984164 56 4929 4487.45319201584 441.546807984164 57 5754 4450.45319201584 1303.54680798416 58 5592 4942.81786126691 649.182138733091 59 4163 4307.53214698119 -144.532146981194 60 4962 4328.83027466183 633.169725338172 61 5208 4410.27802292973 797.721977070274 62 4755 4680.40302292973 74.596977070274 63 4491 3974.40302292973 516.596977070274 64 5732 4367.52802292973 1364.47197707027 65 5730 4512.15302292973 1217.84697707027 66 5024 4476.52802292973 547.471977070274 67 6056 5061.77802292973 994.221977070273 68 4901 4673.15302292973 227.846977070274 69 5353 4636.15302292973 716.846977070274 70 5578 5128.5176921808 449.482307819201 71 4618 4493.23197789508 124.768022104916 72 4724 4514.53010557572 209.469894424282 73 5011 4595.97785384362 415.022146156384 74 5298 4866.10285384362 431.897146156384 75 4143 4160.10285384362 -17.1028538436159 76 4617 4553.22785384362 63.7721461563842 77 4727 4697.85285384362 29.1471461563841 78 4207 4662.22785384362 -455.227853843616 79 5112 5247.47785384362 -135.477853843616 80 4190 4858.85285384362 -668.852853843616 81 4098 4821.85285384362 -723.852853843616 82 5071 5314.21752309469 -243.217523094688 83 4177 4678.93180880897 -501.931808808974 84 4598 4700.22993648961 -102.229936489608 85 3757 4781.67768475751 -1024.67768475751 86 5591 5051.80268475751 539.197315242494 87 4218 4345.80268475751 -127.802684757506 88 3780 4738.92768475751 -958.927684757506 89 4336 4883.55268475751 -547.552684757506 90 4870 4847.92768475751 22.0723152424942 91 4422 5433.17768475751 -1011.17768475751 92 4727 5044.55268475751 -317.552684757506 93 4459 5007.55268475751 -548.552684757506 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.116397292252283 0.232794584504565 0.883602707747717 18 0.0925450899770606 0.185090179954121 0.90745491002294 19 0.194856577264266 0.389713154528532 0.805143422735734 20 0.117412243349216 0.234824486698431 0.882587756650784 21 0.0651366089485215 0.130273217897043 0.934863391051479 22 0.0951553937523966 0.190310787504793 0.904844606247603 23 0.0902076873841132 0.180415374768226 0.909792312615887 24 0.0522784337332254 0.104556867466451 0.947721566266775 25 0.0297381945884449 0.0594763891768897 0.970261805411555 26 0.0330908306247959 0.0661816612495917 0.966909169375204 27 0.0190798096942524 0.0381596193885049 0.980920190305748 28 0.0142950705987572 0.0285901411975144 0.985704929401243 29 0.00924988166634427 0.0184997633326885 0.990750118333656 30 0.0142327120952746 0.0284654241905492 0.985767287904725 31 0.0100379774251119 0.0200759548502238 0.989962022574888 32 0.00575978823895546 0.0115195764779109 0.994240211761044 33 0.00425598762744627 0.00851197525489253 0.995744012372554 34 0.00678178241076486 0.0135635648215297 0.993218217589235 35 0.00400760835860705 0.00801521671721409 0.995992391641393 36 0.00271034849127801 0.00542069698255602 0.997289651508722 37 0.00222003661686421 0.00444007323372841 0.997779963383136 38 0.00227890865845581 0.00455781731691162 0.997721091341544 39 0.00191327831391631 0.00382655662783262 0.998086721686084 40 0.0105703611434686 0.0211407222869371 0.989429638856531 41 0.0479155184482779 0.0958310368965559 0.952084481551722 42 0.0393585147487271 0.0787170294974542 0.960641485251273 43 0.0571761555029041 0.114352311005808 0.942823844497096 44 0.0592485673562426 0.118497134712485 0.940751432643757 45 0.0633143991613052 0.126628798322610 0.936685600838695 46 0.154358581135639 0.308717162271278 0.84564141886436 47 0.136846303965713 0.273692607931427 0.863153696034287 48 0.173562031680948 0.347124063361897 0.826437968319052 49 0.164065113331061 0.328130226662122 0.835934886668939 50 0.182294037589747 0.364588075179494 0.817705962410253 51 0.171092579819797 0.342185159639594 0.828907420180203 52 0.174285275283066 0.348570550566133 0.825714724716934 53 0.271475447866313 0.542950895732627 0.728524552133687 54 0.297119023350654 0.594238046701308 0.702880976649346 55 0.292702098833269 0.585404197666538 0.707297901166731 56 0.254948587980026 0.509897175960051 0.745051412019974 57 0.373884256707675 0.74776851341535 0.626115743292325 58 0.345126200294639 0.690252400589279 0.654873799705361 59 0.376300968427669 0.752601936855339 0.62369903157233 60 0.330566002409302 0.661132004818604 0.669433997590698 61 0.281980799701355 0.563961599402711 0.718019200298645 62 0.479573822558843 0.959147645117686 0.520426177441157 63 0.428410733242071 0.856821466484143 0.571589266757929 64 0.563670793725448 0.872658412549105 0.436329206274552 65 0.59972355012372 0.80055289975256 0.40027644987628 66 0.513055226559705 0.97388954688059 0.486944773440295 67 0.572776005482357 0.854447989035286 0.427223994517643 68 0.495049273196619 0.990098546393238 0.504950726803381 69 0.524482830712949 0.951034338574101 0.475517169287051 70 0.444725829121731 0.889451658243461 0.555274170878269 71 0.372151619946615 0.74430323989323 0.627848380053385 72 0.281336111224211 0.562672222448422 0.718663888775789 73 0.484577014103198 0.969154028206397 0.515422985896802 74 0.381250609726280 0.762501219452559 0.61874939027372 75 0.273681610818481 0.547363221636961 0.726318389181519 76 0.335950941972858 0.671901883945717 0.664049058027142 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 6 0.1 NOK 5% type I error level 14 0.233333333333333 NOK 10% type I error level 18 0.3 NOK

Charts produced by software:

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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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