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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: Mon, 15 Dec 2008 10:48:10 -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/15/t1229363343cvt6eq4xittcnow.htm/, Retrieved Mon, 15 Dec 2008 18:49:03 +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/15/t1229363343cvt6eq4xittcnow.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 «

467 101.0 0 460 98.7 0 448 105.1 0 443 98.4 0 436 101.7 0 431 102.9 0 484 92.2 0 510 94.9 0 513 92.8 0 503 98.5 0 471 94.3 0 471 87.4 0 476 103.4 0 475 101.2 0 470 109.6 0 461 111.9 0 455 108.9 0 456 105.6 0 517 107.8 0 525 97.5 0 523 102.4 0 519 105.6 0 509 99.8 0 512 96.2 0 519 113.1 0 517 107.4 0 510 116.8 0 509 112.9 0 501 105.3 0 507 109.3 0 569 107.9 0 580 101.1 0 578 114.7 0 565 116.2 0 547 108.4 0 555 113.4 0 562 108.7 0 561 112.6 0 555 124.2 1 544 114.9 1 537 110.5 1 543 121.5 1 594 118.1 1 611 111.7 1 613 132.7 1 611 119.0 1 594 116.7 1 595 120.1 1 591 113.4 1 589 106.6 1 584 116.3 1 573 112.6 1 567 111.6 1 569 125.1 1 621 110.7 1 629 109.6 1 628 114.2 1 612 113.4 1 595 116.0 1 597 109.6 1 593 117.8 1 590 115.8 1 580 125.3 1 574 113.0 1 573 120.5 1 573 116.6 1 620 111.8 1 626 115.2 1 620 118.6 1 588 122.4 1 566 116.4 1 557 114.5 1 561 119.8 1 549 115.8 1 532 127.8 1 526 118.8 1 511 119.7 1 499 118.6 1 555 120.8 1 565 115.9 1 542 109.7 1 527 114.8 1 510 116.2 1 514 112.2 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 253.121341592206 + 2.55465072343757X[t] + 53.4799373483157DUM[t] -9.20020519368495M1[t] -5.90393501861316M2[t] -46.5270026487257M3[t] -37.6544050449706M4[t] -42.9022535423097M5[t] -50.6721768385552M6[t] + 15.2829630654453M7[t] + 36.4342329706287M8[t] + 18.3110549776420M9[t] + 3.74216053983409M10[t] -6.86671897504944M11[t] -0.325723201120734t + 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) 253.121341592206 65.85215 3.8438 0.000266 0.000133 X 2.55465072343757 0.670181 3.8119 0.000296 0.000148 DUM 53.4799373483157 13.671596 3.9118 0.000212 0.000106 M1 -9.20020519368495 16.768037 -0.5487 0.584999 0.2925 M2 -5.90393501861316 16.380997 -0.3604 0.719639 0.359819 M3 -46.5270026487257 18.213644 -2.5545 0.012844 0.006422 M4 -37.6544050449706 16.759942 -2.2467 0.027862 0.013931 M5 -42.9022535423097 16.612471 -2.5825 0.011932 0.005966 M6 -50.6721768385552 17.064713 -2.9694 0.004101 0.002051 M7 15.2829630654453 16.376617 0.9332 0.353961 0.176981 M8 36.4342329706287 16.215228 2.2469 0.027847 0.013924 M9 18.3110549776420 16.552902 1.1062 0.272476 0.136238 M10 3.74216053983409 16.613546 0.2252 0.822452 0.411226 M11 -6.86671897504944 16.248522 -0.4226 0.673897 0.336949 t -0.325723201120734 0.3025 -1.0768 0.285334 0.142667 Multiple Linear Regression - Regression Statistics Multiple R 0.84668065124361 R-squared 0.716868125190303 Adjusted R-squared 0.659421078127466 F-TEST (value) 12.4787636935659 F-TEST (DF numerator) 14 F-TEST (DF denominator) 69 p-value 7.4940054162198e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 30.2632645239872 Sum Squared Residuals 63194.6973957687 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 467 501.615136264594 -34.6151362645943 2 460 498.709986574639 -38.7099865746391 3 448 474.110960373406 -26.1109603734063 4 443 465.541674929009 -22.5416749290089 5 436 468.398450617893 -32.3984506178931 6 431 463.368384988652 -32.3683849886519 7 484 501.66303895075 -17.6630389507497 8 510 529.386142608094 -19.3861426080938 9 513 505.572474894767 7.42752510523255 10 503 505.239366379433 -2.23936637943296 11 471 483.575230624991 -12.5752306249909 12 471 472.4891364072 -1.48913640720043 13 476 503.837619587396 -27.8376195873958 14 475 501.187934969784 -26.1879349697842 15 470 481.698210215426 -11.6982102154265 16 461 496.120781281967 -35.1207812819672 17 455 482.883257413195 -27.8832574131948 18 456 466.357263528485 -10.3572635284845 19 517 537.606911822927 -20.6069118229269 20 525 532.119556075583 -7.1195560755827 21 523 526.188443426319 -3.18844342631929 22 519 519.468708102391 -0.468708102390854 23 509 493.717131190449 15.2828688095513 24 512 491.061384360002 20.9386156399978 25 519 524.709053191291 -5.70905319129133 26 517 513.118091041648 3.8819089583517 27 510 496.183017010728 13.8169829892718 28 509 494.766753591956 14.233246408044 29 501 469.777836395371 31.2221636046293 30 507 471.900792791755 35.0992072082453 31 569 533.953698481822 35.0463015181781 32 580 537.407620266509 42.5923797334909 33 578 553.701968911153 24.2980310888475 34 565 542.63932735738 22.3606726426197 35 547 511.778448998563 35.221551001437 36 555 531.09269838968 23.9073016103205 37 562 509.559911594717 52.4400884052827 38 561 522.493596390075 38.5064036099252 39 555 564.658691299033 -9.65869129903305 40 544 549.447313973698 -5.44731397369801 41 537 532.633279092113 4.36672090788706 42 543 552.63879055256 -9.6387905525599 43 594 609.582394795752 -15.5823947957519 44 611 614.058176869814 -3.05817686981421 45 613 649.256940867896 -36.2569408678955 46 611 599.363608317872 11.6363916821277 47 594 582.553308937962 11.4466910620384 48 595 597.780117171578 -2.78011717157805 49 591 571.138028929741 19.8619710702593 50 589 556.736950984316 32.2630490156837 51 584 540.568272170427 43.4317278295725 52 573 539.662938896343 33.3370611036572 53 567 531.534716474445 35.4652835255546 54 569 557.926854743486 11.0731452565137 55 621 586.769301028865 34.2306989711349 56 629 604.784731937146 24.2152680628535 57 628 598.087224070852 29.9127759291482 58 612 581.148885853173 30.8511141468269 59 595 576.856375018107 18.1436249818935 60 597 567.047606162035 29.9523938379652 61 593 578.469813699417 14.5301863005828 62 590 576.331059226493 13.6689407735069 63 580 559.651450267917 20.3485497320833 64 574 536.776120772269 37.223879227731 65 573 550.362429499591 22.6375705004090 66 573 532.303645180818 40.6963548191818 67 620 585.670738411198 34.3292615888024 68 626 615.182097574948 10.8179024250519 69 620 605.419008840528 14.5809911594717 70 588 600.232063950662 -12.2320639506624 71 566 573.969556894033 -7.96955689403277 72 557 575.65671629343 -18.6567162934301 73 561 579.670436732843 -18.6704367328435 74 549 572.422380813044 -23.4223808130443 75 532 562.129398663062 -30.1293986630618 76 526 547.684416554758 -21.6844165547581 77 511 544.410030507392 -33.4100305073921 78 499 533.504268214244 -34.5042682142445 79 555 604.753916508687 -49.7539165086869 80 565 613.061674667906 -48.0616746679056 81 542 578.773938988485 -36.7739389884852 82 527 576.908040039088 -49.9080400390881 83 510 569.549948335896 -59.5499483358965 84 514 565.872341216075 -51.8723412160749 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.00967598015466901 0.0193519603093380 0.990324019845331 19 0.00380059598491008 0.00760119196982017 0.99619940401509 20 0.000789985824991917 0.00157997164998383 0.999210014175008 21 0.000537014432259587 0.00107402886451917 0.99946298556774 22 0.000130985585662821 0.000261971171325642 0.999869014414337 23 0.000424450048017922 0.000848900096035845 0.999575549951982 24 0.00063641790245432 0.00127283580490864 0.999363582097546 25 0.000558694747028158 0.00111738949405632 0.999441305252972 26 0.000600900794643855 0.00120180158928771 0.999399099205356 27 0.000427078619024558 0.000854157238049115 0.999572921380975 28 0.000462180439369458 0.000924360878738916 0.99953781956063 29 0.000333877096111228 0.000667754192222456 0.999666122903889 30 0.000366183436415885 0.000732366872831771 0.999633816563584 31 0.000528583253963281 0.00105716650792656 0.999471416746037 32 0.000334123680248789 0.000668247360497578 0.999665876319751 33 0.000230402347663499 0.000460804695326998 0.999769597652337 34 0.00010087470558313 0.00020174941116626 0.999899125294417 35 4.62554859074422e-05 9.25109718148844e-05 0.999953744514093 36 2.56100480702303e-05 5.12200961404606e-05 0.99997438995193 37 1.14095850272061e-05 2.28191700544121e-05 0.999988590414973 38 5.01363189749721e-06 1.00272637949944e-05 0.999994986368102 39 3.61680307387211e-06 7.23360614774421e-06 0.999996383196926 40 3.83896471529836e-06 7.67792943059672e-06 0.999996161035285 41 6.10068560174656e-06 1.22013712034931e-05 0.999993899314398 42 7.70585537914073e-06 1.54117107582815e-05 0.99999229414462 43 1.59447630791287e-05 3.18895261582575e-05 0.99998405523692 44 3.27373904633927e-05 6.54747809267854e-05 0.999967262609537 45 5.67377840534884e-05 0.000113475568106977 0.999943262215947 46 5.37529317313165e-05 0.000107505863462633 0.999946247068269 47 5.89241949227195e-05 0.000117848389845439 0.999941075805077 48 0.000170060204726355 0.000340120409452710 0.999829939795274 49 0.000264920696035604 0.000529841392071208 0.999735079303964 50 0.0002228050445942 0.0004456100891884 0.999777194955406 51 0.000131875599552576 0.000263751199105152 0.999868124400447 52 0.000233497218100179 0.000466994436200358 0.9997665027819 53 0.000326474246874868 0.000652948493749737 0.999673525753125 54 0.00244293457522062 0.00488586915044125 0.99755706542478 55 0.00430047996895114 0.00860095993790229 0.995699520031049 56 0.0180688954622730 0.0361377909245459 0.981931104537727 57 0.0539660705557037 0.107932141111407 0.946033929444296 58 0.0908417837758875 0.181683567551775 0.909158216224113 59 0.199552190125132 0.399104380250265 0.800447809874868 60 0.285420595153271 0.570841190306542 0.714579404846729 61 0.541589211559672 0.916821576880656 0.458410788440328 62 0.676164640291493 0.647670719417014 0.323835359708507 63 0.68713720557409 0.62572558885182 0.31286279442591 64 0.660617080084221 0.678765839831558 0.339382919915779 65 0.535945930847381 0.928108138305238 0.464054069152619 66 0.486186271846423 0.972372543692846 0.513813728153577 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 37 0.755102040816326 NOK 5% type I error level 39 0.795918367346939 NOK 10% type I error level 39 0.795918367346939 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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