*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: Fri, 20 Nov 2009 07:54:37 -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/20/t1258729048l2adxdeogtacrid.htm/, Retrieved Fri, 20 Nov 2009 15:57:40 +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/20/t1258729048l2adxdeogtacrid.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

555 0 562 0 561 0 555 0 544 0 537 0 543 0 594 0 611 0 613 0 611 0 594 0 595 0 591 0 589 0 584 0 573 0 567 0 569 0 621 0 629 0 628 0 612 0 595 0 597 0 593 0 590 0 580 0 574 0 573 0 573 0 620 0 626 0 620 0 588 0 566 0 557 0 561 0 549 0 532 0 526 0 511 0 499 0 555 0 565 0 542 0 527 0 510 0 514 0 517 0 508 0 493 0 490 1 469 1 478 1 528 1 534 1 518 1 506 1 502 1 516 1 528 1 533 1 536 1 537 1 524 1 536 1 587 1 597 1 581 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 593.286943257603 -9.6360562361279X[t] -0.908505841360937t + 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) 593.286943257603 9.459284 62.7201 0 0 X -9.6360562361279 14.287427 -0.6744 0.50235 0.251175 t -0.908505841360937 0.309051 -2.9397 0.004505 0.002252 Multiple Linear Regression - Regression Statistics Multiple R 0.545245856416598 R-squared 0.297293043939469 Adjusted R-squared 0.276316716892886 F-TEST (value) 14.1727883665838 F-TEST (DF numerator) 2 F-TEST (DF denominator) 67 p-value 7.36101093157249e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 34.1323536075516 Sum Squared Residuals 78056.1767069929 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 555 592.378437416241 -37.378437416241 2 562 591.469931574881 -29.4699315748814 3 561 590.56142573352 -29.5614257335206 4 555 589.65291989216 -34.6529198921596 5 544 588.744414050799 -44.7444140507986 6 537 587.835908209438 -50.8359082094377 7 543 586.927402368077 -43.9274023680768 8 594 586.018896526716 7.98110347328416 9 611 585.110390685355 25.8896093146451 10 613 584.201884843994 28.7981151560060 11 611 583.293379002633 27.706620997367 12 594 582.384873161272 11.6151268387279 13 595 581.476367319911 13.5236326800889 14 591 580.56786147855 10.4321385214498 15 589 579.659355637189 9.34064436281073 16 584 578.750849795828 5.24915020417167 17 573 577.842343954467 -4.84234395446740 18 567 576.933838113107 -9.93383811310646 19 569 576.025332271745 -7.02533227174552 20 621 575.116826430385 45.8831735696154 21 629 574.208320589024 54.7916794109764 22 628 573.299814747663 54.7001852523373 23 612 572.391308906302 39.6086910936982 24 595 571.482803064941 23.5171969350592 25 597 570.57429722358 26.4257027764201 26 593 569.665791382219 23.3342086177810 27 590 568.757285540858 21.2427144591420 28 580 567.848779699497 12.1512203005029 29 574 566.940273858136 7.05972614186386 30 573 566.031768016775 6.96823198322479 31 573 565.123262175414 7.87673782458573 32 620 564.214756334053 55.7852436659467 33 626 563.306250492692 62.6937495073076 34 620 562.397744651331 57.6022553486685 35 588 561.489238809971 26.5107611900295 36 566 560.58073296861 5.41926703139042 37 557 559.672227127249 -2.67222712724864 38 561 558.763721285888 2.23627871411230 39 549 557.855215444527 -8.85521544452677 40 532 556.946709603166 -24.9467096031658 41 526 556.038203761805 -30.0382037618049 42 511 555.129697920444 -44.1296979204440 43 499 554.221192079083 -55.221192079083 44 555 553.312686237722 1.68731376227792 45 565 552.404180396361 12.5958196036389 46 542 551.495674555 -9.4956745550002 47 527 550.587168713639 -23.5871687136393 48 510 549.678662872278 -39.6786628722783 49 514 548.770157030917 -34.7701570309174 50 517 547.861651189556 -30.8616511895565 51 508 546.953145348196 -38.9531453481955 52 493 546.044639506835 -53.0446395068346 53 490 535.500077429346 -45.5000774293457 54 469 534.591571587985 -65.5915715879848 55 478 533.683065746624 -55.6830657466239 56 528 532.774559905263 -4.77455990526293 57 534 531.866054063902 2.133945936098 58 518 530.957548222541 -12.9575482225411 59 506 530.04904238118 -24.0490423811801 60 502 529.140536539819 -27.1405365398192 61 516 528.232030698458 -12.2320306984582 62 528 527.323524857097 0.67647514290269 63 533 526.415019015736 6.58498098426363 64 536 525.506513174375 10.4934868256246 65 537 524.598007333014 12.4019926669855 66 524 523.689501491654 0.310498508346438 67 536 522.780995650293 13.2190043497074 68 587 521.872489808932 65.1275101910683 69 597 520.963983967571 76.0360160324293 70 581 520.05547812621 60.9445218737902 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.0160919545360308 0.0321839090720616 0.98390804546397 7 0.00411491257883576 0.00822982515767152 0.995885087421164 8 0.169376886645288 0.338753773290575 0.830623113354713 9 0.267625555729587 0.535251111459174 0.732374444270413 10 0.226444627526901 0.452889255053802 0.773555372473099 11 0.151209105813716 0.302418211627433 0.848790894186284 12 0.104693231549389 0.209386463098777 0.895306768450611 13 0.0713069100456086 0.142613820091217 0.928693089954391 14 0.05297288064834 0.10594576129668 0.94702711935166 15 0.0406134837802216 0.0812269675604431 0.959386516219778 16 0.0347471786946376 0.0694943573892753 0.965252821305362 17 0.0403480892628224 0.0806961785256448 0.959651910737178 18 0.049346727218647 0.098693454437294 0.950653272781353 19 0.0487597526982384 0.097519505396477 0.951240247301762 20 0.0423320084870189 0.0846640169740378 0.95766799151298 21 0.0394285527388641 0.0788571054777283 0.960571447261136 22 0.0323019313228372 0.0646038626456743 0.967698068677163 23 0.0212548590048332 0.0425097180096664 0.978745140995167 24 0.0163400602228707 0.0326801204457415 0.98365993977713 25 0.0119994858015662 0.0239989716031325 0.988000514198434 26 0.00937800380726781 0.0187560076145356 0.990621996192732 27 0.00763612754115744 0.0152722550823149 0.992363872458843 28 0.00759880133056122 0.0151976026611224 0.99240119866944 29 0.00810713768811853 0.0162142753762371 0.991892862311881 30 0.00790431291293472 0.0158086258258694 0.992095687087065 31 0.0070517594599582 0.0141035189199164 0.992948240540042 32 0.00908017871514482 0.0181603574302896 0.990919821284855 33 0.019374750593415 0.03874950118683 0.980625249406585 34 0.0496912113104161 0.0993824226208322 0.950308788689584 35 0.0873866165077682 0.174773233015536 0.912613383492232 36 0.156630647890311 0.313261295780621 0.84336935210969 37 0.261107022236684 0.522214044473367 0.738892977763316 38 0.403962421999393 0.807924843998786 0.596037578000607 39 0.560584784897276 0.878830430205448 0.439415215102724 40 0.688395207906021 0.623209584187959 0.311604792093979 41 0.76946950268729 0.46106099462542 0.23053049731271 42 0.824446492844575 0.35110701431085 0.175553507155425 43 0.86713887528284 0.265722249434318 0.132861124717159 44 0.909662864708872 0.180674270582255 0.0903371352911276 45 0.976089476318352 0.0478210473632961 0.0239105236816481 46 0.989938982941284 0.0201220341174327 0.0100610170587163 47 0.99331074179871 0.0133785164025816 0.0066892582012908 48 0.9924701445154 0.0150597109692016 0.00752985548460082 49 0.991080866424564 0.0178382671508711 0.00891913357543553 50 0.989999116844723 0.0200017663105539 0.0100008831552770 51 0.987082432669886 0.0258351346602278 0.0129175673301139 52 0.981549240049883 0.0369015199002339 0.0184507599501169 53 0.969929166385356 0.060141667229287 0.0300708336146435 54 0.960290094377664 0.0794198112446729 0.0397099056223365 55 0.948303477204121 0.103393045591757 0.0516965227958785 56 0.956370354806724 0.0872592903865516 0.0436296451932758 57 0.981452169216789 0.0370956615664224 0.0185478307832112 58 0.981981406585232 0.0360371868295356 0.0180185934147678 59 0.966690987022884 0.0666180259542311 0.0333090129771155 60 0.933828631176138 0.132342737647724 0.0661713688238621 61 0.88127170061532 0.237456598769361 0.118728299384681 62 0.822824994789116 0.354350010421767 0.177175005210884 63 0.755231873861692 0.489536252276617 0.244768126138308 64 0.671221587746919 0.657556824506162 0.328778412253081 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0169491525423729 NOK 5% type I error level 23 0.389830508474576 NOK 10% type I error level 36 0.610169491525424 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/10ll3j1258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/10ll3j1258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/15bww1258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/15bww1258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/2xlo71258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/2xlo71258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/3ke411258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/3ke411258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/4rxsm1258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/4rxsm1258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/5p4051258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/5p4051258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/67qky1258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/67qky1258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/73ema1258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/73ema1258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/8jpc31258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/8jpc31258728872.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/9tkor1258728872.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729048l2adxdeogtacrid/9tkor1258728872.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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