*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 08:03:46 -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/t1258729613lojh5ltn31z7gfy.htm/, Retrieved Fri, 20 Nov 2009 16:07:05 +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/t1258729613lojh5ltn31z7gfy.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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 562.196907216495 -43.9845360824743X[t] + 0.80051546391773M1[t] + 3.8005154639175M2[t] + 0.133848797250830M3[t] -8.1994845360825M4[t] -6.86872852233679M5[t] -17.3687285223368M6[t] -14.5353951890034M7[t] + 36.6312714776632M8[t] + 46.1312714776632M9[t] + 36.1312714776632M10[t] + 15.4000000000000M11[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) 562.196907216495 14.454172 38.8951 0 0 X -43.9845360824743 8.918478 -4.9318 7e-06 4e-06 M1 0.80051546391773 19.423726 0.0412 0.96727 0.483635 M2 3.8005154639175 19.423726 0.1957 0.845569 0.422785 M3 0.133848797250830 19.423726 0.0069 0.994526 0.497263 M4 -8.1994845360825 19.423726 -0.4221 0.674513 0.337256 M5 -6.86872852233679 19.457821 -0.353 0.725386 0.362693 M6 -17.3687285223368 19.457821 -0.8926 0.375806 0.187903 M7 -14.5353951890034 19.457821 -0.747 0.458122 0.229061 M8 36.6312714776632 19.457821 1.8826 0.064862 0.032431 M9 46.1312714776632 19.457821 2.3708 0.021152 0.010576 M10 36.1312714776632 19.457821 1.8569 0.068495 0.034247 M11 15.4000000000000 20.285044 0.7592 0.450873 0.225437 Multiple Linear Regression - Regression Statistics Multiple R 0.687111170448407 R-squared 0.47212176055498 Adjusted R-squared 0.360989499619186 F-TEST (value) 4.24828719023134 F-TEST (DF numerator) 12 F-TEST (DF denominator) 57 p-value 9.1030585401386e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 32.0734710949691 Sum Squared Residuals 58636.3302405498 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 555 562.997422680411 -7.99742268041122 2 562 565.997422680412 -3.99742268041233 3 561 562.330756013746 -1.33075601374569 4 555 553.997422680412 1.00257731958763 5 544 555.328178694158 -11.3281786941581 6 537 544.828178694158 -7.82817869415804 7 543 547.661512027491 -4.66151202749142 8 594 598.828178694158 -4.82817869415803 9 611 608.328178694158 2.67182130584191 10 613 598.328178694158 14.6718213058419 11 611 577.596907216495 33.4030927835052 12 594 562.196907216495 31.8030927835052 13 595 562.997422680413 32.0025773195874 14 591 565.997422680412 25.0025773195876 15 589 562.330756013746 26.6692439862543 16 584 553.997422680412 30.0025773195876 17 573 555.328178694158 17.6718213058419 18 567 544.828178694158 22.1718213058419 19 569 547.661512027491 21.3384879725086 20 621 598.828178694158 22.1718213058419 21 629 608.328178694158 20.6718213058419 22 628 598.328178694158 29.6718213058419 23 612 577.596907216495 34.4030927835052 24 595 562.196907216495 32.8030927835051 25 597 562.997422680413 34.0025773195874 26 593 565.997422680412 27.0025773195876 27 590 562.330756013746 27.6692439862543 28 580 553.997422680412 26.0025773195876 29 574 555.328178694158 18.6718213058419 30 573 544.828178694158 28.1718213058419 31 573 547.661512027491 25.3384879725086 32 620 598.828178694158 21.1718213058419 33 626 608.328178694158 17.6718213058419 34 620 598.328178694158 21.6718213058419 35 588 577.596907216495 10.4030927835052 36 566 562.196907216495 3.80309278350514 37 557 562.997422680413 -5.9974226804126 38 561 565.997422680412 -4.99742268041238 39 549 562.330756013746 -13.3307560137457 40 532 553.997422680412 -21.9974226804124 41 526 555.328178694158 -29.3281786941581 42 511 544.828178694158 -33.8281786941581 43 499 547.661512027491 -48.6615120274914 44 555 598.828178694158 -43.8281786941581 45 565 608.328178694158 -43.3281786941581 46 542 598.328178694158 -56.3281786941581 47 527 577.596907216495 -50.5969072164948 48 510 562.196907216495 -52.1969072164949 49 514 562.997422680413 -48.9974226804126 50 517 565.997422680412 -48.9974226804124 51 508 562.330756013746 -54.3307560137457 52 493 553.997422680412 -60.9974226804124 53 490 511.343642611684 -21.3436426116838 54 469 500.843642611684 -31.8436426116838 55 478 503.676975945017 -25.6769759450172 56 528 554.843642611684 -26.8436426116838 57 534 564.343642611684 -30.3436426116838 58 518 554.343642611684 -36.3436426116838 59 506 533.612371134021 -27.6123711340206 60 502 518.212371134021 -16.2123711340206 61 516 519.012886597938 -3.01288659793836 62 528 522.012886597938 5.98711340206187 63 533 518.346219931271 14.6537800687285 64 536 510.012886597938 25.9871134020619 65 537 511.343642611684 25.6563573883162 66 524 500.843642611684 23.1563573883162 67 536 503.676975945017 32.3230240549828 68 587 554.843642611684 32.1563573883162 69 597 564.343642611684 32.6563573883162 70 581 554.343642611684 26.6563573883162 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.343381164972707 0.686762329945414 0.656618835027293 17 0.250854647846801 0.501709295693601 0.7491453521532 18 0.18929786806102 0.37859573612204 0.81070213193898 19 0.134300019923798 0.268600039847597 0.865699980076202 20 0.0972148317809989 0.194429663561998 0.902785168219001 21 0.0610754299903543 0.122150859980709 0.938924570009646 22 0.0385265179664507 0.0770530359329014 0.96147348203355 23 0.0239446010954762 0.0478892021909525 0.976055398904524 24 0.0147867934000526 0.0295735868001052 0.985213206599947 25 0.0129714066646477 0.0259428133292954 0.987028593335352 26 0.00936549658997399 0.0187309931799480 0.990634503410026 27 0.00687917277230095 0.0137583455446019 0.9931208272277 28 0.00478933415581597 0.00957866831163195 0.995210665844184 29 0.00332962110164543 0.00665924220329085 0.996670378898355 30 0.00345397426740992 0.00690794853481983 0.99654602573259 31 0.0033419542697251 0.0066839085394502 0.996658045730275 32 0.00293583492161676 0.00587166984323352 0.997064165078383 33 0.00246289223925413 0.00492578447850825 0.997537107760746 34 0.00320369483854950 0.00640738967709899 0.99679630516145 35 0.00709568395100848 0.0141913679020170 0.992904316048992 36 0.0152114266260229 0.0304228532520458 0.984788573373977 37 0.0206553660564964 0.0413107321129928 0.979344633943504 38 0.0238468545706986 0.0476937091413973 0.976153145429301 39 0.0306296260292763 0.0612592520585526 0.969370373970724 40 0.0444649732756112 0.0889299465512225 0.955535026724389 41 0.0514101992673715 0.102820398534743 0.948589800732629 42 0.0752063546611028 0.150412709322206 0.924793645338897 43 0.123243326740749 0.246486653481499 0.87675667325925 44 0.147088661538242 0.294177323076484 0.852911338461758 45 0.162043334078506 0.324086668157011 0.837956665921494 46 0.218632950779787 0.437265901559574 0.781367049220213 47 0.279512106073364 0.559024212146728 0.720487893926636 48 0.303536393572119 0.607072787144237 0.696463606427881 49 0.282365454722467 0.564730909444935 0.717634545277533 50 0.245440969977629 0.490881939955258 0.754559030022371 51 0.203033725534259 0.406067451068518 0.796966274465741 52 0.158459842099167 0.316919684198335 0.841540157900833 53 0.123427830221458 0.246855660442916 0.876572169778542 54 0.103639047175279 0.207278094350558 0.896360952824721 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 7 0.179487179487179 NOK 5% type I error level 16 0.41025641025641 NOK 10% type I error level 19 0.487179487179487 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/10t1w21258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/10t1w21258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/1faij1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/1faij1258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/2cmy01258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/2cmy01258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/3g9zc1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/3g9zc1258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/4uvov1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/4uvov1258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/5j9lg1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/5j9lg1258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/6aadn1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/6aadn1258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/707bt1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/707bt1258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/8tq811258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/8tq811258729421.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/9gldu1258729421.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729613lojh5ltn31z7gfy/9gldu1258729421.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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