WS7

*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 08:40:42 -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/t1258731800zgprqq48jb4g75f.htm/, Retrieved Fri, 20 Nov 2009 16:43:32 +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/t1258731800zgprqq48jb4g75f.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:

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363 14.3 364 14.2 363 15.9 358 15.3 357 15.5 357 15.1 380 15 378 12.1 376 15.8 380 16.9 379 15.1 384 13.7 392 14.8 394 14.7 392 16 396 15.4 392 15 396 15.5 419 15.1 421 11.7 420 16.3 418 16.7 410 15 418 14.9 426 14.6 428 15.3 430 17.9 424 16.4 423 15.4 427 17.9 441 15.9 449 13.9 452 17.8 462 17.9 455 17.4 461 16.7 461 16 463 16.6 462 19.1 456 17.8 455 17.2 456 18.6 472 16.3 472 15.1 471 19.2 465 17.7 459 19.1 465 18 468 17.5 467 17.8 463 21.1 460 17.2 462 19.4 461 19.8 476 17.6 476 16.2 471 19.5 453 19.9 443 20 442 17.3

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation WK>25j[t] = + 225.948105079717 + 12.2360142217909ExpBe[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) 225.948105079717 33.116723 6.8228 0 0 ExpBe 12.2360142217909 1.991437 6.1443 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.627910959932989 R-squared 0.394272173603967 Adjusted R-squared 0.383828590390243 F-TEST (value) 37.7525764419461 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 7.8704400552354e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 29.6188975514582 Sum Squared Residuals 50882.1873454992 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 363 400.923108451326 -37.9231084513259 2 364 399.699507029148 -35.699507029148 3 363 420.500731206193 -57.5007312061926 4 358 413.159122673118 -55.1591226731181 5 357 415.606325517476 -58.6063255174763 6 357 410.71191982876 -53.7119198287599 7 380 409.488318406581 -29.4883184065808 8 378 374.003877163387 3.99612283661285 9 376 419.277129784014 -43.2771297840136 10 380 432.736745427984 -52.7367454279835 11 379 410.71191982876 -31.7119198287599 12 384 393.581499918253 -9.5814999182526 13 392 407.041115562223 -15.0411155622226 14 394 405.817514140043 -11.8175141400435 15 392 421.724332628372 -29.7243326283717 16 396 414.382724095297 -18.3827240952972 17 392 409.488318406581 -17.4883184065808 18 396 415.606325517476 -19.6063255174763 19 419 410.71191982876 8.2880801712401 20 421 369.109471474671 51.8905285253292 21 420 425.395136894909 -5.39513689490901 22 418 430.289542583625 -12.2895425836254 23 410 409.488318406581 0.511681593419189 24 418 408.264716984402 9.73528301559828 25 426 404.593912717864 21.4060872821356 26 428 413.159122673118 14.8408773268819 27 430 444.972759649774 -14.9727596497745 28 424 426.618738317088 -2.61873831708808 29 423 414.382724095297 8.61727590470282 30 427 444.972759649774 -17.9727596497745 31 441 420.500731206193 20.4992687938074 32 449 396.028702762611 52.9712972373892 33 452 443.749158227595 8.2508417724046 34 462 444.972759649774 17.0272403502255 35 455 438.854752538879 16.145247461121 36 461 430.289542583625 30.7104574163746 37 461 421.724332628372 39.2756673716283 38 463 429.065941161446 33.9340588385537 39 462 459.655976715924 2.34402328407641 40 456 443.749158227595 12.2508417724046 41 455 436.407549694521 18.5924503054792 42 456 453.537969605028 2.46203039497187 43 472 425.395136894909 46.604863105091 44 472 410.71191982876 61.2880801712401 45 471 460.879578138103 10.1204218618973 46 465 442.525556805416 22.4744431945837 47 459 459.655976715924 -0.655976715923595 48 465 446.196361071954 18.8036389280464 49 468 440.078353961058 27.9216460389419 50 467 443.749158227595 23.2508417724046 51 463 484.128005159505 -21.1280051595054 52 460 436.407549694521 23.5924503054792 53 462 463.326780982461 -1.32678098246083 54 461 468.221186671177 -7.22118667117723 55 476 441.301955383237 34.6980446167628 56 476 424.17153547273 51.8284645272701 57 471 464.55038240464 6.44961759536006 58 453 469.444788093356 -16.4447880933563 59 443 470.668389515535 -27.6683895155354 60 442 437.6311511167 4.36884888330007 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00257513054709729 0.00515026109419458 0.997424869452903 6 0.00061177077558229 0.00122354155116458 0.999388229224418 7 0.0128925028258305 0.0257850056516611 0.98710749717417 8 0.00420773165539052 0.00841546331078104 0.99579226834461 9 0.00617426758997194 0.0123485351799439 0.993825732410028 10 0.0120154654008798 0.0240309308017596 0.98798453459912 11 0.0109605290536845 0.0219210581073691 0.989039470946316 12 0.0109488658308091 0.0218977316616183 0.98905113416919 13 0.0232849932849134 0.0465699865698267 0.976715006715087 14 0.0408725042668058 0.0817450085336116 0.959127495733194 15 0.0716461498377686 0.143292299675537 0.928353850162231 16 0.118273533140135 0.236547066280271 0.881726466859865 17 0.164716631438353 0.329433262876706 0.835283368561647 18 0.265210322363365 0.530420644726729 0.734789677636635 19 0.553455742967139 0.893088514065722 0.446544257032861 20 0.660419176713513 0.679161646572974 0.339580823286487 21 0.840487416348247 0.319025167303506 0.159512583651753 22 0.923840748726984 0.152318502546033 0.0761592512730163 23 0.95763516763684 0.0847296647263182 0.0423648323631591 24 0.978729605789508 0.0425407884209848 0.0212703942104924 25 0.989821178756695 0.0203576424866093 0.0101788212433047 26 0.995769543374432 0.00846091325113504 0.00423045662556752 27 0.998519950187409 0.00296009962518289 0.00148004981259145 28 0.99964473124843 0.000710537503141008 0.000355268751570504 29 0.999967849105822 6.43017883560779e-05 3.21508941780389e-05 30 0.999998512215718 2.97556856371764e-06 1.48778428185882e-06 31 0.999999792727278 4.14545444467483e-07 2.07272722233741e-07 32 0.999999976430805 4.71383906807145e-08 2.35691953403572e-08 33 0.999999981418851 3.71622975040493e-08 1.85811487520247e-08 34 0.999999972774466 5.44510680745652e-08 2.72255340372826e-08 35 0.999999963035036 7.39299282989615e-08 3.69649641494808e-08 36 0.999999943181739 1.13636522329712e-07 5.6818261164856e-08 37 0.999999924128844 1.51742311617459e-07 7.58711558087295e-08 38 0.99999985249019 2.95019618884409e-07 1.47509809442204e-07 39 0.999999519293458 9.61413083296411e-07 4.80706541648206e-07 40 0.999998913107996 2.17378400726327e-06 1.08689200363164e-06 41 0.999998233293826 3.53341234718722e-06 1.76670617359361e-06 42 0.999995310252576 9.37949484697647e-06 4.68974742348824e-06 43 0.99999116571797 1.76685640603642e-05 8.83428203018211e-06 44 0.99998518281422 2.96343715613768e-05 1.48171857806884e-05 45 0.999969577359277 6.0845281446512e-05 3.0422640723256e-05 46 0.999907313729362 0.000185372541276442 9.2686270638221e-05 47 0.999709843996474 0.000580312007052959 0.000290156003526480 48 0.999167914155006 0.00166417168998765 0.000832085844993826 49 0.99787744256144 0.00424511487711844 0.00212255743855922 50 0.994638437687294 0.0107231246254118 0.00536156231270588 51 0.988739551177817 0.0225208976443666 0.0112604488221833 52 0.97399410164241 0.0520117967151801 0.0260058983575901 53 0.940118993971296 0.119762012057407 0.0598810060287036 54 0.874720052178228 0.250559895643543 0.125279947821772 55 0.805506675249021 0.388986649501957 0.194493324750979 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.529411764705882 NOK 5% type I error level 37 0.725490196078431 NOK 10% type I error level 40 0.784313725490196 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/10grk11258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/10grk11258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/1s2aa1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/1s2aa1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/2tl8g1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/2tl8g1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/3ijyz1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/3ijyz1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/4k0xd1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/4k0xd1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/5k9vo1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/5k9vo1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/63nnh1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/63nnh1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/7lgbt1258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/7lgbt1258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/8lgp01258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/8lgp01258731636.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/9rwu91258731636.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258731800zgprqq48jb4g75f/9rwu91258731636.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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