Model5

*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 16:08:43 -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/21/t1258758673oioerow0h5yem4a.htm/, Retrieved Sat, 21 Nov 2009 00:11:25 +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/21/t1258758673oioerow0h5yem4a.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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561 15,9 562 555 18,2 561 544 19,7 555 537 20,1 544 543 19,9 537 594 20 543 611 22,6 594 613 20,6 611 611 20,1 613 594 20,2 611 595 21,8 594 591 22 595 589 19,5 591 584 17,5 589 573 18,2 584 567 18,8 573 569 19,7 567 621 18,8 569 629 18,5 621 628 18,7 629 612 18,5 628 595 19,3 612 597 18,9 595 593 21,4 597 590 22,5 593 580 25 590 574 22,9 580 573 22,9 574 573 21,3 573 620 22,3 573 626 20,9 620 620 19,9 626 588 20,2 620 566 19,8 588 557 17,7 566 561 18,1 557 549 17,6 561 532 18,2 549 526 16 532 511 16,3 526 499 17,3 511 555 19 499 565 18,6 555 542 18 565 527 17,9 542 510 17,8 527 514 18,5 510 517 17,4 514 508 19 517 493 17,4 508 490 20,6 493 469 18,5 490 478 20 469 528 18,8 478 534 18,8 528 518 19,7 534 506 15,3 518 502 10,6 506 516 6,1 502 528 0,9 516

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] = + 49.328366603281 -0.651288616346723X[t] + 0.947192748369779Y1[t] -7.46602476253019M1[t] -12.5116219686066M2[t] -9.5229972894487M3[t] -12.4128790792859M4[t] -1.52744118751515M5[t] + 49.0216445212456M6[t] + 10.1956027171895M7[t] -7.6285553748178M8[t] -15.1264239823416M9[t] -16.2946658166632M10[t] + 0.284989259707416M11[t] -0.205098050841956t + 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) 49.328366603281 23.852712 2.068 0.044413 0.022207 X -0.651288616346723 0.296327 -2.1979 0.033145 0.016572 Y1 0.947192748369779 0.0398 23.7989 0 0 M1 -7.46602476253019 4.301168 -1.7358 0.08944 0.04472 M2 -12.5116219686066 4.341435 -2.8819 0.006038 0.003019 M3 -9.5229972894487 4.431793 -2.1488 0.037068 0.018534 M4 -12.4128790792859 4.484485 -2.768 0.00816 0.00408 M5 -1.52744118751515 4.623348 -0.3304 0.742649 0.371325 M6 49.0216445212456 4.600156 10.6565 0 0 M7 10.1956027171895 4.337639 2.3505 0.023192 0.011596 M8 -7.6285553748178 4.387332 -1.7388 0.088914 0.044457 M9 -15.1264239823416 4.302066 -3.5161 0.001012 0.000506 M10 -16.2946658166632 4.218318 -3.8628 0.000356 0.000178 M11 0.284989259707416 4.198695 0.0679 0.946185 0.473093 t -0.205098050841956 0.083122 -2.4674 0.017478 0.008739 Multiple Linear Regression - Regression Statistics Multiple R 0.99054160865246 R-squared 0.981172678471802 Adjusted R-squared 0.975315289551918 F-TEST (value) 167.510249343534 F-TEST (DF numerator) 14 F-TEST (DF denominator) 45 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.62519234950304 Sum Squared Residuals 1975.19281505611 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 561 563.624079373812 -2.62407937381185 2 555 555.928227550926 -0.928227550926121 3 544 552.051664764503 -8.05166476450334 4 537 538.277049245218 -1.27704924521792 5 543 542.457297570828 0.542702429172447 6 594 598.41931285733 -4.41931285733032 7 611 606.00165276679 4.99834723321046 8 613 605.37725057892 7.62274942108004 9 611 599.894313725467 11.1056862745328 10 594 596.561459481929 -2.56145948192933 11 595 595.791677999017 -0.791677999017001 12 591 596.118525713568 -5.11852571356806 13 589 586.286853447584 2.71314655241640 14 584 580.444349926619 3.55565007338085 15 573 578.036010781643 -5.03601078164348 16 567 564.131137539089 2.86886246091123 17 569 568.542161135087 0.457838864913215 18 621 621.366694044457 -0.366694044457154 19 629 631.784963689692 -2.78496368969167 20 628 621.202991810531 6.79700818946873 21 612 612.683090127065 -0.68309012706509 22 595 595.633635374908 -0.633635374907683 23 597 596.166431124689 0.833568875311195 24 593 595.942507770012 -2.94250777001218 25 590 583.76619648518 6.23380351482047 26 580 574.045701442285 5.95429855771496 27 574 568.725006681231 5.2749933187687 28 573 559.946870350333 13.0531296496665 29 573 570.722079229047 2.27792077095277 30 620 620.414778270619 -0.414778270619278 31 626 626.813501651986 -0.813501651986298 32 620 615.118690615702 4.88130938429759 33 588 601.537180882214 -13.5371808822140 34 566 570.114188495756 -4.11418849575617 35 557 567.018211151478 -10.0182111514778 36 561 557.742873659062 3.25712634093825 37 549 554.186166147342 -5.18616614734208 38 532 537.178384740178 -5.17838474017835 39 526 525.292469602171 0.707530397829175 40 511 516.318946686369 -5.31894668636903 41 499 512.140106685404 -13.1401066854044 42 555 550.010590715096 4.98940928490363 43 565 564.282760215445 0.717239784555338 44 542 556.116204726101 -14.1162047261012 45 527 526.692933716865 0.307066283134788 46 510 511.17683146779 -1.17683146778963 47 514 510.993209739589 3.00679026041063 48 517 515.0083109005 1.99168909949949 49 508 509.136704546083 -1.13670454608293 50 493 496.403336339991 -3.40333633999134 51 490 482.894848170451 7.10515182954894 52 469 478.325996178991 -9.32599617899074 53 478 468.138355379634 9.86164462036595 54 528 527.788624112497 0.211375887503119 55 534 536.117121676088 -2.11712167608783 56 518 523.184862268745 -5.18486226874515 57 506 503.192481548389 2.80751845161147 58 502 493.513885179617 8.4861148203828 59 516 509.030469985227 6.96953001477299 60 528 525.187781956858 2.8122180431425 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.00776634328312592 0.0155326865662518 0.992233656716874 19 0.0383388743687602 0.0766777487375204 0.96166112563124 20 0.0148639951388774 0.0297279902777547 0.985136004861123 21 0.0202561220930235 0.040512244186047 0.979743877906976 22 0.0289815880480547 0.0579631760961094 0.971018411951945 23 0.0188005025660338 0.0376010051320675 0.981199497433966 24 0.00836264887256025 0.0167252977451205 0.99163735112744 25 0.00458221527031271 0.00916443054062542 0.995417784729687 26 0.00321528310837400 0.00643056621674799 0.996784716891626 27 0.00322516498888334 0.00645032997776668 0.996774835011117 28 0.0137758728120099 0.0275517456240198 0.98622412718799 29 0.00979972438159918 0.0195994487631984 0.9902002756184 30 0.00593410863961332 0.0118682172792266 0.994065891360387 31 0.00452687117165917 0.00905374234331834 0.99547312882834 32 0.0734298286707038 0.146859657341408 0.926570171329296 33 0.335866706298841 0.671733412597682 0.664133293701159 34 0.313359517700108 0.626719035400217 0.686640482299892 35 0.240686419697996 0.481372839395992 0.759313580302004 36 0.471672784947965 0.94334556989593 0.528327215052035 37 0.419943880472316 0.839887760944633 0.580056119527684 38 0.673989208575194 0.652021582849613 0.326010791424807 39 0.609567827522405 0.78086434495519 0.390432172477595 40 0.943435486750927 0.113129026498145 0.0565645132490726 41 0.926502887804866 0.146994224390268 0.0734971121951342 42 0.851399457911113 0.297201084177773 0.148600542088887 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.16 NOK 5% type I error level 12 0.48 NOK 10% type I error level 14 0.56 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/102nhj1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/102nhj1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/1454w1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/1454w1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/2j7ux1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/2j7ux1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/36sci1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/36sci1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/477ep1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/477ep1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/59f1i1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/59f1i1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/67i2x1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/67i2x1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/792na1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/792na1258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/8rdo61258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/8rdo61258758519.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/9r1ub1258758519.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258758673oioerow0h5yem4a/9r1ub1258758519.ps (open in new window)

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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