*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 11:03:57 -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/t1258740280pn3rdt3crnj0chd.htm/, Retrieved Fri, 20 Nov 2009 19:04:52 +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/t1258740280pn3rdt3crnj0chd.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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12.6 18 15.7 16 13.2 19 20.3 18 12.8 23 8 20 0.9 20 3.6 15 14.1 17 21.7 16 24.5 15 18.9 10 13.9 13 11 10 5.8 19 15.5 21 22.4 17 31.7 16 30.3 17 31.4 14 20.2 18 19.7 17 10.8 14 13.2 15 15.1 16 15.6 11 15.5 15 12.7 13 10.9 17 10 16 9.1 9 10.3 17 16.9 15 22 12 27.6 12 28.9 12 31 12 32.9 4 38.1 7 28.8 4 29 3 21.8 3 28.8 0 25.6 5 28.2 3 20.2 4 17.9 3 16.3 10 13.2 4 8.1 1 4.5 1 -0.1 8 0 5 2.3 4 2.8 0 2.9 2 0.1 7 3.5 6 8.6 9 13.8 10

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 Rvnp[t] = + 18.4525616663621 -0.0204001461721176Svdg[t] -1.03551982459347M1[t] -1.62120043851635M2[t] -2.78367988306231M3[t] -2.75143979535904M4[t] -3.16735976612461M5[t] -3.45183994153115M6[t] -3.88488032157866M7[t] -3.47632011693769M8[t] -2.30775991229673M9[t] -0.808160058468847M10[t] -0.356320116937693M11[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) 18.4525616663621 5.481337 3.3664 0.001526 0.000763 Svdg -0.0204001461721176 0.230032 -0.0887 0.92971 0.464855 M1 -1.03551982459347 6.812685 -0.152 0.879839 0.439919 M2 -1.62120043851635 6.841982 -0.2369 0.813726 0.406863 M3 -2.78367988306231 6.809578 -0.4088 0.684551 0.342276 M4 -2.75143979535904 6.814705 -0.4038 0.688227 0.344114 M5 -3.16735976612461 6.817034 -0.4646 0.644346 0.322173 M6 -3.45183994153115 6.807713 -0.507 0.614492 0.307246 M7 -3.88488032157866 6.825877 -0.5691 0.571971 0.285986 M8 -3.47632011693769 6.809578 -0.5105 0.612088 0.306044 M9 -2.30775991229673 6.80849 -0.339 0.736155 0.368077 M10 -0.808160058468847 6.807713 -0.1187 0.906009 0.453005 M11 -0.356320116937693 6.809578 -0.0523 0.95849 0.479245 Multiple Linear Regression - Regression Statistics Multiple R 0.133224252760056 R-squared 0.0177487015234754 Adjusted R-squared -0.233038864044999 F-TEST (value) 0.0707718561853072 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.999990611475983 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.7629555079151 Sum Squared Residuals 5444.53692947195 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12.6 17.0498392106706 -4.44983921067059 2 15.7 16.5049588890919 -0.804958889091902 3 13.2 15.2812790060296 -2.0812790060296 4 20.3 15.333919239905 4.96608076009501 5 12.8 14.8159985382788 -2.01599853827882 6 8 14.5927188013886 -6.59271880138864 7 0.9 14.1596784213411 -13.2596784213411 8 3.6 14.6702393568427 -11.0702393568427 9 14.1 15.7979992691394 -1.69799926913941 10 21.7 17.3179992691394 4.38200073086059 11 24.5 17.7902393568427 6.70976064315731 12 18.9 18.2485602046410 0.651439795359037 13 13.9 17.1518399415311 -3.25183994153114 14 11 16.6273597661246 -5.62735976612461 15 5.8 15.2812790060296 -9.4812790060296 16 15.5 15.2727188013886 0.227281198611366 17 22.4 14.9383994153115 7.46160058468847 18 31.7 14.6743193860771 17.0256806139229 19 30.3 14.2208788598575 16.0791211401425 20 31.4 14.6906395030148 16.7093604969852 21 20.2 15.7775991229673 4.42240087703271 22 19.7 17.2975991229673 2.40240087703271 23 10.8 17.8106395030148 -7.0106395030148 24 13.2 18.1465594737804 -4.94655947378038 25 15.1 17.0906395030148 -1.99063950301479 26 15.6 16.6069596199525 -1.00695961995249 27 15.5 15.3628795907181 0.137120409281927 28 12.7 15.4359199707656 -2.73591997076558 29 10.9 14.9383994153115 -4.03839941531153 30 10 14.6743193860771 -4.6743193860771 31 9.1 14.3840800292344 -5.28408002923442 32 10.3 14.6294390644984 -4.32943906449844 33 16.9 15.8387995614836 1.06120043851635 34 22 17.3995998538279 4.60040014617212 35 27.6 17.8514397953590 9.74856020464096 36 28.9 18.2077599122967 10.6922400877033 37 31 17.1722400877033 13.8277599122967 38 32.9 16.7497606431573 16.1502393568427 39 38.1 15.5260807600950 22.573919239905 40 28.8 15.6195212863146 13.1804787136854 41 29 15.2240014617212 13.7759985382788 42 21.8 14.9395212863146 6.86047871368536 43 28.8 14.5676813447835 14.2323186552165 44 25.6 14.8742408185639 10.7257591814361 45 28.2 16.0836013155491 12.1163986844509 46 20.2 17.5628010232048 2.63719897679517 47 17.9 18.0350411109081 -0.135041110908095 48 16.3 18.2485602046410 -1.94856020464096 49 13.2 17.3354412570802 -4.1354412570802 50 8.1 16.8109610816737 -8.71096108167367 51 4.5 15.6484816371277 -11.1484816371277 52 -0.1 15.5379207016262 -15.6379207016262 53 0 15.1832011693770 -15.1832011693770 54 2.3 14.9191211401425 -12.6191211401425 55 2.8 14.5676813447835 -11.7676813447835 56 2.9 14.9354412570802 -12.0354412570802 57 0.1 16.0020007308606 -15.9020007308606 58 3.5 17.5220007308606 -14.0220007308606 59 8.6 17.9126402338754 -9.31264023387539 60 13.8 18.2485602046410 -4.44856020464096 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0496233549463809 0.0992467098927618 0.95037664505362 17 0.0349474073056031 0.0698948146112062 0.965052592694397 18 0.155143790124196 0.310287580248392 0.844856209875804 19 0.342525594519004 0.685051189038008 0.657474405480996 20 0.515116604999925 0.96976679000015 0.484883395000075 21 0.419313391875039 0.838626783750078 0.580686608124961 22 0.311839549852880 0.623679099705761 0.68816045014712 23 0.287761688786072 0.575523377572145 0.712238311213928 24 0.206790357695469 0.413580715390937 0.793209642304531 25 0.142739350240227 0.285478700480454 0.857260649759773 26 0.0922785967850378 0.184557193570076 0.907721403214962 27 0.0574091396295178 0.114818279259036 0.942590860370482 28 0.0517489712977251 0.103497942595450 0.948251028702275 29 0.037524380976095 0.07504876195219 0.962475619023905 30 0.0288716910273695 0.057743382054739 0.97112830897263 31 0.0233069661799426 0.0466139323598853 0.976693033820057 32 0.0158068175266925 0.0316136350533850 0.984193182473307 33 0.00862466349615174 0.0172493269923035 0.991375336503848 34 0.00427980646742578 0.00855961293485156 0.995720193532574 35 0.002738177426988 0.005476354853976 0.997261822573012 36 0.00229787479739983 0.00459574959479966 0.9977021252026 37 0.00254241171738609 0.00508482343477217 0.997457588282614 38 0.00352185352349237 0.00704370704698474 0.996478146476508 39 0.0398024985372744 0.0796049970745488 0.960197501462726 40 0.0302273201767292 0.0604546403534585 0.96977267982327 41 0.0391286989065364 0.0782573978130727 0.960871301093464 42 0.0380835012071680 0.0761670024143361 0.961916498792832 43 0.072929672700523 0.145859345401046 0.927070327299477 44 0.396013244199629 0.792026488399257 0.603986755800371 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.172413793103448 NOK 5% type I error level 8 0.275862068965517 NOK 10% type I error level 16 0.551724137931034 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/10bs371258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/10bs371258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/1b17v1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/1b17v1258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/2gvde1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/2gvde1258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/303vd1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/303vd1258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/41w8m1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/41w8m1258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/52cvl1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/52cvl1258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/6aqy71258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/6aqy71258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/7egxd1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/7egxd1258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/8ip451258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/8ip451258740233.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/99qwx1258740233.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258740280pn3rdt3crnj0chd/99qwx1258740233.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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