Seatbelt Law part 3

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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 15:41:21 -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/t1258756911nlrgaffxxog73zi.htm/, Retrieved Fri, 20 Nov 2009 23:42:02 +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/t1258756911nlrgaffxxog73zi.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:

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Dataseries X:

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8.9 1,6 8.8 1,3 8.3 1,1 7.5 1,6 7.2 1,9 7.4 1,6 8.8 1,7 9.3 1,6 9.3 1,4 8.7 2,1 8.2 1,9 8.3 1,7 8.5 1,8 8.6 2 8.5 2,5 8.2 2,1 8.1 2,1 7.9 2,3 8.6 2,4 8.7 2,4 8.7 2,3 8.5 1,7 8.4 2 8.5 2,3 8.7 2 8.7 2 8.6 1,3 8.5 1,7 8.3 1,9 8 1,7 8.2 1,6 8.1 1,7 8.1 1,8 8 1,9 7.9 1,9 7.9 1,9 8 2 8 2,1 7.9 1,9 8 1,9 7.7 1,3 7.2 1,3 7.5 1,4 7.3 1,2 7 1,3 7 1,8 7 2,2 7.2 2,6 7.3 2,8 7.1 3,1 6.8 3,9 6.4 3,7 6.1 4,6 6.5 5,1 7.7 5,2 7.9 4,9 7.5 5,1 6.9 4,8 6.6 3,9 6.9 3,5

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 TWIB[t] = + 8.94570339635394 -0.0524066112185449GI[t] + 0.177266874857880M1[t] + 0.169853702904038M2[t] -0.0186076012741805M3[t] -0.286020773228028M4[t] -0.488193284060021M5[t] -0.536654588238240M6[t] + 0.255932239807913M7[t] + 0.380134010059099M8[t] + 0.270624573656509M9[t] + 0.00425953392703265M10[t] -0.170490563597411M11[t] -0.0294424313730398t + 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) 8.94570339635394 0.253406 35.3019 0 0 GI -0.0524066112185449 0.073392 -0.7141 0.478797 0.239398 M1 0.177266874857880 0.295987 0.5989 0.552177 0.276089 M2 0.169853702904038 0.295577 0.5747 0.568328 0.284164 M3 -0.0186076012741805 0.295177 -0.063 0.950009 0.475004 M4 -0.286020773228028 0.294858 -0.97 0.337106 0.168553 M5 -0.488193284060021 0.294934 -1.6553 0.104679 0.052339 M6 -0.536654588238240 0.29466 -1.8213 0.075073 0.037537 M7 0.255932239807913 0.294519 0.869 0.389368 0.194684 M8 0.380134010059099 0.293845 1.2937 0.202243 0.101121 M9 0.270624573656509 0.293657 0.9216 0.361563 0.180781 M10 0.00425953392703265 0.293639 0.0145 0.988489 0.494245 M11 -0.170490563597411 0.2934 -0.5811 0.56402 0.28201 t -0.0294424313730398 0.00458 -6.4278 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.834480053008709 R-squared 0.696356958869417 Adjusted R-squared 0.610544795071644 F-TEST (value) 8.11489802903078 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 4.13355589756748e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.463867330782165 Sum Squared Residuals 9.89795342608065 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 9.00967726188914 -0.109677261889136 2 8.8 8.9885436419278 -0.18854364192779 3 8.3 8.78112122862024 -0.481121228620242 4 7.5 8.45806231968408 -0.958062319684083 5 7.2 8.21072539411349 -1.01072539411349 6 7.4 8.14854364192779 -0.748543641927792 7 8.8 8.90644737747905 -0.106447377479049 8 9.3 9.00644737747905 0.293552622520952 9 9.3 8.87797683194713 0.422023168052872 10 8.7 8.54548473299163 0.154515267008368 11 8.2 8.35177352633786 -0.151773526337857 12 8.3 8.50330298080594 -0.203302980805937 13 8.5 8.64588676316892 -0.145886763168923 14 8.6 8.59854983759833 0.00145016240166682 15 8.5 8.3544427964378 0.145557203562198 16 8.2 8.07854983759833 0.121450162401667 17 8.1 7.8469348953933 0.2530651046067 18 7.9 7.75854983759833 0.141450162401668 19 8.6 8.51645357314959 0.083546426850409 20 8.7 8.61121291202774 0.0887870879722631 21 8.7 8.47750170537396 0.222498294626038 22 8.5 8.21313820100257 0.286861798997428 23 8.4 7.99322368873952 0.406776311260475 24 8.5 8.11854983759833 0.381450162401667 25 8.7 8.28209626444874 0.417903735551263 26 8.7 8.24524066112185 0.454759338878145 27 8.6 8.06402155342358 0.535978446576422 28 8.5 7.74620330560927 0.753796694390728 29 8.3 7.50410704116053 0.79589295883947 30 8 7.43668462785298 0.563315372147018 31 8.2 8.20506968564795 -0.00506968564794955 32 8.1 8.29458836340424 -0.19458836340424 33 8.1 8.15039583450676 -0.0503958345067563 34 8 7.84934770228238 0.150652297717615 35 7.9 7.6451551733849 0.254844826615099 36 7.9 7.78620330560927 0.113796694390728 37 8 7.92878708797226 0.0712129120277414 38 8 7.88669082352352 0.113309176476478 39 7.9 7.67926841021597 0.220731589784027 40 8 7.38241280688909 0.617587193110915 41 7.7 7.18224183141518 0.517758168584821 42 7.2 7.10433809586392 0.0956619041360786 43 7.5 7.86224183141518 -0.362241831415179 44 7.3 7.96748249253703 -0.667482492537034 45 7 7.82328996363955 -0.82328996363955 46 7 7.50127918692776 -0.501279186927762 47 7 7.27612401354286 -0.27612401354286 48 7.2 7.39620950127981 -0.196209501279814 49 7.3 7.53355262252095 -0.233552622520945 50 7.1 7.4809750358285 -0.380975035828500 51 6.8 7.2211460113024 -0.421146011302406 52 6.4 6.93477173021923 -0.534771730219227 53 6.1 6.6559908379175 -0.555990837917504 54 6.5 6.55188379675697 -0.0518837967569736 55 7.7 7.30978753230823 0.390212467691769 56 7.9 7.42026885455194 0.47973114544806 57 7.5 7.2708356645326 0.229164335467398 58 6.9 6.99075017679565 -0.090750176795649 59 6.6 6.83372359799486 -0.233723597994856 60 6.9 6.99573437470665 -0.0957343747066452 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.771144280862221 0.457711438275558 0.228855719137779 18 0.684789250698459 0.630421498603083 0.315210749301542 19 0.649629037303912 0.700741925392176 0.350370962696088 20 0.723209873561065 0.55358025287787 0.276790126438935 21 0.715879526342434 0.568240947315133 0.284120473657566 22 0.66245216316661 0.675095673666781 0.337547836833391 23 0.564295793956954 0.871408412086091 0.435704206043046 24 0.487861620645541 0.975723241291082 0.512138379354459 25 0.386637327694515 0.773274655389031 0.613362672305485 26 0.289582427128512 0.579164854257024 0.710417572871488 27 0.213617611588218 0.427235223176437 0.786382388411782 28 0.200607469153051 0.401214938306103 0.799392530846949 29 0.179976039237837 0.359952078475675 0.820023960762163 30 0.124775150489583 0.249550300979167 0.875224849510417 31 0.183115132750648 0.366230265501295 0.816884867249352 32 0.330865211728397 0.661730423456793 0.669134788271603 33 0.381089774614411 0.762179549228821 0.618910225385589 34 0.328371359003443 0.656742718006886 0.671628640996557 35 0.251176998172528 0.502353996345056 0.748823001827472 36 0.21311778265466 0.42623556530932 0.78688221734534 37 0.174627491908716 0.349254983817432 0.825372508091284 38 0.128434997371355 0.256869994742709 0.871565002628645 39 0.0946913942640307 0.189382788528061 0.90530860573597 40 0.137727214795305 0.275454429590609 0.862272785204695 41 0.610146301790981 0.779707396418038 0.389853698209019 42 0.94241754383455 0.115164912330899 0.0575824561654493 43 0.896992087438221 0.206015825123557 0.103007912561779 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/10hcqo1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/10hcqo1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/1sqsa1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/1sqsa1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/2f4lg1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/2f4lg1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/3xr1p1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/3xr1p1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/4r6kn1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/4r6kn1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/5t0k71258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/5t0k71258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/6eaok1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/6eaok1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/7pl911258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/7pl911258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/8ruhw1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/8ruhw1258756877.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/9q8ts1258756877.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258756911nlrgaffxxog73zi/9q8ts1258756877.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 = 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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