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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: Wed, 18 Nov 2009 09:18:24 -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/18/t1258561138loaimgvmc0dtmha.htm/, Retrieved Wed, 18 Nov 2009 17:19:10 +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/18/t1258561138loaimgvmc0dtmha.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 «

23 25,7 19 24,7 18 24,2 19 23,6 19 24,4 22 22,5 23 19,4 20 18,1 14 18,1 14 20,7 14 19,1 15 18,3 11 16,9 17 17,9 16 20,2 20 21,2 24 23,8 23 24 20 26,6 21 25,3 19 27,6 23 24,7 23 26,6 23 24,4 23 24,6 27 26 26 24,8 17 24 24 22,7 26 23 24 24,1 27 24 27 22,7 26 22,6 24 23,1 23 24,4 23 23 24 22 17 21,3 21 21,5 19 21,3 22 23,2 22 21,8 18 23,3 16 21 14 22,4 12 20,4 14 19,9 16 21,3 8 18,9 3 15,6 0 12,5 5 7,8 1 5,5 1 4 3 3,3 6 3,7 7 3,1 8 5 14 6,3

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Icons[t] = -0.38093323978063 + 0.898293762592767Inprod[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) -0.38093323978063 1.746297 -0.2181 0.828087 0.414044 Inprod 0.898293762592767 0.083502 10.7577 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.816177622465578 R-squared 0.666145911413564 Adjusted R-squared 0.660389806437936 F-TEST (value) 115.728589772785 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.99840144432528e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.22211716668176 Sum Squared Residuals 1033.92385541295 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 23 22.7052164588535 0.294783541146501 2 19 21.8069226962607 -2.80692269626071 3 18 21.3577758149643 -3.35777581496433 4 19 20.8187995574087 -1.81879955740868 5 19 21.5374345674829 -2.53743456748289 6 22 19.8306764185566 2.16932358144337 7 23 17.0459657545191 5.95403424548095 8 20 15.8781838631485 4.12181613685154 9 14 15.8781838631485 -1.87818386314846 10 14 18.2137476458897 -4.21374764588965 11 14 16.7764776257412 -2.77647762574122 12 15 16.057842615667 -1.05784261566701 13 11 14.8002313480371 -3.80023134803714 14 17 15.6985251106299 1.30147488937010 15 16 17.7646007645933 -1.76460076459327 16 20 18.6628945271860 1.33710547281397 17 24 20.9984583099272 3.00154169007277 18 23 21.1781170624458 1.82188293755422 19 20 23.5136808451870 -3.51368084518698 20 21 22.3458989538164 -1.34589895381638 21 19 24.4119746077797 -5.41197460777974 22 23 21.8069226962607 1.19307730373928 23 23 23.5136808451870 -0.513680845186977 24 23 21.5374345674829 1.46256543251711 25 23 21.7170933200014 1.28290667999856 26 27 22.9747045876313 4.02529541236868 27 26 21.89675207252 4.10324792748000 28 17 21.1781170624458 -4.17811706244578 29 24 20.0103351710752 3.98966482892482 30 26 20.279823299853 5.72017670014699 31 24 21.2679464387051 2.73205356129494 32 27 21.1781170624458 5.82188293755422 33 27 20.0103351710752 6.98966482892482 34 26 19.9205057948159 6.07949420518409 35 24 20.3696526761123 3.63034732388771 36 23 21.5374345674829 1.46256543251711 37 23 20.279823299853 2.72017670014699 38 24 19.3815295372602 4.61847046273975 39 17 18.7527239034453 -1.75272390344531 40 21 18.9323826559639 2.06761734403614 41 19 18.7527239034453 0.247276096554688 42 22 20.4594820523716 1.54051794762843 43 22 19.2018707847417 2.79812921525830 44 18 20.5493114286308 -2.54931142863085 45 16 18.4832357746675 -2.48323577466748 46 14 19.7408470422974 -5.74084704229735 47 12 17.9442595171118 -5.94425951711182 48 14 17.4951126358154 -3.49511263581544 49 16 18.7527239034453 -2.75272390344531 50 8 16.5968188732227 -8.59681887322267 51 3 13.6324494566665 -10.6324494566665 52 0 10.8477387926290 -10.8477387926290 53 5 6.62575810844296 -1.62575810844296 54 1 4.55968245447959 -3.55968245447959 55 1 3.21224181059044 -2.21224181059044 56 3 2.58343617677551 0.416563823224494 57 6 2.94275368181261 3.05724631818739 58 7 2.40377742425695 4.59622257574305 59 8 4.11053557318321 3.88946442681679 60 14 5.27831746455381 8.72168253544619 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0296407961792342 0.0592815923584683 0.970359203820766 6 0.0966576294945398 0.193315258989080 0.90334237050546 7 0.0571389014006156 0.114277802801231 0.942861098599384 8 0.035237126817347 0.070474253634694 0.964762873182653 9 0.129753664329705 0.25950732865941 0.870246335670295 10 0.182690573942409 0.365381147884818 0.817309426057591 11 0.172431503946813 0.344863007893627 0.827568496053187 12 0.120715763241877 0.241431526483754 0.879284236758123 13 0.129329051752836 0.258658103505672 0.870670948247164 14 0.088845068543999 0.177690137087998 0.911154931456001 15 0.0582333623205408 0.116466724641082 0.94176663767946 16 0.0408095265680014 0.0816190531360027 0.959190473431999 17 0.0418992511326545 0.083798502265309 0.958100748867345 18 0.0306723152992199 0.0613446305984399 0.96932768470078 19 0.0242210414233513 0.0484420828467026 0.975778958576649 20 0.0143429894657367 0.0286859789314735 0.985657010534263 21 0.0160761628384799 0.0321523256769599 0.98392383716152 22 0.0115067357393763 0.0230134714787526 0.988493264260624 23 0.00684688407678859 0.0136937681535772 0.993153115923211 24 0.00471812093852492 0.00943624187704985 0.995281879061475 25 0.00304858165529098 0.00609716331058196 0.99695141834471 26 0.0041849210815038 0.0083698421630076 0.995815078918496 27 0.00494518411018561 0.00989036822037121 0.995054815889814 28 0.00513553425847482 0.0102710685169496 0.994864465741525 29 0.00534972793166437 0.0106994558633287 0.994650272068336 30 0.0094371070633679 0.0188742141267358 0.990562892936632 31 0.00708361667706343 0.0141672333541269 0.992916383322937 32 0.0120469685625121 0.0240939371250243 0.987953031437488 33 0.0285815949993697 0.0571631899987395 0.97141840500063 34 0.047831263610226 0.095662527220452 0.952168736389774 35 0.0471282977238743 0.0942565954477485 0.952871702276126 36 0.0360566942550727 0.0721133885101455 0.963943305744927 37 0.0330116576085902 0.0660233152171803 0.96698834239141 38 0.0489173997170648 0.0978347994341296 0.951082600282935 39 0.0358773876798068 0.0717547753596136 0.964122612320193 40 0.0342758659594788 0.0685517319189575 0.965724134040521 41 0.0272106149825881 0.0544212299651763 0.972789385017412 42 0.0315341871249107 0.0630683742498214 0.96846581287509 43 0.0609077012831776 0.121815402566355 0.939092298716822 44 0.062010241177879 0.124020482355758 0.93798975882212 45 0.0613395941132032 0.122679188226406 0.938660405886797 46 0.064358850777492 0.128717701554984 0.935641149222508 47 0.0613626764153379 0.122725352830676 0.938637323584662 48 0.0607180325252668 0.121436065050534 0.939281967474733 49 0.168640168328073 0.337280336656146 0.831359831671927 50 0.235623753335669 0.471247506671338 0.764376246664331 51 0.221127318054259 0.442254636108517 0.778872681945741 52 0.277069977219661 0.554139954439322 0.722930022780339 53 0.273665465469395 0.547330930938789 0.726334534530605 54 0.605421851683568 0.789156296632865 0.394578148316432 55 0.82509868155329 0.34980263689342 0.17490131844671 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0784313725490196 NOK 5% type I error level 14 0.274509803921569 NOK 10% type I error level 29 0.568627450980392 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/10b4i01258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/10b4i01258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/15ty51258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/15ty51258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/21ca81258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/21ca81258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/301st1258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/301st1258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/4ebsa1258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/4ebsa1258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/5atvx1258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/5atvx1258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/6222p1258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/6222p1258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/7uue31258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/7uue31258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/8vd4m1258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/8vd4m1258561099.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/9e8jp1258561099.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258561138loaimgvmc0dtmha/9e8jp1258561099.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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