*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 07:49:09 -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/t1258728757ngeavp3th18au2q.htm/, Retrieved Fri, 20 Nov 2009 15:52:50 +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/t1258728757ngeavp3th18au2q.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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100.6 71.7 104.3 77.5 120.4 89.8 107.5 80.3 102.9 78.7 125.6 93.8 107.5 57.6 108.8 60.6 128.4 91 121.1 85.3 119.5 77.4 128.7 77.3 108.7 68.3 105.5 69.9 119.8 81.7 111.3 75.1 110.6 69.9 120.1 84 97.5 54.3 107.7 60 127.3 89.9 117.2 77 119.8 85.3 116.2 77.6 111 69.2 112.4 75.5 130.6 85.7 109.1 72.2 118.8 79.9 123.9 85.3 101.6 52.2 112.8 61.2 128 82.4 129.6 85.4 125.8 78.2 119.5 70.2 115.7 70.2 113.6 69.3 129.7 77.5 112 66.1 116.8 69 127 79.2 112.1 56.2 114.2 63.3 121.1 77.8 131.6 92 125 78.1 120.4 65.1 117.7 71.1 117.5 70.9 120.6 72 127.5 81.9 112.3 70.6 124.5 72.5 115.2 65.1 104.7 54.9 130.9 80 129.2 77.4 113.5 59.6 125.6 57.4 107.6 50.8

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] = + 77.4030165657879 + 0.541259094853539X[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) 77.4030165657879 6.113744 12.6605 0 0 X 0.541259094853539 0.082428 6.5664 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.649797303847281 R-squared 0.422236536087195 Adjusted R-squared 0.412443935003928 F-TEST (value) 43.1179144842988 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.45968518383555e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6.7176759170251 Sum Squared Residuals 2662.50301384456 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100.6 116.211293666786 -15.6112936667864 2 104.3 119.350596416937 -15.0505964169371 3 120.4 126.008083283636 -5.60808328363562 4 107.5 120.866121882527 -13.366121882527 5 102.9 120.000107330761 -17.1001073307613 6 125.6 128.173119663050 -2.57311966304978 7 107.5 108.579540429352 -1.07954042935168 8 108.8 110.203317713912 -1.4033177139123 9 128.4 126.65759419746 1.74240580254014 10 121.1 123.572417356795 -2.47241735679470 11 119.5 119.296470507452 0.203529492548252 12 128.7 119.242344597966 9.4576554020336 13 108.7 114.371012744285 -5.67101274428454 14 105.5 115.237027296050 -9.7370272960502 15 119.8 121.623884615322 -1.82388461532197 16 111.3 118.051574589289 -6.7515745892886 17 110.6 115.237027296050 -4.63702729605021 18 120.1 122.868780533485 -2.76878053348511 19 97.5 106.793385416335 -9.293385416335 20 107.7 109.878562257000 -2.17856225700017 21 127.3 126.062209193121 1.23779080687902 22 117.2 119.079966869510 -1.87996686951033 23 119.8 123.572417356795 -3.7724173567947 24 116.2 119.404722326422 -3.20472232642245 25 111 114.858145929653 -3.85814592965273 26 112.4 118.26807822723 -5.86807822723002 27 130.6 123.788920994736 6.81107900526388 28 109.1 116.481923214213 -7.38192321421335 29 118.8 120.649618244586 -1.84961824458560 30 123.9 123.572417356795 0.327582643205308 31 101.6 105.656741317143 -4.05674131714258 32 112.8 110.528073170824 2.27192682917558 33 128 122.002765981719 5.99723401828056 34 129.6 123.62654326628 5.97345673371994 35 125.8 119.729477783335 6.07052221666542 36 119.5 115.399405024506 4.10059497549373 37 115.7 115.399405024506 0.300594975493734 38 113.6 114.912271839138 -1.31227183913809 39 129.7 119.350596416937 10.3494035830629 40 112 113.180242735607 -1.18024273560676 41 116.8 114.749894110682 2.05010588931798 42 127 120.270736878188 6.72926312181188 43 112.1 107.821777696557 4.27822230344327 44 114.2 111.664717270017 2.53528272998315 45 121.1 119.512974145393 1.58702585460684 46 131.6 127.198853292313 4.40114670768659 47 125 119.675351873849 5.32464812615078 48 120.4 112.638983640753 7.76101635924679 49 117.7 115.886538209874 1.81346179012555 50 117.5 115.778286390904 1.72171360909625 51 120.6 116.373671395243 4.22632860475736 52 127.5 121.732136434293 5.76786356570733 53 112.3 115.615908662448 -3.31590866244768 54 124.5 116.644300942669 7.8556990573306 55 115.2 112.638983640753 2.56101635924679 56 104.7 107.118140873247 -2.41814087324712 57 130.9 120.703744154071 10.1962558459291 58 129.2 119.296470507452 9.90352949254824 59 113.5 109.662058619059 3.83794138094124 60 125.6 108.471288610381 17.1287113896190 61 107.6 104.898978584348 2.70102141565238 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.118008709415478 0.236017418830957 0.881991290584522 6 0.0534200597175088 0.106840119435018 0.946579940282491 7 0.87356148755858 0.252877024882839 0.126438512441420 8 0.876121694500243 0.247756610999514 0.123878305499757 9 0.9287033442312 0.142593311537601 0.0712966557688003 10 0.909765471295201 0.180469057409597 0.0902345287047985 11 0.905374079427813 0.189251841144375 0.0946259205721875 12 0.978355543304264 0.0432889133914725 0.0216444566957362 13 0.969013922788776 0.0619721544224487 0.0309860772112244 14 0.972283529461703 0.055432941076594 0.027716470538297 15 0.961493034103224 0.0770139317935523 0.0385069658967761 16 0.956783243125092 0.0864335137498163 0.0432167568749082 17 0.944957481865735 0.110085036268530 0.0550425181342651 18 0.93095397164798 0.138092056704042 0.0690460283520208 19 0.939941416270355 0.120117167459291 0.0600585837296455 20 0.927489104313446 0.145021791373107 0.0725108956865537 21 0.913052628441025 0.173894743117949 0.0869473715589746 22 0.894346284571123 0.211307430857755 0.105653715428877 23 0.88637951056158 0.227240978876839 0.113620489438420 24 0.87502817374044 0.249943652519118 0.124971826259559 25 0.865976484835748 0.268047030328504 0.134023515164252 26 0.890621792097697 0.218756415804605 0.109378207902303 27 0.914091193631702 0.171817612736596 0.0859088063682979 28 0.952634862032532 0.094730275934937 0.0473651379674685 29 0.953775815579376 0.0924483688412488 0.0462241844206244 30 0.951615128286206 0.0967697434275888 0.0483848717137944 31 0.953523686938176 0.0929526261236482 0.0464763130618241 32 0.950029308572578 0.0999413828548432 0.0499706914274216 33 0.950481580047045 0.0990368399059101 0.0495184199529551 34 0.946111632309627 0.107776735380746 0.0538883676903731 35 0.943131093851776 0.113737812296449 0.0568689061482243 36 0.932796755375457 0.134406489249087 0.0672032446245435 37 0.919497414666218 0.161005170667563 0.0805025853337815 38 0.915745724188857 0.168508551622285 0.0842542758111427 39 0.94385008572411 0.112299828551783 0.0561499142758913 40 0.939735968523446 0.120528062953108 0.0602640314765539 41 0.921501697587372 0.156996604825257 0.0784983024126284 42 0.905687735684488 0.188624528631024 0.094312264315512 43 0.880641081957323 0.238717836085355 0.119358918042677 44 0.843197421614642 0.313605156770716 0.156802578385358 45 0.806817491098507 0.386365017802987 0.193182508901493 46 0.754138805729371 0.491722388541257 0.245861194270629 47 0.691772013244247 0.616455973511506 0.308227986755753 48 0.661168111474262 0.677663777051476 0.338831888525738 49 0.59400084240975 0.8119983151805 0.40599915759025 50 0.526848635453644 0.946302729092712 0.473151364546356 51 0.436391668756762 0.872783337513524 0.563608331243238 52 0.348492628908404 0.696985257816808 0.651507371091596 53 0.491849232656592 0.983698465313184 0.508150767343408 54 0.38439282471508 0.76878564943016 0.61560717528492 55 0.305549231081619 0.611098462163237 0.694450768918381 56 0.37258091157498 0.74516182314996 0.62741908842502 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 1 0.0192307692307692 OK 10% type I error level 11 0.211538461538462 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/10wsk41258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/10wsk41258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/1oxvb1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/1oxvb1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/2371k1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/2371k1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/3z2n81258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/3z2n81258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/4dyfx1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/4dyfx1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/54b5l1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/54b5l1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/6wzlq1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/6wzlq1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/7767x1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/7767x1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/81del1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/81del1258728545.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/9n9zm1258728545.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728757ngeavp3th18au2q/9n9zm1258728545.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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