Model 3

*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: Thu, 19 Nov 2009 14:01:04 -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/19/t125866454421v2frsgrk563o3.htm/, Retrieved Thu, 19 Nov 2009 22:02:36 +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/19/t125866454421v2frsgrk563o3.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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109.8 8.4 111.7 8.4 98.6 8.4 96.9 8.6 95.1 8.9 97 8.8 112.7 8.3 102.9 7.5 97.4 7.2 111.4 7.4 87.4 8.8 96.8 9.3 114.1 9.3 110.3 8.7 103.9 8.2 101.6 8.3 94.6 8.5 95.9 8.6 104.7 8.5 102.8 8.2 98.1 8.1 113.9 7.9 80.9 8.6 95.7 8.7 113.2 8.7 105.9 8.5 108.8 8.4 102.3 8.5 99 8.7 100.7 8.7 115.5 8.6 100.7 8.5 109.9 8.3 114.6 8 85.4 8.2 100.5 8.1 114.8 8.1 116.5 8 112.9 7.9 102 7.9 106 8 105.3 8 118.8 7.9 106.1 8 109.3 7.7 117.2 7.2 92.5 7.5 104.2 7.3 112.5 7 122.4 7 113.3 7 100 7.2 110.7 7.3 112.8 7.1 109.8 6.8 117.3 6.4 109.1 6.1 115.9 6.5 96 7.7 99.8 7.9 116.8 7.5 115.7 6.9

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 X[t] = + 10.0715783709973 -0.00873369273130395Y[t] -0.100931769606700M1[t] -0.322832544139754M2[t] -0.445119417252861M3[t] -0.359524319432952M4[t] -0.148775873837516M5[t] -0.151564495620915M6[t] -0.258369990641970M7[t] -0.587534677183279M8[t] -0.811808183085685M9[t] -0.779661721234496M10[t] -0.221928197710249M11[t] -0.0262069253751581t + 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) 10.0715783709973 1.651738 6.0976 0 0 Y -0.00873369273130395 0.017653 -0.4947 0.62304 0.31152 M1 -0.100931769606700 0.390912 -0.2582 0.79736 0.39868 M2 -0.322832544139754 0.391096 -0.8255 0.413197 0.206598 M3 -0.445119417252861 0.346364 -1.2851 0.204917 0.102459 M4 -0.359524319432952 0.303319 -1.1853 0.241733 0.120867 M5 -0.148775873837516 0.303999 -0.4894 0.626791 0.313396 M6 -0.151564495620915 0.307623 -0.4927 0.624474 0.312237 M7 -0.258369990641970 0.386147 -0.6691 0.506638 0.253319 M8 -0.587534677183279 0.325445 -1.8053 0.077298 0.038649 M9 -0.811808183085685 0.316373 -2.566 0.013465 0.006733 M10 -0.779661721234496 0.405862 -1.921 0.060682 0.030341 M11 -0.221928197710249 0.353809 -0.6273 0.533465 0.266732 t -0.0262069253751581 0.00477 -5.494 1e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.807916145521653 R-squared 0.652728498194565 Adjusted R-squared 0.558675799788926 F-TEST (value) 6.94002946496464 F-TEST (DF numerator) 13 F-TEST (DF denominator) 48 p-value 2.76054716197294e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.471765822725034 Sum Squared Residuals 10.6830235915885 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.4 8.9854802141183 -0.585480214118304 2 8.4 8.72077849802058 -0.320778498020583 3 8.4 8.6866960743124 -0.286696074312398 4 8.6 8.76093152440037 -0.160931524400368 5 8.9 8.96119369153699 -0.061193691536991 6 8.8 8.91560412818896 -0.115604128188956 7 8.3 8.64547273191127 -0.345472731911272 8 7.5 8.37569130876158 -0.875691308761584 9 7.2 8.17324618750619 -0.97324618750619 10 7.4 8.05691402574397 -0.656914025743966 11 8.8 8.79804924944435 0.00195075055565072 12 9.3 8.91167381010519 0.388326189894816 13 9.3 8.63344223087177 0.666557769128233 14 8.7 8.41852256334251 0.281477436657489 15 8.2 8.3259243983346 -0.125924398334592 16 8.3 8.40540006406134 -0.105400064061340 17 8.5 8.65107743340075 -0.151077433400746 18 8.6 8.6107280856915 -0.0107280856914942 19 8.5 8.4008591692598 0.0991408307401932 20 8.2 8.06208157353282 0.137918426467182 21 8.1 7.85264949809238 0.247350501907618 22 7.9 7.72059668941381 0.179403310586191 23 8.6 8.54033514769593 0.0596648523040709 24 8.7 8.60679776760772 0.0932022323922777 25 8.7 8.32681944982804 0.373180550171955 26 8.5 8.14246770685835 0.357532293141649 27 8.4 7.9686461994493 0.431353800550695 28 8.5 8.08480337464753 0.415196625352469 29 8.7 8.29816608088111 0.401833919118887 30 8.7 8.25432325607934 0.445676743920661 31 8.6 7.99205218325983 0.607947816740172 32 8.5 7.76593922376666 0.734060776233341 33 8.3 7.4351088193611 0.864891180638903 34 8 7.4 0.6 35 8.2 8.18655042590316 0.0134495740968354 36 8.1 8.25039293799557 -0.150392937995566 37 8.1 7.99836243695606 0.101637563043939 38 8 7.73540745940463 0.264592540595368 39 7.9 7.61835495474906 0.281645045250939 40 7.9 7.77294037796503 0.127059622034975 41 8 7.92254712726009 0.0774528727399124 42 8 7.89966516501344 0.100334834986557 43 7.9 7.64874789274463 0.251252107255373 44 8 7.40429417851572 0.59570582148428 45 7.7 7.12586593049798 0.574134069502017 46 7.2 7.06280929439671 0.137190705603287 47 7.5 7.81005810300901 -0.310058103009009 48 7.3 7.90359517038784 -0.603595170387844 49 7 7.70396682573616 -0.703966825736163 50 7 7.36939556778804 -0.369395567788042 51 7 7.30037837315464 -0.300378373154644 52 7.2 7.47592465892574 -0.275924658925736 53 7.3 7.56701566692106 -0.267015666921062 54 7.1 7.51967936502677 -0.419679365026767 55 6.8 7.41286802282447 -0.612868022824466 56 6.4 6.99199371542322 -0.591993715423219 57 6.1 6.81312956454235 -0.713129564542348 58 6.5 6.75967999044551 -0.259679990445511 59 7.7 7.46500707394755 0.234992926052452 60 7.9 7.62754031390369 0.272459686096316 61 7.5 7.35192884248966 0.148071157510341 62 6.9 7.11342820458588 -0.213428204585881 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.711589009135627 0.576821981728747 0.288410990864373 18 0.579622099510695 0.84075580097861 0.420377900489305 19 0.463638306938551 0.927276613877101 0.536361693061449 20 0.504391721543718 0.991216556912563 0.495608278456282 21 0.580260135005579 0.839479729988841 0.419739864994421 22 0.514880959126333 0.970238081747335 0.485119040873667 23 0.470691918950193 0.941383837900385 0.529308081049807 24 0.529537350370308 0.940925299259383 0.470462649629692 25 0.459691182011698 0.919382364023395 0.540308817988302 26 0.365469013549692 0.730938027099383 0.634530986450308 27 0.277178454412107 0.554356908824215 0.722821545587893 28 0.199354354653113 0.398708709306225 0.800645645346888 29 0.138814228890680 0.277628457781360 0.86118577110932 30 0.0922940444822546 0.184588088964509 0.907705955517745 31 0.0634712853925621 0.126942570785124 0.936528714607438 32 0.0664497782445967 0.132899556489193 0.933550221755403 33 0.0655712858138789 0.131142571627758 0.934428714186121 34 0.0452374549537841 0.0904749099075682 0.954762545046216 35 0.0543210058990873 0.108642011798175 0.945678994100913 36 0.110744823025405 0.22148964605081 0.889255176974595 37 0.112748665535106 0.225497331070213 0.887251334464894 38 0.0913332908430246 0.182666581686049 0.908666709156975 39 0.067408095740584 0.134816191481168 0.932591904259416 40 0.0475772995962073 0.0951545991924146 0.952422700403793 41 0.0332520791887349 0.0665041583774697 0.966747920811265 42 0.0212878885804527 0.0425757771609055 0.978712111419547 43 0.0289161247545929 0.0578322495091858 0.971083875245407 44 0.0339757542625198 0.0679515085250397 0.96602424573748 45 0.313321462055144 0.626642924110289 0.686678537944855 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 0.0344827586206897 OK 10% type I error level 6 0.206896551724138 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/10y8eg1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/10y8eg1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/1be7z1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/1be7z1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/2u70p1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/2u70p1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/30bcm1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/30bcm1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/40kto1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/40kto1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/5h2ao1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/5h2ao1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/6skdh1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/6skdh1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/7849a1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/7849a1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/86gkf1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/86gkf1258664460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/92igj1258664460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125866454421v2frsgrk563o3/92igj1258664460.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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