verb ws 7

*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: Sat, 21 Nov 2009 02:45:52 -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/21/t1258796832x1bdtdjn6dzmlzf.htm/, Retrieved Sat, 21 Nov 2009 10:47:24 +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/21/t1258796832x1bdtdjn6dzmlzf.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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109 102.86 108.6 102.55 108.8 102.28 108.5 102.26 108.3 102.57 108.2 103.08 108 102.76 107.9 102.51 108 102.87 109.3 103.14 109.6 103.12 109 103.16 108.7 102.48 108.3 102.57 108.4 102.88 107.8 102.63 107.8 102.38 107.6 101.69 107.7 101.96 107.6 102.19 107.6 101.87 108.6 101.6 108.6 101.63 108.2 101.22 107.5 101.21 107.1 101.49 107 101.64 106.9 101.66 106.6 101.77 106.3 101.82 106.1 101.78 105.9 101.28 106 101.29 107.2 101.37 107.2 101.12 106.4 101.51 106.1 102.24 105.9 102.94 106.1 103.09 105.9 103.46 105.8 103.64 105.7 104.39 105.6 104.15 105.3 105.21 105.5 105.8 106.5 105.91 106.5 105.39 106.1 105.46 105.9 104.72 105.8 103.14 106.2 102.63 106.5 102.32 106.6 101.93 106.7 100.62 106.6 100.6 106.5 99.63 106.8 98.9 107.8 98.32 107.9 99.22 107.4 98.81

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 Werkl[t] = + 133.503019293675 -0.237213983652731Infl[t] -0.395389499003M1[t] -0.68208142249928M2[t] -0.477935528120714M3[t] -0.61473848967676M4[t] -0.664425031723232M5[t] -0.76494939164455M6[t] -0.829343200677488M7[t] -0.957532433448864M8[t] -0.769591115331858M9[t] + 0.364117363765983M10[t] + 0.502970525131017M11[t] -0.0522111698227556t + 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) 133.503019293675 4.668324 28.5976 0 0 Infl -0.237213983652731 0.045466 -5.2174 4e-06 2e-06 M1 -0.395389499003 0.349086 -1.1326 0.263234 0.131617 M2 -0.68208142249928 0.348157 -1.9591 0.056176 0.028088 M3 -0.477935528120714 0.347639 -1.3748 0.175852 0.087926 M4 -0.61473848967676 0.347165 -1.7707 0.08323 0.041615 M5 -0.664425031723232 0.346801 -1.9159 0.061609 0.030805 M6 -0.76494939164455 0.346271 -2.2091 0.032187 0.016093 M7 -0.829343200677488 0.345934 -2.3974 0.020629 0.010315 M8 -0.957532433448864 0.345658 -2.7702 0.008054 0.004027 M9 -0.769591115331858 0.345481 -2.2276 0.03084 0.01542 M10 0.364117363765983 0.345339 1.0544 0.297217 0.148608 M11 0.502970525131017 0.345271 1.4567 0.151981 0.07599 t -0.0522111698227556 0.004192 -12.4563 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.901808660837282 R-squared 0.813258860761132 Adjusted R-squared 0.760484190976235 F-TEST (value) 15.4100227263547 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.28741461935533e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.545869860667813 Sum Squared Residuals 13.7067996201329 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 109 108.655588266329 0.34441173367093 2 108.6 108.390221507942 0.209778492057694 3 108.8 108.606204008084 0.193795991915651 4 108.5 108.421934156379 0.0780658436214021 5 108.3 108.246500109577 0.0534998904229708 6 108.2 107.97278544817 0.227214551829943 7 108 107.932088944083 0.0679110559167612 8 107.9 107.810992037402 0.089007962597716 9 108 107.861325151582 0.138674848418444 10 109.3 108.878774685270 0.421225314729592 11 109.6 108.970160956486 0.629839043514256 12 109 108.405490702186 0.594509297814142 13 108.7 108.119195542244 0.580804457756047 14 108.3 107.758943190396 0.541056809603818 15 108.4 107.837341580020 0.562658419980364 16 107.8 107.707630944554 0.0923690554459752 17 107.8 107.665036728598 0.134963271402020 18 107.6 107.675978847574 -0.0759788475742943 19 107.7 107.495326093132 0.204673906867644 20 107.6 107.260366474298 0.339633525701897 21 107.6 107.472005097361 0.127994902638775 22 108.6 108.617550182223 -0.0175501822225505 23 108.6 108.697075754255 -0.0970757542552465 24 108.2 108.239151792599 -0.0391517925990848 25 107.5 107.793923263610 -0.29392326360986 26 107.1 107.388600254868 -0.288600254868066 27 107 107.504952881876 -0.504952881875959 28 106.9 107.311194470824 -0.411194470824097 29 106.6 107.183203220753 -0.583203220753081 30 106.3 107.018606991826 -0.71860699182637 31 106.1 106.911490572317 -0.811490572316786 32 105.9 106.849697161549 -0.94969716154901 33 106 106.983055170007 -0.983055170006736 34 107.2 108.045575360590 -0.8455753605896 35 107.2 108.191520848045 -0.99152084804506 36 106.4 107.543825699467 -1.14382569946672 37 106.1 106.923058822574 -0.823058822574485 38 105.9 106.418105940699 -0.518105940698526 39 106.1 106.534458567706 -0.434458567706437 40 105.9 106.257675262376 -0.357675262376115 41 105.8 106.113079033449 -0.313079033449403 42 105.7 105.782433015966 -0.082433015965776 43 105.6 105.722759393187 -0.122759393186745 44 105.3 105.290912167921 0.00908783207928167 45 105.5 105.286686065860 0.213313934140147 46 106.5 106.342089836933 0.157910163066861 47 106.5 106.552083099975 -0.0520830999748357 48 106.1 105.980296426165 0.119703573834622 49 105.9 105.708234105243 0.191765894757368 50 105.8 105.744129106095 0.0558708939050803 51 106.2 106.017042962314 0.182957037686382 52 106.5 105.901565165867 0.598434834132834 53 106.6 105.892180907623 0.707819092377494 54 106.7 106.050195696464 0.649804303536497 55 106.6 105.938334997281 0.661665002719125 56 106.5 105.988032158830 0.511967841170114 57 106.8 106.296928515191 0.50307148480937 58 107.8 107.516009934984 0.283990065015698 59 107.9 107.389159341239 0.510840658760887 60 107.4 106.931235379583 0.468764620417040 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0198476052914280 0.0396952105828559 0.980152394708572 18 0.00777913085971494 0.0155582617194299 0.992220869140285 19 0.00307654480674009 0.00615308961348017 0.99692345519326 20 0.00142296975134588 0.00284593950269176 0.998577030248654 21 0.00050267747581979 0.00100535495163958 0.99949732252418 22 0.000536608823162407 0.00107321764632481 0.999463391176838 23 0.00317974886813432 0.00635949773626864 0.996820251131866 24 0.00631426700466357 0.0126285340093271 0.993685732995336 25 0.0541568426546244 0.108313685309249 0.945843157345376 26 0.296163151707604 0.592326303415207 0.703836848292396 27 0.790325707581018 0.419348584837963 0.209674292418982 28 0.944505234268225 0.110989531463549 0.0554947657317745 29 0.987323622804882 0.0253527543902364 0.0126763771951182 30 0.994239813775113 0.0115203724497748 0.00576018622488741 31 0.996916249286194 0.00616750142761222 0.00308375071380611 32 0.997327231448106 0.00534553710378742 0.00267276855189371 33 0.996335608944198 0.00732878211160405 0.00366439105580203 34 0.99697869559347 0.00604260881305923 0.00302130440652961 35 0.996663436221505 0.00667312755698998 0.00333656377849499 36 0.996804017456716 0.00639196508656791 0.00319598254328395 37 0.99332347155526 0.0133530568894814 0.0066765284447407 38 0.996656789996578 0.00668642000684422 0.00334321000342211 39 0.999936132141335 0.000127735717330363 6.38678586651816e-05 40 0.999894844120282 0.000210311759437011 0.000105155879718505 41 0.999394073892622 0.00121185221475576 0.000605926107377879 42 0.99696684778466 0.00606630443067963 0.00303315221533982 43 0.990994506291557 0.0180109874168857 0.00900549370844284 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.592592592592593 NOK 5% type I error level 23 0.851851851851852 NOK 10% type I error level 23 0.851851851851852 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/10nfqt1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/10nfqt1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/1m6001258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/1m6001258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/2ohpe1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/2ohpe1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/32nzr1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/32nzr1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/4rxad1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/4rxad1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/51lso1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/51lso1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/63rqx1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/63rqx1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/7nxde1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/7nxde1258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/8h4q51258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/8h4q51258796747.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/90vqd1258796747.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258796832x1bdtdjn6dzmlzf/90vqd1258796747.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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