SHW WS7

*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 05:47:50 -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/t125872130794q9ttq63pxi267.htm/, Retrieved Fri, 20 Nov 2009 13:48:40 +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/t125872130794q9ttq63pxi267.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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8.3 0 8.5 8.6 7.8 0 8.3 8.5 7.8 0 7.8 8.3 8 0 7.8 7.8 8.6 0 8 7.8 8.9 0 8.6 8 8.9 0 8.9 8.6 8.6 0 8.9 8.9 8.3 0 8.6 8.9 8.3 0 8.3 8.6 8.3 0 8.3 8.3 8.4 0 8.3 8.3 8.5 0 8.4 8.3 8.4 0 8.5 8.4 8.6 0 8.4 8.5 8.5 0 8.6 8.4 8.5 0 8.5 8.6 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.5 0 8.5 8.5 8.6 0 8.5 8.5 8.4 0 8.6 8.5 8.1 0 8.4 8.6 8 0 8.1 8.4 8 0 8 8.1 8 0 8 8 8 0 8 8 7.9 0 8 8 7.8 0 7.9 8 7.8 0 7.8 7.9 7.9 0 7.8 7.8 8.1 0 7.9 7.8 8 0 8.1 7.9 7.6 0 8 8.1 7.3 0 7.6 8 7 0 7.3 7.6 6.8 0 7 7.3 7 0 6.8 7 7.1 0 7 6.8 7.2 0 7.1 7 7.1 1 7.2 7.1 6.9 1 7.1 7.2 6.7 1 6.9 7.1 6.7 1 6.7 6.9 6.6 1 6.7 6.7 6.9 1 6.6 6.7 7.3 1 6.9 6.6 7.5 1 7.3 6.9 7.3 1 7.5 7.3 7.1 1 7.3 7.5 6.9 1 7.1 7.3 7.1 1 6.9 7.1

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] = + 2.06927334447505 -0.0797015328416982X[t] + 1.39881716350103Y1[t] -0.633441242873896Y2[t] -0.0335768798788945M1[t] -0.0768525073841112M2[t] + 0.0358245516507144M3[t] -0.0767177553795189M4[t] + 0.078013091687698M5[t] -0.0331205954376406M6[t] -0.0843123036080516M7[t] -0.0136287700007924M8[t] -0.0542657039509433M9[t] + 0.0657425302264035M10[t] -0.0510501938447505M11[t] -0.00610933566969867t + 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) 2.06927334447505 0.587125 3.5244 0.00104 0.00052 X -0.0797015328416982 0.084142 -0.9472 0.348941 0.174471 Y1 1.39881716350103 0.121775 11.4869 0 0 Y2 -0.633441242873896 0.124017 -5.1077 7e-06 4e-06 M1 -0.0335768798788945 0.113042 -0.297 0.767907 0.383954 M2 -0.0768525073841112 0.112984 -0.6802 0.500105 0.250053 M3 0.0358245516507144 0.113091 0.3168 0.752983 0.376491 M4 -0.0767177553795189 0.11335 -0.6768 0.502231 0.251116 M5 0.078013091687698 0.112986 0.6905 0.4937 0.24685 M6 -0.0331205954376406 0.113996 -0.2905 0.772832 0.386416 M7 -0.0843123036080516 0.113241 -0.7445 0.460695 0.230348 M8 -0.0136287700007924 0.113018 -0.1206 0.904591 0.452296 M9 -0.0542657039509433 0.113129 -0.4797 0.633945 0.316973 M10 0.0657425302264035 0.11341 0.5797 0.565221 0.282611 M11 -0.0510501938447505 0.119009 -0.429 0.670144 0.335072 t -0.00610933566969867 0.00245 -2.494 0.016651 0.008326 Multiple Linear Regression - Regression Statistics Multiple R 0.975611291691286 R-squared 0.95181739247554 Adjusted R-squared 0.934609318359662 F-TEST (value) 55.3122555183133 F-TEST (DF numerator) 15 F-TEST (DF denominator) 42 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.168088897767444 Sum Squared Residuals 1.18666285721232 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.3 8.4719383299697 -0.171938329969697 2 7.8 8.20613405838197 -0.406134058381967 3 7.8 7.73998144857136 0.0600185514286436 4 8 7.93805042730837 0.0619495726916263 5 8.6 8.3664353714061 0.233564628593901 6 8.9 8.9617943981369 -0.0617943981368987 7 8.9 8.94407375762276 -0.0440737576227613 8 8.6 8.81861558269815 -0.218615582698153 9 8.3 8.352224164028 -0.0522241640279928 10 8.3 8.2365102863475 0.0634897136524972 11 8.3 8.30364059946882 -0.00364059946881829 12 8.4 8.34858145764387 0.0514185423561296 13 8.5 8.44877695844538 0.0512230415546199 14 8.4 8.47592958733318 -0.0759295873331775 15 8.6 8.37927147006081 0.220728529939187 16 8.5 8.60372738434848 -0.103727384348475 17 8.5 8.48577893082111 0.0142210691788878 18 8.5 8.43188003231346 0.0681199676865357 19 8.5 8.37457898847335 0.125421011526645 20 8.5 8.43915318641091 0.0608468135890848 21 8.5 8.39240691679107 0.107593083208934 22 8.5 8.50630581529871 -0.00630581529871382 23 8.5 8.38340375555786 0.116596244442139 24 8.5 8.42834461373291 0.071655386267087 25 8.5 8.38865839818432 0.111341601815680 26 8.5 8.3392734350094 0.160726564990596 27 8.6 8.44584115837453 0.154158841625468 28 8.4 8.4670712320247 -0.0670712320247015 29 8.1 8.27258518643463 -0.172585186434626 30 8 7.86238526316406 0.137614736835943 31 8 7.85523487583601 0.144765124163985 32 8 7.98315319806096 0.0168468019390354 33 8 7.93640692844111 0.063593071558885 34 7.9 8.05030582694876 -0.150305826948763 35 7.8 7.7875220508578 0.0124779491421919 36 7.8 7.75592531697015 0.0440746830298544 37 7.9 7.77958322570894 0.120416774291058 38 8.1 7.87007997888413 0.229920021115869 39 8 8.19306701066207 -0.193067010662073 40 7.6 7.80784540303726 -0.20784540303726 41 7.3 7.46028417332175 -0.160284173321755 42 7 7.17677249862597 -0.176772498625967 43 6.8 6.88985867859772 -0.089858678597718 44 7 6.86470181669724 0.135298183302759 45 7.1 7.22440722835238 -0.124407228352377 46 7.2 7.35149959463535 -0.151499594635348 47 7.1 7.22543359411551 -0.125433594115513 48 6.9 7.06714861165307 -0.16714861165307 49 6.7 6.81104308769166 -0.111043087691662 50 6.7 6.60858294039132 0.0914170596086807 51 6.6 6.84183891233123 -0.241838912331226 52 6.9 6.58330555328119 0.31669444671881 53 7.3 7.21491633801641 0.0850836619835917 54 7.5 7.46716780775961 0.0328321922403873 55 7.3 7.43625369947015 -0.136253699470151 56 7.1 7.09437621613273 0.0056237838672736 57 6.9 6.89455476238745 0.00544523761255068 58 7.1 6.85537847676967 0.244621523230327 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.250879021910422 0.501758043820844 0.749120978089578 20 0.138778869348912 0.277557738697824 0.861221130651088 21 0.102648590634357 0.205297181268714 0.897351409365643 22 0.130375125372907 0.260750250745814 0.869624874627093 23 0.0740806770164721 0.148161354032944 0.925919322983528 24 0.0381795749997823 0.0763591499995645 0.961820425000218 25 0.0176440846080840 0.0352881692161679 0.982355915391916 26 0.0262679667706543 0.0525359335413086 0.973732033229346 27 0.0325580412827049 0.0651160825654097 0.967441958717295 28 0.0223477494953255 0.0446954989906511 0.977652250504675 29 0.0840654297527896 0.168130859505579 0.91593457024721 30 0.0566165290471322 0.113233058094264 0.943383470952868 31 0.0787901812680195 0.157580362536039 0.92120981873198 32 0.0705260796382611 0.141052159276522 0.92947392036174 33 0.0814432641803783 0.162886528360757 0.918556735819622 34 0.101121009988781 0.202242019977562 0.898878990011219 35 0.0837883591798034 0.167576718359607 0.916211640820197 36 0.0805835247069533 0.161167049413907 0.919416475293047 37 0.092315271054184 0.184630542108368 0.907684728945816 38 0.146924635079385 0.293849270158770 0.853075364920615 39 0.740330892265572 0.519338215468855 0.259669107734428 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 2 0.0952380952380952 NOK 10% type I error level 5 0.238095238095238 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/10k33s1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/10k33s1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/19ef11258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/19ef11258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/2jzb51258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/2jzb51258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/3d65c1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/3d65c1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/4xj9z1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/4xj9z1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/5h5uy1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/5h5uy1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/6xzbp1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/6xzbp1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/7gnaz1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/7gnaz1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/8dhic1258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/8dhic1258721265.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/96zp61258721265.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872130794q9ttq63pxi267/96zp61258721265.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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