*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 06:56:12 -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/t1258639145bvjdlf9nubwk4k2.htm/, Retrieved Thu, 19 Nov 2009 14:59:18 +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/t1258639145bvjdlf9nubwk4k2.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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110.5 55 110.8 48.7 104.2 70.3 88.9 94.8 89.8 58.5 90 62.4 93.9 56.7 91.3 65.1 87.8 114.4 99.7 50.7 73.5 44.5 79.2 72 96.9 61.2 95.2 68.4 95.6 78.7 89.7 64.1 92.8 64.6 88 71.9 101.1 71 92.7 76.4 95.8 117.3 103.8 66.1 81.8 57.3 87.1 75 105.9 63.8 108.1 62.2 102.6 75.4 93.7 58 103.5 62.1 100.6 99.2 113.3 70.7 102.4 73.3 102.1 111.2 106.9 68.9 87.3 57.6 93.1 72.9 109.1 75.9 120.3 79.4 104.9 96.9 92.6 75.2 109.8 60.3 111.4 88.9 117.9 90.5 121.6 79.9 117.8 116.3 124.2 95.2 106.8 81.5 102.7 89.1 116.8 76 113.6 100.5 96.1 83.9 85 75.1 83.2 69.5 84.9 95.1 83 90.1 79.6 78.4 83.2 113.8 83.8 73.6 82.8 56.5 71.4 97.7

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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation prod[t] = + 66.615835719766 + 0.261866270220996`inv `[t] + 24.6859521932016M1[t] + 25.0499411950045M2[t] + 13.7545503461808M3[t] + 5.0785128370699M4[t] + 13.6861755353866M5[t] + 7.51169583306567M6[t] + 16.4218449509768M7[t] + 12.4446259870471M8[t] + 1.82899134082118M9[t] + 19.6463261866882M10[t] + 5.43061782982147M11[t] -0.033778837209496t + 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) 66.615835719766 12.597272 5.2881 3e-06 2e-06 `inv ` 0.261866270220996 0.167355 1.5647 0.124499 0.062249 M1 24.6859521932016 7.368378 3.3503 0.00162 0.00081 M2 25.0499411950045 7.204189 3.4771 0.001118 0.000559 M3 13.7545503461808 7.16031 1.9209 0.060949 0.030475 M4 5.0785128370699 7.170758 0.7082 0.482379 0.241189 M5 13.6861755353866 7.577135 1.8062 0.077425 0.038712 M6 7.51169583306567 7.156678 1.0496 0.299382 0.149691 M7 16.4218449509768 7.132997 2.3022 0.025898 0.012949 M8 12.4446259870471 7.155111 1.7393 0.088677 0.044338 M9 1.82899134082118 9.151114 0.1999 0.842467 0.421233 M10 19.6463261866882 7.278557 2.6992 0.00969 0.004845 M11 5.43061782982147 7.952979 0.6828 0.498132 0.249066 t -0.033778837209496 0.10958 -0.3083 0.759279 0.37964 Multiple Linear Regression - Regression Statistics Multiple R 0.615161986324822 R-squared 0.378424269419101 Adjusted R-squared 0.202761562950586 F-TEST (value) 2.15426641788038 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.0283017411167295 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.2201809184622 Sum Squared Residuals 5791.05315277902 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.5 105.670653937913 4.82934606208667 2 110.8 104.351106600114 6.44889339988596 3 104.2 98.6782483508544 5.5217516491456 4 88.9 96.3841556249483 -7.48415562494834 5 89.8 95.4522938770334 -5.65229387703344 6 90 90.2653137913649 -0.265313791364894 7 93.9 97.6490463318069 -3.74904633180687 8 91.3 95.837725200524 -4.53772520052403 9 87.8 98.0983188389837 -10.2983188389837 10 99.7 99.2009934345638 0.499006565436208 11 73.5 83.3279353651174 -9.82793536511739 12 79.2 85.0648611291638 -5.86486112916379 13 96.9 106.888878766769 -9.98887876676908 14 95.2 109.104526076954 -13.9045260769537 15 95.6 100.472578974197 -4.87257897419682 16 89.7 87.9395150826499 1.76048491735015 17 92.8 96.6443320788675 -3.84433207886754 18 88 92.3476973119504 -4.3476973119504 19 101.1 100.988387949453 0.111612050546823 20 92.7 98.3914680075073 -5.69146800750732 21 95.8 98.4523849761106 -2.65238497611064 22 103.8 102.828387949453 0.971612050546831 23 81.8 86.2744775774322 -4.47447757743218 24 87.1 85.4451138933128 1.65488610668716 25 105.9 107.164385022830 -1.26438502282974 26 108.1 107.075609155070 1.02439084493039 27 102.6 99.2030742359536 3.39692576404642 28 93.7 85.9367847877878 7.76321521221217 29 103.5 95.584320356801 7.9156796431989 30 100.6 99.0913004424696 1.50869955753036 31 113.3 100.504482021873 12.7955179781271 32 102.4 97.1743365233083 5.22566347669172 33 102.1 96.4496546812486 5.65034531875138 34 106.9 103.156267459558 3.743732540442 35 87.3 85.9476914119845 1.35230858801547 36 93.1 84.4898486793348 8.6101513206652 37 109.1 109.927620845990 -0.82762084598985 38 120.3 111.174362956357 9.12563704364322 39 104.9 104.427852999191 0.472147000808966 40 92.6 90.035538589075 2.56446141092498 41 109.8 94.7076150238894 15.0923849761106 42 111.4 95.9887318126794 15.4112681873206 43 117.9 105.284088125735 12.6159118742653 44 121.6 98.497307860253 23.1026921397471 45 117.8 97.3798266128617 20.4201733871383 46 124.2 109.638004319856 14.5619956801437 47 106.8 91.8009492237524 14.9990507762476 48 102.7 88.326736210401 14.3732637895990 49 116.8 109.548461426498 7.251538573502 50 113.6 116.294395211506 -2.69439521150585 51 96.1 100.618245439804 -4.51824543980414 52 85 89.604005915539 -4.60400591553896 53 83.2 96.7114386634086 -13.5114386634086 54 84.9 97.2069566415357 -12.3069566415356 55 83 104.773995571132 -21.7739955711323 56 79.6 97.6991624084075 -18.0991624084075 57 83.2 96.3198148907953 -13.1198148907953 58 83.8 103.576346836569 -19.7763468365688 59 82.8 84.8489464217135 -2.04894642171353 60 71.4 90.1734400877876 -18.7734400877876 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0964755949310787 0.192951189862157 0.903524405068921 18 0.0481627202277533 0.0963254404555066 0.951837279772247 19 0.0707887892008522 0.141577578401704 0.929211210799148 20 0.043355154581319 0.086710309162638 0.956644845418681 21 0.0416281727657958 0.0832563455315916 0.958371827234204 22 0.0255625174886962 0.0511250349773925 0.974437482511304 23 0.0272918533766425 0.054583706753285 0.972708146623358 24 0.0203029135598361 0.0406058271196723 0.979697086440164 25 0.0116444020881405 0.023288804176281 0.98835559791186 26 0.00620866260139614 0.0124173252027923 0.993791337398604 27 0.00276951984733803 0.00553903969467606 0.997230480152662 28 0.00112716273968816 0.00225432547937631 0.998872837260312 29 0.00104122173245483 0.00208244346490967 0.998958778267545 30 0.00157715800674303 0.00315431601348606 0.998422841993257 31 0.00168666497250730 0.00337332994501459 0.998313335027493 32 0.00119244490695311 0.00238488981390622 0.998807555093047 33 0.00103379065341227 0.00206758130682454 0.998966209346588 34 0.000547407720036218 0.00109481544007244 0.999452592279964 35 0.00180834123598707 0.00361668247197413 0.998191658764013 36 0.00212702691195710 0.00425405382391421 0.997872973088043 37 0.0162280586777488 0.0324561173554975 0.983771941322251 38 0.0299430891259082 0.0598861782518164 0.970056910874092 39 0.0823794600976472 0.164758920195294 0.917620539902353 40 0.526637578241239 0.946724843517522 0.473362421758761 41 0.477972860841221 0.955945721682442 0.522027139158779 42 0.51978503574513 0.96042992850974 0.48021496425487 43 0.364093281555167 0.728186563110335 0.635906718444833 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.370370370370370 NOK 5% type I error level 14 0.518518518518518 NOK 10% type I error level 20 0.740740740740741 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/10wwpa1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/10wwpa1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/1d2qa1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/1d2qa1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/2yk7v1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/2yk7v1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/3geao1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/3geao1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/4qp721258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/4qp721258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/5dokr1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/5dokr1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/6q5sy1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/6q5sy1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/717id1258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/717id1258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/89b251258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/89b251258638968.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/9dtm71258638968.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258639145bvjdlf9nubwk4k2/9dtm71258638968.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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