cs.shw.ws7.v2

*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, 26 Nov 2009 09:07:10 -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/26/t125925190026zba80duvrixrw.htm/, Retrieved Thu, 26 Nov 2009 17:11:52 +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/26/t125925190026zba80duvrixrw.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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8.9 1.9 9 1.6 9 1.7 9 2 9 2.5 9 2.4 9 2.3 9 2.3 9 2.1 9 2.4 9 2.2 9.1 2.4 9 1.9 9 2.1 9.1 2.1 9 2.1 9 2 9 2.1 9 2.2 8.9 2.2 8.9 2.6 8.9 2.5 8.9 2.3 8.8 2.2 8.8 2.4 8.7 2.3 8.7 2.2 8.5 2.5 8.5 2.5 8.4 2.5 8.2 2.4 8.2 2.3 8.1 1.7 8.1 1.6 8 1.9 7.9 1.9 7.8 1.8 7.7 1.8 7.6 1.9 7.5 1.9 7.5 1.9 7.5 1.9 7.5 1.8 7.5 1.7 7.4 2.1 7.4 2.6 7.3 3.1 7.3 3.1 7.3 3.2 7.2 3.3 7.2 3.6 7.3 3.3 7.4 3.7 7.4 4 7.5 4 7.6 3.8 7.7 3.6 7.9 3.2 8 2.1 8.2 1.6

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 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 werkl[t] = + 9.34448245145198 -0.484143951541065`infl `[t] + 0.0999999999999908M1[t] + 0.0503171209691788M2[t] + 0.0890486370924635M3[t] + 0.0580972741849276M4[t] + 0.155560306431498M5[t] + 0.164608943523962M6[t] + 0.125243185462319M7[t] + 0.0865116693390339M8[t] + 0.0471459112773918M9[t] + 0.106511669339034M10[t] + 0.0187315161232850M11[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) 9.34448245145198 0.450109 20.7605 0 0 `infl ` -0.484143951541065 0.145973 -3.3167 0.001763 0.000881 M1 0.0999999999999908 0.437453 0.2286 0.820174 0.410087 M2 0.0503171209691788 0.437462 0.115 0.908919 0.454459 M3 0.0890486370924635 0.43754 0.2035 0.839606 0.419803 M4 0.0580972741849276 0.437803 0.1327 0.894996 0.447498 M5 0.155560306431498 0.439358 0.3541 0.724875 0.362438 M6 0.164608943523962 0.440259 0.3739 0.710167 0.355083 M7 0.125243185462319 0.439639 0.2849 0.77699 0.388495 M8 0.0865116693390339 0.43863 0.1972 0.844497 0.422248 M9 0.0471459112773918 0.438241 0.1076 0.914787 0.457393 M10 0.106511669339034 0.43863 0.2428 0.809195 0.404598 M11 0.0187315161232850 0.437608 0.0428 0.966039 0.483019 Multiple Linear Regression - Regression Statistics Multiple R 0.438622344152539 R-squared 0.192389560789868 Adjusted R-squared -0.0138088492212289 F-TEST (value) 0.933031252663462 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.523093765381267 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.691673198671604 Sum Squared Residuals 22.4853552467486 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.524608943524 0.375391056476 2 9 8.62016924995546 0.379830750044541 3 9 8.61048637092464 0.389513629075362 4 9 8.43429182255478 0.565708177445217 5 9 8.28968287903082 0.710317120969179 6 9 8.3471459112774 0.652854088722608 7 9 8.35619454836986 0.643805451630144 8 9 8.31746303224657 0.682536967753429 9 9 8.37492606449314 0.625073935506859 10 9 8.28904863709246 0.710951362907536 11 9 8.29809727418493 0.701902725815072 12 9.1 8.18253696775343 0.91746303224657 13 9 8.52460894352395 0.475391056476048 14 9 8.37809727418493 0.621902725815072 15 9.1 8.41682879030821 0.683171209691787 16 9 8.38587742740068 0.614122572599323 17 9 8.53175485480135 0.468245145198646 18 9 8.49238909673971 0.507610903260289 19 9 8.40460894352396 0.595391056476038 20 8.9 8.36587742740068 0.534122572599324 21 8.9 8.13285408872261 0.767145911277392 22 8.9 8.24063424193836 0.659365758061643 23 8.9 8.24968287903082 0.650317120969179 24 8.8 8.27936575806164 0.520634241938358 25 8.8 8.28253696775342 0.51746303224658 26 8.7 8.28126848387672 0.418731516123284 27 8.7 8.3684143951541 0.331585604845893 28 8.5 8.19221984678425 0.307780153215749 29 8.5 8.28968287903082 0.210317120969178 30 8.4 8.29873151612328 0.101268483876715 31 8.2 8.30778015321575 -0.10778015321575 32 8.2 8.31746303224657 -0.117463032246571 33 8.1 8.56858364510957 -0.468583645109567 34 8.1 8.67636379832532 -0.576363798325316 35 8 8.44334045964725 -0.443340459647247 36 7.9 8.42460894352396 -0.524608943523962 37 7.8 8.57302333867806 -0.77302333867806 38 7.7 8.52334045964725 -0.823340459647247 39 7.6 8.51365758061642 -0.913657580616426 40 7.5 8.48270621770889 -0.98270621770889 41 7.5 8.58016924995546 -1.08016924995546 42 7.5 8.58921788704792 -1.08921788704792 43 7.5 8.59826652414039 -1.09826652414039 44 7.5 8.6079494031712 -1.10794940317121 45 7.4 8.37492606449314 -0.97492606449314 46 7.4 8.19221984678425 -0.79221984678425 47 7.3 7.86236771779797 -0.56236771779797 48 7.3 7.84363620167469 -0.543636201674685 49 7.3 7.89522180652057 -0.595221806520569 50 7.2 7.79712453233565 -0.59712453233565 51 7.2 7.69061286299662 -0.490612862996616 52 7.3 7.8049046855514 -0.504904685551399 53 7.4 7.70871013718154 -0.308710137181544 54 7.4 7.57251558881169 -0.172515588811688 55 7.5 7.53314983075005 -0.0331498307500458 56 7.6 7.59124710493497 0.0087528950650263 57 7.7 7.64871013718154 0.0512898628184562 58 7.9 7.90173347585961 -0.00173347585961171 59 8 8.34651166933903 -0.346511669339034 60 8.2 8.56985212898628 -0.369852128986282 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000697989967363515 0.00139597993472703 0.999302010032636 17 5.78564290888853e-05 0.000115712858177771 0.99994214357091 18 4.23915078926217e-06 8.47830157852434e-06 0.99999576084921 19 3.08190685036631e-07 6.16381370073261e-07 0.999999691809315 20 8.79520420503894e-08 1.75904084100779e-07 0.999999912047958 21 4.27369409037339e-08 8.54738818074678e-08 0.99999995726306 22 1.40184697940510e-08 2.80369395881021e-08 0.99999998598153 23 5.14919880119734e-09 1.02983976023947e-08 0.999999994850801 24 1.16246869537657e-07 2.32493739075314e-07 0.99999988375313 25 1.23130353760903e-07 2.46260707521805e-07 0.999999876869646 26 7.7283795597078e-07 1.54567591194156e-06 0.999999227162044 27 7.19839503419977e-06 1.43967900683995e-05 0.999992801604966 28 0.000121403844401110 0.000242807688802221 0.999878596155599 29 0.00158687446096917 0.00317374892193834 0.99841312553903 30 0.0181216007853252 0.0362432015706505 0.981878399214675 31 0.144898410331370 0.289796820662739 0.85510158966863 32 0.384541631498602 0.769083262997205 0.615458368501398 33 0.7208263037632 0.5583473924736 0.2791736962368 34 0.799858593784743 0.400282812430514 0.200141406215257 35 0.843728962428615 0.312542075142769 0.156271037571385 36 0.854508403458303 0.290983193083394 0.145491596541697 37 0.883122114887575 0.233755770224850 0.116877885112425 38 0.904681476611247 0.190637046777507 0.0953185233887533 39 0.913992660992047 0.172014678015906 0.0860073390079531 40 0.891979601912467 0.216040796175066 0.108020398087533 41 0.843883149251065 0.312233701497870 0.156116850748935 42 0.766015335470025 0.467969329059951 0.233984664529975 43 0.654218821352735 0.69156235729453 0.345781178647265 44 0.542955838771419 0.914088322457163 0.457044161228581 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.482758620689655 NOK 5% type I error level 15 0.517241379310345 NOK 10% type I error level 15 0.517241379310345 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/10jyxu1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/10jyxu1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/1pkss1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/1pkss1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/20kon1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/20kon1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/3zafx1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/3zafx1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/4ngy91259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/4ngy91259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/53lvv1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/53lvv1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/6vyhx1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/6vyhx1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/7zczl1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/7zczl1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/8jphy1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/8jphy1259251626.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/9xezp1259251626.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/26/t125925190026zba80duvrixrw/9xezp1259251626.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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