*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: Tue, 21 Dec 2010 11:34:30 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd.htm/, Retrieved Tue, 21 Dec 2010 12:32:52 +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/2010/Dec/21/t1292931160jwk08u7kvwp7hrd.htm/}, year = {2010}, } @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 = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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101.76 101.82 107.34 93.63 99.85 102.37 101.68 107.34 93.63 99.91 102.38 101.68 107.34 93.63 99.87 102.86 102.45 107.34 96.13 99.86 102.87 102.45 107.34 96.13 100.10 102.92 102.45 107.34 96.13 100.10 102.95 102.45 107.34 96.13 100.12 103.02 102.45 107.34 96.13 99.95 104.08 102.45 112.60 96.13 99.94 104.16 102.52 112.60 96.13 100.18 104.24 102.52 112.60 96.13 100.31 104.33 102.85 112.60 96.13 100.65 104.73 102.85 112.61 96.13 100.65 104.86 102.85 112.61 96.13 100.69 105.03 103.25 112.61 96.13 101.26 105.62 103.25 112.61 98.73 101.26 105.63 103.25 112.61 98.73 101.38 105.63 103.25 112.61 98.73 101.38 105.94 104.45 112.61 98.73 101.38 106.61 104.45 112.61 98.73 101.44 107.69 104.45 118.65 98.73 101.40 107.78 104.80 118.65 98.73 101.40 107.93 104.80 118.65 98.73 100.58 108.48 105.29 118.65 98.73 100.58 108.14 105.29 114.29 98.73 100.58 108.48 105.29 114.29 98.73 100.59 108.48 105.29 114.29 98.73 100.81 108.89 106.04 114.29 101.67 100.75 108.93 105.94 114.29 101.67 100.75 109.21 105.94 etc...

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 28 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation HuurDVD[t] = + 83.1351258298301 -0.0934365930966516Cultuur[t] + 0.109135245038658Bioscoop[t] + 0.0869617581299381Schouwburg[t] + 0.0646540079085407EendagsA[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) 83.1351258298301 1.838159 45.2274 0 0 Cultuur -0.0934365930966516 0.09627 -0.9706 0.336171 0.168085 Bioscoop 0.109135245038658 0.028887 3.7781 0.000402 0.000201 Schouwburg 0.0869617581299381 0.039463 2.2036 0.031919 0.015959 EendagsA 0.0646540079085407 0.05414 1.1942 0.237718 0.118859 Multiple Linear Regression - Regression Statistics Multiple R 0.937045913686147 R-squared 0.878055044355907 Adjusted R-squared 0.868851651477107 F-TEST (value) 95.4055809546664 F-TEST (DF numerator) 4 F-TEST (DF denominator) 53 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.458486156322067 Sum Squared Residuals 11.1411064435661 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 99.85 100.127198644296 -0.277198644295529 2 99.91 100.054923388201 -0.144923388200880 3 99.87 100.05398902227 -0.183989022269906 4 99.86 100.254808616035 -0.394808616034638 5 100.1 100.253874250104 -0.153874250103675 6 100.1 100.249202420449 -0.149202420448842 7 100.12 100.246399322656 -0.126399322655933 8 99.95 100.239858761139 -0.289858761139169 9 99.94 100.598234820220 -0.658234820220197 10 100.18 100.598399359925 -0.418399359925161 11 100.31 100.590924432477 -0.280924432477434 12 100.65 100.618529769961 0.0314702300385115 13 100.65 100.582024750304 0.0679752496958729 14 100.69 100.569877993202 0.120122006798429 15 101.26 100.597647870391 0.662352129609404 16 101.26 100.710620701026 0.549379298974222 17 101.38 100.709686335095 0.670313664905178 18 101.38 100.709686335095 0.670313664905178 19 101.38 100.811683285281 0.568316714718751 20 101.44 100.749080767906 0.69091923209351 21 101.4 101.173418266467 0.226581733533075 22 101.4 101.203206308852 0.196793691148244 23 100.58 101.189190819887 -0.609190819887265 24 100.58 101.191276963753 -0.61127696375305 25 100.58 100.843892139959 -0.263892139959382 26 100.59 100.812123698307 -0.222123698306515 27 100.81 100.812123698307 -0.00212369830651655 28 100.75 101.045748912167 -0.295748912166995 29 100.75 101.031097923939 -0.281097923939262 30 100.96 101.004935677872 -0.0449356778722066 31 101.31 100.980642163667 0.329357836332932 32 101.64 100.986914071258 0.653085928741681 33 101.46 101.614542789084 -0.154542789084159 34 101.73 101.68081642189 0.0491835781100019 35 101.73 101.759267024541 -0.0292670245406264 36 101.64 101.765194746351 -0.125194746350822 37 101.77 101.763326014489 0.0066739855111056 38 101.74 101.759170463506 -0.0191704635056463 39 101.89 101.747023706403 0.142976293596923 40 101.89 102.092604916408 -0.202604916407770 41 101.93 102.084195623029 -0.154195623029064 42 101.93 102.039869890200 -0.109869890199809 43 102.32 102.041738622062 0.278261377938245 44 102.41 102.413636260089 -0.00363626008916682 45 103.58 103.075534563676 0.50446543632427 46 104.12 103.312619470698 0.807380529302485 47 104.1 103.326634959662 0.773365040337978 48 104.15 103.350809253276 0.79919074672451 49 104.15 103.362036750818 0.787963249181803 50 104.16 103.362851896158 0.79714810384248 51 102.94 103.375013759631 -0.435013759631192 52 103.07 103.495529909975 -0.425529909974759 53 103.04 103.500837238893 -0.460837238892813 54 103.06 103.525041745249 -0.465041745248511 55 103.05 103.502930935944 -0.452930935944159 56 102.95 103.474899958015 -0.524899958015158 57 102.95 103.721742832605 -0.771742832605233 58 103.05 103.690908756883 -0.640908756883344 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0289658713591797 0.0579317427183593 0.97103412864082 9 0.00746213064323157 0.0149242612864631 0.992537869356768 10 0.00216179362829080 0.00432358725658161 0.99783820637171 11 0.00063376116478035 0.0012675223295607 0.99936623883522 12 0.000282983811411905 0.000565967622823811 0.999717016188588 13 0.000106513456637936 0.000213026913275872 0.999893486543362 14 2.59918901128813e-05 5.19837802257625e-05 0.999974008109887 15 5.47678210960592e-06 1.09535642192118e-05 0.99999452321789 16 0.000770064060613216 0.00154012812122643 0.999229935939387 17 0.00119871914466579 0.00239743828933158 0.998801280855334 18 0.00087729561782457 0.00175459123564914 0.999122704382175 19 0.000778754016190083 0.00155750803238017 0.99922124598381 20 0.0021863964359172 0.0043727928718344 0.997813603564083 21 0.00230804787171213 0.00461609574342425 0.997691952128288 22 0.00159498883196763 0.00318997766393526 0.998405011168032 23 0.0422248428992797 0.0844496857985594 0.95777515710072 24 0.174225941550468 0.348451883100937 0.825774058449532 25 0.24101990674562 0.48203981349124 0.75898009325438 26 0.227601318898627 0.455202637797254 0.772398681101373 27 0.174439467581799 0.348878935163598 0.8255605324182 28 0.234329145405208 0.468658290810416 0.765670854594792 29 0.297638940656466 0.595277881312933 0.702361059343534 30 0.297691480171988 0.595382960343977 0.702308519828012 31 0.244284925505156 0.488569851010312 0.755715074494844 32 0.228159175735471 0.456318351470941 0.77184082426453 33 0.201929374191878 0.403858748383756 0.798070625808122 34 0.160903059470874 0.321806118941748 0.839096940529126 35 0.144052211664309 0.288104423328618 0.855947788335691 36 0.140024790897970 0.280049581795940 0.85997520910203 37 0.146776591626137 0.293553183252274 0.853223408373863 38 0.162806012368132 0.325612024736265 0.837193987631868 39 0.633276099769395 0.733447800461209 0.366723900230605 40 0.633062812935343 0.733874374129313 0.366937187064656 41 0.648388781821508 0.703222436356984 0.351611218178492 42 0.585919113484092 0.828161773031816 0.414080886515908 43 0.515764917684114 0.968470164631771 0.484235082315886 44 0.449894036454031 0.899788072908061 0.55010596354597 45 0.584384989232597 0.831230021534806 0.415615010767403 46 0.488499046882027 0.976998093764055 0.511500953117973 47 0.382325755874989 0.764651511749978 0.617674244125011 48 0.27816375170928 0.55632750341856 0.72183624829072 49 0.250508298317221 0.501016596634442 0.749491701682779 50 0.992493813993596 0.0150123720128081 0.00750618600640404 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.302325581395349 NOK 5% type I error level 15 0.348837209302326 NOK 10% type I error level 17 0.395348837209302 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/10psqt1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/10psqt1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/1b0sl1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/1b0sl1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/2b0sl1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/2b0sl1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/3b0sl1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/3b0sl1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/4maao1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/4maao1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/5maao1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/5maao1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/6e19r1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/6e19r1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/7e19r1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/7e19r1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/8psqt1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/8psqt1292931239.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/9psqt1292931239.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292931160jwk08u7kvwp7hrd/9psqt1292931239.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = no ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

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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