WS7(3)

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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 03:22:47 -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/t1258799017b68tfc408ifzued.htm/, Retrieved Sat, 21 Nov 2009 11:23:49 +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/t1258799017b68tfc408ifzued.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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8.1 10.9 7.7 10 7.5 9.2 7.6 9.2 7.8 9.5 7.8 9.6 7.8 9.5 7.5 9.1 7.5 8.9 7.1 9 7.5 10.1 7.5 10.3 7.6 10.2 7.7 9.6 7.7 9.2 7.9 9.3 8.1 9.4 8.2 9.4 8.2 9.2 8.2 9 7.9 9 7.3 9 6.9 9.8 6.6 10 6.7 9.8 6.9 9.3 7 9 7.1 9 7.2 9.1 7.1 9.1 6.9 9.1 7 9.2 6.8 8.8 6.4 8.3 6.7 8.4 6.6 8.1 6.4 7.7 6.3 7.9 6.2 7.9 6.5 8 6.8 7.9 6.8 7.6 6.4 7.1 6.1 6.8 5.8 6.5 6.1 6.9 7.2 8.2 7.3 8.7 6.9 8.3 6.1 7.9 5.8 7.5 6.2 7.8 7.1 8.3 7.7 8.4 7.9 8.2 7.7 7.7 7.4 7.2 7.5 7.3 8 8.1 8.1 8.5

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_Vrouwen[t] = + 7.38673263298069 + 0.4196382333358Werkl_Mannen[t] -0.10258803728638M1[t] -0.422645927350743M2[t] -0.724667640748687M3[t] -0.680973588814086M4[t] -0.607636124879781M5[t] -0.6419782496116M6[t] -0.77239272767626M7[t] -0.937628911740772M8[t] -1.08929403713842M9[t] -0.949351927202781M10[t] -0.252799992601909M11[t] -0.0360144632684767t + 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) 7.38673263298069 0.882223 8.3729 0 0 Werkl_Mannen 0.4196382333358 0.108377 3.872 0.000339 0.00017 M1 -0.10258803728638 0.294941 -0.3478 0.729557 0.364779 M2 -0.422645927350743 0.297106 -1.4225 0.161615 0.080807 M3 -0.724667640748687 0.298324 -2.4291 0.0191 0.00955 M4 -0.680973588814086 0.294082 -2.3156 0.025093 0.012546 M5 -0.607636124879781 0.292094 -2.0803 0.043098 0.021549 M6 -0.6419782496116 0.292536 -2.1945 0.033285 0.016642 M7 -0.77239272767626 0.291882 -2.6462 0.011104 0.005552 M8 -0.937628911740772 0.291328 -3.2185 0.002364 0.001182 M9 -1.08929403713842 0.291911 -3.7316 0.000522 0.000261 M10 -0.949351927202781 0.293879 -3.2304 0.002285 0.001143 M11 -0.252799992601909 0.29102 -0.8687 0.389538 0.194769 t -0.0360144632684767 0.003953 -9.1117 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.904201859845491 R-squared 0.817581003348045 Adjusted R-squared 0.766027808642058 F-TEST (value) 15.8589784398578 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 7.72271135929259e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.460092760998810 Sum Squared Residuals 9.73752604128135 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.9 10.6471998224458 0.252800177554192 2 10 10.1232721757786 -0.123272175778647 3 9.2 9.70130835244507 -0.50130835244507 4 9.2 9.75095176444477 -0.550951764444773 5 9.5 9.87220241177776 -0.372202411777762 6 9.6 9.80184582377747 -0.201845823777466 7 9.5 9.63541688244433 -0.135416882444329 8 9.1 9.3082747651106 -0.208274765110602 9 8.9 9.12059517644448 -0.220595176444477 10 9 9.05666752977732 -0.0566675297773168 11 10.1 9.88506029444403 0.214939705555966 12 10.3 10.1018458237775 0.198154176222535 13 10.2 10.0052071465562 0.19479285344381 14 9.6 9.69109861655693 -0.0910986165569288 15 9.2 9.3530624398905 -0.153062439890509 16 9.3 9.4446696752238 -0.144669675223792 17 9.4 9.56592032255678 -0.16592032255678 18 9.4 9.53752755789006 -0.137527557890064 19 9.2 9.37109861655693 -0.171098616556928 20 9 9.16984796922394 -0.169847969223940 21 9 8.85627691055708 0.143723089442923 22 9 8.70842161722276 0.291578382777243 23 9.8 9.20110379522083 0.598896204779168 24 10 9.29199785455353 0.708002145446474 25 9.8 9.19535917733225 0.604640822667752 26 9.3 8.92321447066657 0.376785529333432 27 9 8.62714211733373 0.372857882666272 28 9 8.67678552933343 0.323214470666568 29 9.1 8.75607235333284 0.343927646667159 30 9.1 8.64375194199896 0.456248058001036 31 9.1 8.39339535399867 0.706604646001332 32 9.2 8.23410852999926 0.96589147000074 33 8.8 7.96250129466598 0.837498705334024 34 8.3 7.89857364799882 0.401426352001184 35 8.4 8.68500258933195 -0.285002589331952 36 8.1 8.8598242953318 -0.759824295331805 37 7.7 8.63729414810979 -0.937294148109789 38 7.9 8.23925797144337 -0.339257971443368 39 7.9 7.85925797144337 0.0407420285566328 40 8 7.99282903011023 0.00717096988976843 41 7.9 8.1560435007768 -0.256043500776799 42 7.6 8.0856869127765 -0.485686912776504 43 7.1 7.75140267810905 -0.651402678109048 44 6.8 7.42426056077532 -0.62426056077532 45 6.5 7.11068950210846 -0.610689502108456 46 6.9 7.34050861877636 -0.440508618776356 47 8.2 8.46264814677813 -0.262648146778132 48 8.7 8.72139749944514 -0.0213974994451436 49 8.3 8.41493970555597 -0.114939705555966 50 7.9 7.72315676555449 0.176843234445513 51 7.5 7.25922911888733 0.240770881112673 52 7.8 7.43476400088777 0.365235999112229 53 8.3 7.84976141155582 0.450238588444182 54 8.4 8.031187763557 0.368812236442998 55 8.2 7.94868646889103 0.251313531108973 56 7.7 7.66350817489088 0.0364918251091230 57 7.2 7.34993711622401 -0.149937116224014 58 7.3 7.49582858622475 -0.195828586224754 59 8.1 8.36618517422505 -0.26618517422505 60 8.5 8.62493452689206 -0.124934526892061 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00730236631593619 0.0146047326318724 0.992697633684064 18 0.00558196564698079 0.0111639312939616 0.99441803435302 19 0.00421079582555247 0.00842159165110495 0.995789204174448 20 0.00216226129454762 0.00432452258909523 0.997837738705452 21 0.00123579162805974 0.00247158325611948 0.99876420837194 22 0.0005752570758167 0.0011505141516334 0.999424742924183 23 0.000363186087516210 0.000726372175032419 0.999636813912484 24 0.000292905158009509 0.000585810316019017 0.99970709484199 25 0.000154651226420498 0.000309302452840996 0.99984534877358 26 4.75697262196118e-05 9.51394524392236e-05 0.99995243027378 27 5.93322587777853e-05 0.000118664517555571 0.999940667741222 28 4.95175949780294e-05 9.90351899560588e-05 0.999950482405022 29 1.99749660869559e-05 3.99499321739117e-05 0.999980025033913 30 6.26152881613905e-06 1.25230576322781e-05 0.999993738471184 31 7.78773661908973e-06 1.55754732381795e-05 0.99999221226338 32 0.000252129175434248 0.000504258350868497 0.999747870824566 33 0.00185876586830969 0.00371753173661938 0.99814123413169 34 0.0557231348710124 0.111446269742025 0.944276865128988 35 0.70949905529625 0.5810018894075 0.29050094470375 36 0.934143729408794 0.131712541182412 0.0658562705912062 37 0.985089486888441 0.0298210262231174 0.0149105131115587 38 0.975983400952765 0.0480331980944704 0.0240165990472352 39 0.949433076178772 0.101133847642456 0.0505669238212278 40 0.899978058192927 0.200043883614146 0.100021941807073 41 0.902825530240147 0.194348939519706 0.0971744697598529 42 0.977295320584494 0.0454093588310114 0.0227046794155057 43 0.992680902802453 0.014638194395095 0.0073190971975475 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.555555555555556 NOK 5% type I error level 21 0.777777777777778 NOK 10% type I error level 21 0.777777777777778 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/104maj1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/104maj1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/1d0ex1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/1d0ex1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/2kfrt1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/2kfrt1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/3lyx81258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/3lyx81258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/4472n1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/4472n1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/5ql2t1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/5ql2t1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/62k6m1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/62k6m1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/7ao2o1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/7ao2o1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/8kekc1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/8kekc1258798962.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/9ll0s1258798962.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/21/t1258799017b68tfc408ifzued/9ll0s1258798962.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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