Multivariate regressie calculator

*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 01:45:43 -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/t1258620459ajv85vlig2k0iu3.htm/, Retrieved Thu, 19 Nov 2009 09:47:51 +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/t1258620459ajv85vlig2k0iu3.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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121.6 0 118.8 0 114.0 1 111.5 1 97.2 1 102.5 1 113.4 1 109.8 1 104.9 1 126.1 1 80.0 1 96.8 1 117.2 1 112.3 1 117.3 1 111.1 0 102.2 0 104.3 0 122.9 0 107.6 0 121.3 0 131.5 0 89.0 0 104.4 0 128.9 0 135.9 0 133.3 0 121.3 0 120.5 0 120.4 0 137.9 0 126.1 0 133.2 0 151.1 0 105.0 0 119.0 0 140.4 0 156.6 0 137.1 0 122.7 0 125.8 0 139.3 0 134.9 0 149.2 1 132.3 0 149.0 1 117.2 1 119.6 1 152.0 1 149.4 1 127.3 1 114.1 1 102.1 1 107.7 1 104.4 1 102.1 1 96.0 1 109.3 1 90.0 1 83.9 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Promet[t] = + 111.236666666667 -10.8277777777778Dummy[t] + 25.1144444444445M1[t] + 27.6944444444445M2[t] + 21.06M3[t] + 9.23444444444445M4[t] + 2.65444444444443M5[t] + 7.93444444444446M6[t] + 15.7944444444445M7[t] + 14.2200000000000M8[t] + 10.6344444444444M9[t] + 28.66M10[t] -8.5M11[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) 111.236666666667 6.575156 16.9177 0 0 Dummy -10.8277777777778 3.652864 -2.9642 0.004753 0.002377 M1 25.1144444444445 8.797262 2.8548 0.006389 0.003194 M2 27.6944444444445 8.797262 3.1481 0.002853 0.001427 M3 21.06 8.766874 2.4022 0.0203 0.01015 M4 9.23444444444445 8.797262 1.0497 0.299226 0.149613 M5 2.65444444444443 8.797262 0.3017 0.764185 0.382092 M6 7.93444444444446 8.797262 0.9019 0.371697 0.185849 M7 15.7944444444445 8.797262 1.7954 0.079024 0.039512 M8 14.2200000000000 8.766874 1.622 0.11149 0.055745 M9 10.6344444444444 8.797262 1.2088 0.232772 0.116386 M10 28.66 8.766874 3.2691 0.002022 0.001011 M11 -8.5 8.766874 -0.9696 0.33723 0.168615 Multiple Linear Regression - Regression Statistics Multiple R 0.711877431372855 R-squared 0.506769477298014 Adjusted R-squared 0.3808382800124 F-TEST (value) 4.02417739385622 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.00027256842434098 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.8616452992382 Sum Squared Residuals 9030.82488888888 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 121.6 136.351111111111 -14.7511111111110 2 118.8 138.931111111111 -20.1311111111111 3 114 121.468888888889 -7.46888888888888 4 111.5 109.643333333333 1.85666666666675 5 97.2 103.063333333333 -5.86333333333334 6 102.5 108.343333333333 -5.84333333333332 7 113.4 116.203333333333 -2.80333333333332 8 109.8 114.628888888889 -4.82888888888889 9 104.9 111.043333333333 -6.14333333333334 10 126.1 129.068888888889 -2.9688888888889 11 80 91.9088888888889 -11.9088888888889 12 96.8 100.408888888889 -3.60888888888889 13 117.2 125.523333333333 -8.32333333333335 14 112.3 128.103333333333 -15.8033333333333 15 117.3 121.468888888889 -4.16888888888889 16 111.1 120.471111111111 -9.37111111111114 17 102.2 113.891111111111 -11.6911111111111 18 104.3 119.171111111111 -14.8711111111111 19 122.9 127.031111111111 -4.13111111111112 20 107.6 125.456666666667 -17.8566666666667 21 121.3 121.871111111111 -0.571111111111111 22 131.5 139.896666666667 -8.39666666666667 23 89 102.736666666667 -13.7366666666667 24 104.4 111.236666666667 -6.83666666666667 25 128.9 136.351111111111 -7.45111111111114 26 135.9 138.931111111111 -3.03111111111111 27 133.3 132.296666666667 1.00333333333334 28 121.3 120.471111111111 0.828888888888866 29 120.5 113.891111111111 6.60888888888889 30 120.4 119.171111111111 1.22888888888888 31 137.9 127.031111111111 10.8688888888889 32 126.1 125.456666666667 0.64333333333334 33 133.2 121.871111111111 11.3288888888889 34 151.1 139.896666666667 11.2033333333333 35 105 102.736666666667 2.26333333333332 36 119 111.236666666667 7.76333333333333 37 140.4 136.351111111111 4.04888888888886 38 156.6 138.931111111111 17.6688888888889 39 137.1 132.296666666667 4.80333333333332 40 122.7 120.471111111111 2.22888888888887 41 125.8 113.891111111111 11.9088888888889 42 139.3 119.171111111111 20.1288888888889 43 134.9 127.031111111111 7.86888888888888 44 149.2 114.628888888889 34.5711111111111 45 132.3 121.871111111111 10.4288888888889 46 149 129.068888888889 19.9311111111111 47 117.2 91.9088888888889 25.2911111111111 48 119.6 100.408888888889 19.1911111111111 49 152 125.523333333333 26.4766666666666 50 149.4 128.103333333333 21.2966666666667 51 127.3 121.468888888889 5.83111111111111 52 114.1 109.643333333333 4.45666666666665 53 102.1 103.063333333333 -0.96333333333333 54 107.7 108.343333333333 -0.643333333333334 55 104.4 116.203333333333 -11.8033333333333 56 102.1 114.628888888889 -12.5288888888889 57 96 111.043333333333 -15.0433333333333 58 109.3 129.068888888889 -19.7688888888889 59 90 91.9088888888889 -1.90888888888889 60 83.9 100.408888888889 -16.5088888888889 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00627730467783559 0.0125546093556712 0.993722695322164 17 0.000957592689297852 0.00191518537859570 0.999042407310702 18 0.000163574425746034 0.000327148851492067 0.999836425574254 19 9.95659565424565e-05 0.000199131913084913 0.999900434043458 20 7.77568608440021e-05 0.000155513721688004 0.999922243139156 21 0.000324704862759156 0.000649409725518312 0.99967529513724 22 8.30708980192928e-05 0.000166141796038586 0.99991692910198 23 3.24381928596528e-05 6.48763857193056e-05 0.99996756180714 24 8.47280888509778e-06 1.69456177701956e-05 0.999991527191115 25 7.86357942518515e-06 1.57271588503703e-05 0.999992136420575 26 0.000252373442233009 0.000504746884466019 0.999747626557767 27 0.000228592562139244 0.000457185124278488 0.99977140743786 28 0.000107528852607018 0.000215057705214036 0.999892471147393 29 0.000298054398335791 0.000596108796671582 0.999701945601664 30 0.000313471732738198 0.000626943465476396 0.999686528267262 31 0.000429378070286089 0.000858756140572178 0.999570621929714 32 0.000428908587577764 0.000857817175155528 0.999571091412422 33 0.000478763994680649 0.000957527989361297 0.99952123600532 34 0.000554545530422375 0.00110909106084475 0.999445454469578 35 0.000572465663486971 0.00114493132697394 0.999427534336513 36 0.000358530468243616 0.000717060936487233 0.999641469531756 37 0.000650468877600800 0.00130093775520160 0.9993495311224 38 0.00309410034282985 0.0061882006856597 0.99690589965717 39 0.00178395813220162 0.00356791626440323 0.998216041867798 40 0.00112427310000700 0.00224854620001401 0.998875726899993 41 0.000700156831672304 0.00140031366334461 0.999299843168328 42 0.000808850568050072 0.00161770113610014 0.99919114943195 43 0.000282824626606991 0.000565649253213981 0.999717175373393 44 0.0136942420442648 0.0273884840885296 0.986305757955735 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 27 0.93103448275862 NOK 5% type I error level 29 1 NOK 10% type I error level 29 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/101l7a1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/101l7a1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/12k2e1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/12k2e1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/24cal1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/24cal1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/3yz5c1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/3yz5c1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/4158i1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/4158i1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/5yjq21258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/5yjq21258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/62avc1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/62avc1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/7ihcb1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/7ihcb1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/8idsy1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/8idsy1258620338.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/9vo2p1258620338.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258620459ajv85vlig2k0iu3/9vo2p1258620338.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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