*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: Fri, 20 Nov 2009 01:23:13 -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/20/t12587054728lysygpi6xijj3t.htm/, Retrieved Fri, 20 Nov 2009 09:24:44 +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/20/t12587054728lysygpi6xijj3t.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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56.6 0 56 0 54.8 0 52.7 0 50.9 0 50.6 0 52.1 0 53.3 0 53.9 0 54.3 0 54.2 0 54.2 0 53.5 0 51.4 0 50.5 0 50.3 0 49.8 0 50.7 0 52.8 0 55.3 0 57.3 0 57.5 0 56.8 0 56.4 0 56.3 0 56.4 0 57 0 57.9 0 58.9 0 58.8 0 56.5 1 51.9 1 47.4 1 44.9 1 43.9 1 43.4 1 42.9 1 42.6 1 42.2 1 41.2 1 40.2 1 39.3 1 38.5 1 38.3 1 37.9 1 37.6 1 37.3 1 36 1 34.5 1 33.5 1 32.9 1 32.9 1 32.8 1 31.9 1 30.5 1 29.2 1 28.7 1 28.4 1 28 1 27.4 1 26.9 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 59.5323076923077 -7.21057692307693X[t] -0.712996794871812M1[t] -0.199326923076930M2[t] -0.373605769230770M3[t] -0.527884615384615M4[t] -0.682163461538468M5[t] -0.616442307692312M6[t] + 0.97139423076923M7[t] + 0.817115384615381M8[t] + 0.582836538461533M9[t] + 0.408557692307688M10[t] + 0.234278846153841M11[t] -0.325721153846153t + 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) 59.5323076923077 2.823153 21.0872 0 0 X -7.21057692307693 2.725502 -2.6456 0.011056 0.005528 M1 -0.712996794871812 3.168376 -0.225 0.822927 0.411464 M2 -0.199326923076930 3.327191 -0.0599 0.952483 0.476241 M3 -0.373605769230770 3.321148 -0.1125 0.910912 0.455456 M4 -0.527884615384615 3.316887 -0.1592 0.874232 0.437116 M5 -0.682163461538468 3.314416 -0.2058 0.837823 0.418911 M6 -0.616442307692312 3.313739 -0.186 0.853225 0.426612 M7 0.97139423076923 3.324947 0.2922 0.771455 0.385728 M8 0.817115384615381 3.316887 0.2464 0.806484 0.403242 M9 0.582836538461533 3.310606 0.1761 0.86101 0.430505 M10 0.408557692307688 3.306111 0.1236 0.902178 0.451089 M11 0.234278846153841 3.303412 0.0709 0.943762 0.471881 t -0.325721153846153 0.07712 -4.2235 0.000109 5.5e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.890912068408306 R-squared 0.793724313635566 Adjusted R-squared 0.736669336556041 F-TEST (value) 13.9115701077095 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 5.62927482405939e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.22172889694719 Sum Squared Residuals 1281.52327564103 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 56.6 58.4935897435898 -1.8935897435898 2 56 58.6815384615385 -2.68153846153848 3 54.8 58.1815384615385 -3.38153846153846 4 52.7 57.7015384615385 -5.00153846153845 5 50.9 57.2215384615385 -6.32153846153846 6 50.6 56.9615384615385 -6.36153846153846 7 52.1 58.2236538461538 -6.12365384615384 8 53.3 57.7436538461538 -4.44365384615384 9 53.9 57.1836538461538 -3.28365384615384 10 54.3 56.6836538461538 -2.38365384615385 11 54.2 56.1836538461538 -1.98365384615384 12 54.2 55.6236538461539 -1.42365384615385 13 53.5 54.5849358974359 -1.08493589743588 14 51.4 54.7728846153846 -3.3728846153846 15 50.5 54.2728846153846 -3.77288461538461 16 50.3 53.7928846153846 -3.49288461538462 17 49.8 53.3128846153846 -3.51288461538461 18 50.7 53.0528846153846 -2.35288461538461 19 52.8 54.315 -1.51500000000000 20 55.3 53.835 1.46500000000000 21 57.3 53.275 4.025 22 57.5 52.775 4.725 23 56.8 52.275 4.525 24 56.4 51.715 4.68499999999999 25 56.3 50.676282051282 5.62371794871796 26 56.4 50.8642307692308 5.53576923076923 27 57 50.3642307692308 6.63576923076923 28 57.9 49.8842307692308 8.01576923076922 29 58.9 49.4042307692308 9.49576923076923 30 58.8 49.1442307692308 9.65576923076923 31 56.5 43.1957692307692 13.3042307692308 32 51.9 42.7157692307692 9.18423076923077 33 47.4 42.1557692307692 5.24423076923077 34 44.9 41.6557692307692 3.24423076923077 35 43.9 41.1557692307692 2.74423076923077 36 43.4 40.5957692307692 2.80423076923077 37 42.9 39.5570512820513 3.34294871794873 38 42.6 39.745 2.85500000000000 39 42.2 39.245 2.95500000000000 40 41.2 38.765 2.435 41 40.2 38.285 1.91500000000001 42 39.3 38.025 1.27500000000000 43 38.5 39.2871153846154 -0.787115384615385 44 38.3 38.8071153846154 -0.507115384615387 45 37.9 38.2471153846154 -0.347115384615384 46 37.6 37.7471153846154 -0.147115384615383 47 37.3 37.2471153846154 0.0528846153846132 48 36 36.6871153846154 -0.68711538461539 49 34.5 35.6483974358974 -1.14839743589742 50 33.5 35.8363461538462 -2.33634615384615 51 32.9 35.3363461538462 -2.43634615384616 52 32.9 34.8563461538462 -1.95634615384616 53 32.8 34.3763461538462 -1.57634615384616 54 31.9 34.1163461538462 -2.21634615384616 55 30.5 35.3784615384615 -4.87846153846154 56 29.2 34.8984615384615 -5.69846153846154 57 28.7 34.3384615384615 -5.63846153846154 58 28.4 33.8384615384615 -5.43846153846155 59 28 33.3384615384615 -5.33846153846154 60 27.4 32.7784615384615 -5.37846153846155 61 26.9 31.7397435897436 -4.83974358974358 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0483488559345309 0.0966977118690618 0.95165114406547 18 0.0881075550456606 0.176215110091321 0.91189244495434 19 0.181295369832739 0.362590739665477 0.818704630167261 20 0.350919507987495 0.701839015974991 0.649080492012505 21 0.529734498276983 0.940531003446035 0.470265501723017 22 0.595748985459407 0.808502029081187 0.404251014540593 23 0.616085370667871 0.767829258664258 0.383914629332129 24 0.622665214468901 0.754669571062197 0.377334785531099 25 0.610822301166607 0.778355397666786 0.389177698833393 26 0.610632312469551 0.778735375060899 0.389367687530449 27 0.656447364275127 0.687105271449745 0.343552635724873 28 0.74947833017448 0.50104333965104 0.25052166982552 29 0.849864762706242 0.300270474587516 0.150135237293758 30 0.868067390972918 0.263865218054163 0.131932609027082 31 0.997634103306655 0.00473179338668961 0.00236589669334480 32 0.999999092262164 1.81547567208814e-06 9.0773783604407e-07 33 0.999999899103557 2.01792885263215e-07 1.00896442631608e-07 34 0.999999868452973 2.63094055117641e-07 1.31547027558821e-07 35 0.999999892562385 2.14875230803867e-07 1.07437615401934e-07 36 0.999999805774392 3.88451216032274e-07 1.94225608016137e-07 37 0.999999439089218 1.12182156397282e-06 5.60910781986408e-07 38 0.999997748490339 4.50301932283994e-06 2.25150966141997e-06 39 0.999992379649404 1.52407011921703e-05 7.62035059608513e-06 40 0.99995744890651 8.51021869808447e-05 4.25510934904223e-05 41 0.99988780953477 0.000224380930460671 0.000112190465230336 42 0.999785319417124 0.000429361165752509 0.000214680582876255 43 0.999320921971202 0.00135815605759561 0.000679078028797807 44 0.995780306227428 0.00843938754514336 0.00421969377257168 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.5 NOK 5% type I error level 14 0.5 NOK 10% type I error level 15 0.535714285714286 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/10ic0e1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/10ic0e1258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/1g5cn1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/1g5cn1258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/2ivih1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/2ivih1258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/35awt1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/35awt1258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/4dkct1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/4dkct1258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/57l661258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/57l661258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/6s7m21258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/6s7m21258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/73k071258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/73k071258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/8ci7m1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/8ci7m1258705389.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/93bpd1258705389.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587054728lysygpi6xijj3t/93bpd1258705389.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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