oilpricemulregr

*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, 30 Nov 2010 20:36:28 +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/Nov/30/t1291149703ds3h3xjdskkyt3e.htm/, Retrieved Tue, 30 Nov 2010 21:41:53 +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/Nov/30/t1291149703ds3h3xjdskkyt3e.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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46.85 48.05 54.63 53.22 49.87 56.42 59.03 64.99 65.55 62.27 58.34 59.45 65.54 61.93 62.97 70.16 70.96 70.97 74.46 73.08 63.90 59.14 59.40 62.09 54.35 59.39 60.74 64.04 63.53 67.53 74.15 72.36 79.63 85.66 94.63 91.74 92.93 95.35 105.42 112.46 125.46 134.02 133.48 116.69 103.76 76.72 57.44 42.04 41.92 39.26 48.06 49.95 59.21 69.70 64.29 71.14 69.47 75.82 78.15 74.60

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 6 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Crudeoilprice[t] = + 52.80975 -1.64053472222217M1[t] -1.52848611111111M2[t] + 3.6735625M3[t] + 6.90961111111112M4[t] + 10.3836597222222M5[t] + 15.9397083333333M6[t] + 16.9277569444445M7[t] + 15.1318055555556M8[t] + 11.5758541666667M9[t] + 6.6699027777778M10[t] + 3.9739513888889M11[t] + 0.365951388888888t + 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) 52.80975 11.467838 4.605 3.2e-05 1.6e-05 M1 -1.64053472222217 13.951267 -0.1176 0.906893 0.453447 M2 -1.52848611111111 13.930422 -0.1097 0.913096 0.456548 M3 3.6735625 13.911536 0.2641 0.792884 0.396442 M4 6.90961111111112 13.894616 0.4973 0.621305 0.310653 M5 10.3836597222222 13.87967 0.7481 0.458115 0.229058 M6 15.9397083333333 13.866704 1.1495 0.256168 0.128084 M7 16.9277569444445 13.855722 1.2217 0.227908 0.113954 M8 15.1318055555556 13.846731 1.0928 0.280048 0.140024 M9 11.5758541666667 13.839734 0.8364 0.40715 0.203575 M10 6.6699027777778 13.834734 0.4821 0.631963 0.315982 M11 3.9739513888889 13.831733 0.2873 0.77514 0.38757 t 0.365951388888888 0.166358 2.1998 0.032775 0.016388 Multiple Linear Regression - Regression Statistics Multiple R 0.430197138182165 R-squared 0.185069577700125 Adjusted R-squared -0.0229977641636727 F-TEST (value) 0.889469611339932 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.563345577233602 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 21.8683087123745 Sum Squared Residuals 22476.4775191667 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 46.85 51.5351666666665 -4.68516666666653 2 48.05 52.0131666666667 -3.96316666666669 3 54.63 57.5811666666667 -2.95116666666668 4 53.22 61.1831666666666 -7.96316666666663 5 49.87 65.0231666666667 -15.1531666666667 6 56.42 70.9451666666667 -14.5251666666667 7 59.03 72.2991666666667 -13.2691666666667 8 64.99 70.8691666666667 -5.87916666666668 9 65.55 67.6791666666667 -2.12916666666667 10 62.27 63.1391666666667 -0.869166666666666 11 58.34 60.8091666666667 -2.46916666666667 12 59.45 57.2011666666667 2.24883333333334 13 65.54 55.9265833333334 9.61341666666665 14 61.93 56.4045833333333 5.52541666666666 15 62.97 61.9725833333333 0.99741666666666 16 70.16 65.5745833333333 4.58541666666666 17 70.96 69.4145833333333 1.54541666666666 18 70.97 75.3365833333333 -4.36658333333334 19 74.46 76.6905833333333 -2.23058333333335 20 73.08 75.2605833333333 -2.18058333333334 21 63.9 72.0705833333333 -8.17058333333334 22 59.14 67.5305833333333 -8.39058333333334 23 59.4 65.2005833333333 -5.80058333333334 24 62.09 61.5925833333333 0.497416666666681 25 54.35 60.318 -5.96800000000004 26 59.39 60.796 -1.40600000000000 27 60.74 66.364 -5.624 28 64.04 69.966 -5.92599999999999 29 63.53 73.806 -10.276 30 67.53 79.728 -12.198 31 74.15 81.082 -6.932 32 72.36 79.652 -7.292 33 79.63 76.462 3.168 34 85.66 71.922 13.738 35 94.63 69.592 25.038 36 91.74 65.984 25.756 37 92.93 64.7094166666667 28.2205833333333 38 95.35 65.1874166666667 30.1625833333333 39 105.42 70.7554166666667 34.6645833333333 40 112.46 74.3574166666667 38.1025833333333 41 125.46 78.1974166666667 47.2625833333333 42 134.02 84.1194166666667 49.9005833333334 43 133.48 85.4734166666667 48.0065833333333 44 116.69 84.0434166666667 32.6465833333333 45 103.76 80.8534166666667 22.9065833333333 46 76.72 76.3134166666667 0.406583333333334 47 57.44 73.9834166666667 -16.5434166666667 48 42.04 70.3754166666667 -28.3354166666667 49 41.92 69.1008333333334 -27.1808333333334 50 39.26 69.5788333333333 -30.3188333333333 51 48.06 75.1468333333333 -27.0868333333333 52 49.95 78.7488333333333 -28.7988333333333 53 59.21 82.5888333333333 -23.3788333333333 54 69.7 88.5108333333333 -18.8108333333333 55 64.29 89.8648333333333 -25.5748333333333 56 71.14 88.4348333333333 -17.2948333333333 57 69.47 85.2448333333333 -15.7748333333333 58 75.82 80.7048333333333 -4.88483333333333 59 78.15 78.3748333333333 -0.224833333333324 60 74.6 74.7668333333333 -0.166833333333314 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00263053886931193 0.00526107773862387 0.997369461130688 17 0.00063186857559217 0.00126373715118434 0.999368131424408 18 7.34641743466015e-05 0.000146928348693203 0.999926535825653 19 7.56746256307722e-06 1.51349251261544e-05 0.999992432537437 20 2.88995603352467e-06 5.77991206704933e-06 0.999997110043966 21 1.3740556288444e-05 2.7481112576888e-05 0.999986259443712 22 1.82225554028152e-05 3.64451108056304e-05 0.999981777444597 23 7.52575243867712e-06 1.50515048773542e-05 0.999992474247561 24 2.22725955859776e-06 4.45451911719552e-06 0.999997772740441 25 2.76103348583162e-06 5.52206697166324e-06 0.999997238966514 26 8.20603256876916e-07 1.64120651375383e-06 0.999999179396743 27 3.06150426693875e-07 6.12300853387751e-07 0.999999693849573 28 1.05196928216101e-07 2.10393856432202e-07 0.999999894803072 29 4.60776990022425e-08 9.21553980044849e-08 0.999999953922301 30 3.04172723222194e-08 6.08345446444388e-08 0.999999969582728 31 1.92152636098083e-08 3.84305272196167e-08 0.999999980784736 32 2.11392849930122e-08 4.22785699860244e-08 0.999999978860715 33 3.03014407716362e-08 6.06028815432723e-08 0.99999996969856 34 1.60663035611125e-07 3.21326071222249e-07 0.999999839336964 35 2.62510739550891e-06 5.25021479101782e-06 0.999997374892605 36 5.25595625307418e-06 1.05119125061484e-05 0.999994744043747 37 8.52552973774598e-06 1.70510594754920e-05 0.999991474470262 38 1.18880723280486e-05 2.37761446560972e-05 0.999988111927672 39 2.7223710540691e-05 5.4447421081382e-05 0.99997277628946 40 7.76653677387382e-05 0.000155330735477476 0.99992233463226 41 0.000714205485848743 0.00142841097169749 0.99928579451415 42 0.00487908051233727 0.00975816102467455 0.995120919487663 43 0.0444397785160251 0.0888795570320501 0.955560221483975 44 0.118399872576986 0.236799745153973 0.881600127423014 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 27 0.93103448275862 NOK 10% type I error level 28 0.96551724137931 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/1022ng1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/1022ng1291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/1e1qm1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/1e1qm1291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/2osp71291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/2osp71291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/3osp71291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/3osp71291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/4osp71291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/4osp71291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/5z2ps1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/5z2ps1291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/6z2ps1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/6z2ps1291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/7sb6d1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/7sb6d1291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/8sb6d1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/8sb6d1291149381.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/922ng1291149381.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/30/t1291149703ds3h3xjdskkyt3e/922ng1291149381.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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