*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 07:08:59 -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/t1258726163o34ybi9p0vov5ha.htm/, Retrieved Fri, 20 Nov 2009 15:09:35 +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/t1258726163o34ybi9p0vov5ha.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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22 0 22 0 20 0 21 0 20 0 21 0 21 0 21 0 19 0 21 0 21 0 22 0 19 0 24 0 22 0 22 0 22 0 24 0 22 0 23 0 24 0 21 0 20 0 22 0 23 0 23 0 22 0 20 0 21 1 21 1 20 1 20 1 17 1 18 1 19 1 19 1 20 1 21 1 20 1 21 1 19 1 22 1 20 1 18 1 16 1 17 1 18 1 19 1 18 1 20 1 21 1 18 1 19 1 19 1 19 1 21 1 19 1 19 1 17 1 16 1 16 1 17 1 16 1 15 1 16 1 16 1 16 1 18 1 19 1 16 1 16 1 16 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 Y[t] = + 21.1 -3.15000000000001X[t] + 0.141666666666680M1[t] + 1.64166666666667M2[t] + 0.641666666666665M3[t] -0.0250000000000011M4[t] + 0.499999999999997M5[t] + 1.5M6[t] + 0.666666666666666M7[t] + 1.16666666666666M8[t] -1.24298986312465e-15M9[t] -0.333333333333334M10[t] -0.500000000000001M11[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) 21.1 0.744443 28.3434 0 0 X -3.15000000000001 0.414719 -7.5955 0 0 M1 0.141666666666680 0.979942 0.1446 0.885546 0.442773 M2 1.64166666666667 0.979942 1.6753 0.099174 0.049587 M3 0.641666666666665 0.979942 0.6548 0.515141 0.25757 M4 -0.0250000000000011 0.979942 -0.0255 0.979733 0.489866 M5 0.499999999999997 0.977501 0.5115 0.610904 0.305452 M6 1.5 0.977501 1.5345 0.130246 0.065123 M7 0.666666666666666 0.977501 0.682 0.497901 0.24895 M8 1.16666666666666 0.977501 1.1935 0.237443 0.118722 M9 -1.24298986312465e-15 0.977501 0 1 0.5 M10 -0.333333333333334 0.977501 -0.341 0.734311 0.367155 M11 -0.500000000000001 0.977501 -0.5115 0.610904 0.305452 Multiple Linear Regression - Regression Statistics Multiple R 0.740843854357209 R-squared 0.548849616538846 Adjusted R-squared 0.457090216512849 F-TEST (value) 5.98139935944814 F-TEST (DF numerator) 12 F-TEST (DF denominator) 59 p-value 1.09294272232496e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.69308163528189 Sum Squared Residuals 169.125 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 22 21.2416666666666 0.758333333333405 2 22 22.7416666666667 -0.741666666666658 3 20 21.7416666666667 -1.74166666666667 4 21 21.075 -0.0750000000000007 5 20 21.6 -1.60000000000000 6 21 22.6 -1.6 7 21 21.7666666666667 -0.766666666666667 8 21 22.2666666666667 -1.26666666666667 9 19 21.1 -2.1 10 21 20.7666666666667 0.233333333333332 11 21 20.6 0.399999999999997 12 22 21.1 0.899999999999997 13 19 21.2416666666667 -2.24166666666668 14 24 22.7416666666667 1.25833333333333 15 22 21.7416666666667 0.258333333333333 16 22 21.075 0.924999999999997 17 22 21.6 0.399999999999999 18 24 22.6 1.40000000000000 19 22 21.7666666666667 0.233333333333331 20 23 22.2666666666667 0.733333333333333 21 24 21.1 2.9 22 21 20.7666666666667 0.233333333333331 23 20 20.6 -0.600000000000002 24 22 21.1 0.899999999999997 25 23 21.2416666666667 1.75833333333332 26 23 22.7416666666667 0.25833333333333 27 22 21.7416666666667 0.258333333333333 28 20 21.075 -1.07500000000000 29 21 18.45 2.55 30 21 19.45 1.55 31 20 18.6166666666667 1.38333333333333 32 20 19.1166666666667 0.883333333333335 33 17 17.95 -0.949999999999998 34 18 17.6166666666667 0.383333333333334 35 19 17.45 1.55 36 19 17.95 1.05 37 20 18.0916666666667 1.90833333333332 38 21 19.5916666666667 1.40833333333333 39 20 18.5916666666667 1.40833333333334 40 21 17.925 3.075 41 19 18.45 0.550000000000001 42 22 19.45 2.55 43 20 18.6166666666667 1.38333333333333 44 18 19.1166666666667 -1.11666666666666 45 16 17.95 -1.95 46 17 17.6166666666667 -0.616666666666666 47 18 17.45 0.550000000000001 48 19 17.95 1.05 49 18 18.0916666666667 -0.0916666666666789 50 20 19.5916666666667 0.408333333333333 51 21 18.5916666666667 2.40833333333334 52 18 17.925 0.0750000000000006 53 19 18.45 0.550000000000001 54 19 19.45 -0.449999999999999 55 19 18.6166666666667 0.383333333333334 56 21 19.1166666666667 1.88333333333334 57 19 17.95 1.05000000000000 58 19 17.6166666666667 1.38333333333333 59 17 17.45 -0.449999999999999 60 16 17.95 -1.95 61 16 18.0916666666667 -2.09166666666668 62 17 19.5916666666667 -2.59166666666667 63 16 18.5916666666667 -2.59166666666666 64 15 17.925 -2.925 65 16 18.45 -2.45 66 16 19.45 -3.45 67 16 18.6166666666667 -2.61666666666667 68 18 19.1166666666667 -1.11666666666666 69 19 17.95 1.05000000000000 70 16 17.6166666666667 -1.61666666666667 71 16 17.45 -1.45 72 16 17.95 -1.95 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.570564699526603 0.858870600946795 0.429435300473397 17 0.494451888661285 0.98890377732257 0.505548111338715 18 0.529039689708414 0.941920620583172 0.470960310291586 19 0.411438566863586 0.822877133727173 0.588561433136414 20 0.357035863078195 0.71407172615639 0.642964136921805 21 0.61990942089022 0.760181158219561 0.380090579109781 22 0.511182700597037 0.977634598805926 0.488817299402963 23 0.421805740121019 0.843611480242038 0.578194259878981 24 0.32741392537055 0.6548278507411 0.67258607462945 25 0.339378124319997 0.678756248639994 0.660621875680003 26 0.258075658550901 0.516151317101801 0.7419243414491 27 0.198707822444359 0.397415644888719 0.80129217755564 28 0.159039455928126 0.318078911856253 0.840960544071874 29 0.132357056673552 0.264714113347104 0.867642943326448 30 0.106090093816294 0.212180187632587 0.893909906183706 31 0.0777429631830008 0.155485926366002 0.922257036817 32 0.0546413216169656 0.109282643233931 0.945358678383034 33 0.0656704426985962 0.131340885397192 0.934329557301404 34 0.0457934642218891 0.0915869284437783 0.954206535778111 35 0.0333764383883581 0.0667528767767162 0.966623561611642 36 0.0251796220413571 0.0503592440827141 0.974820377958643 37 0.0217909786640965 0.043581957328193 0.978209021335904 38 0.0164140517884582 0.0328281035769164 0.983585948211542 39 0.0108536549725292 0.0217073099450583 0.98914634502747 40 0.0216093472894519 0.0432186945789038 0.978390652710548 41 0.0150482032451810 0.0300964064903619 0.984951796754819 42 0.0291400478416725 0.0582800956833451 0.970859952158327 43 0.0253224868628918 0.0506449737257836 0.974677513137108 44 0.0248758723481227 0.0497517446962453 0.975124127651877 45 0.0436124741550672 0.0872249483101344 0.956387525844933 46 0.032124076066876 0.064248152133752 0.967875923933124 47 0.0225835867244914 0.0451671734489827 0.977416413275509 48 0.0256486579235899 0.0512973158471799 0.97435134207641 49 0.0209491098518246 0.0418982197036492 0.979050890148175 50 0.0214551762144137 0.0429103524288274 0.978544823785586 51 0.0784121248314467 0.156824249662893 0.921587875168553 52 0.0985789946416548 0.197157989283310 0.901421005358345 53 0.121542043474729 0.243084086949458 0.87845795652527 54 0.176481068659110 0.352962137318220 0.82351893134089 55 0.250031277563079 0.500062555126158 0.749968722436921 56 0.412380518336263 0.824761036672525 0.587619481663737 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 9 0.219512195121951 NOK 10% type I error level 17 0.414634146341463 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/10fw081258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/10fw081258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/162al1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/162al1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/20i1k1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/20i1k1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/361md1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/361md1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/4k1721258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/4k1721258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/5i8iu1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/5i8iu1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/6sy4m1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/6sy4m1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/70l4h1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/70l4h1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/8ohdk1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/8ohdk1258726134.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/9n2lh1258726134.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726163o34ybi9p0vov5ha/9n2lh1258726134.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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