SHWWS7model1b

*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 09:07:07 -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/t12586468853icbaux6fs2r2ft.htm/, Retrieved Thu, 19 Nov 2009 17:08:17 +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/t12586468853icbaux6fs2r2ft.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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25.60 161 23.70 149 22.00 139 21.30 135 20.70 130 20.40 127 20.30 122 20.40 117 19.80 112 19.50 113 23.10 149 23.50 157 23.50 157 22.90 147 21.90 137 21.50 132 20.50 125 20.20 123 19.40 117 19.20 114 18.80 111 18.80 112 22.60 144 23.30 150 23.00 149 21.40 134 19.90 123 18.80 116 18.60 117 18.40 111 18.60 105 19.90 102 19.20 95 18.40 93 21.10 124 20.50 130 19.10 124 18.10 115 17.00 106 17.10 105 17.40 105 16.80 101 15.30 95 14.30 93 13.40 84 15.30 87 22.10 116 23.70 120 22.20 117 19.50 109 16.60 105 17.30 107 19.80 109 21.20 109 21.50 108 20.60 107 19.10 99 19.60 103 23.50 131 24.00 137

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] = + 6.14943712562875 + 0.116613271853615X[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) 6.14943712562875 1.164542 5.2806 2e-06 1e-06 X 0.116613271853615 0.009632 12.1069 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.846455334645394 R-squared 0.716486633549646 Adjusted R-squared 0.711598472059123 F-TEST (value) 146.575892580205 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.36227282550261 Sum Squared Residuals 107.635660563966 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 25.6 24.9241738940608 0.675826105939228 2 23.7 23.5248146318174 0.175185368182579 3 22 22.3586819132813 -0.358681913281271 4 21.3 21.8922288258668 -0.59222882586681 5 20.7 21.3091624665987 -0.609162466598735 6 20.4 20.9593226510379 -0.55932265103789 7 20.3 20.3762562917698 -0.0762562917698112 8 20.4 19.7931899325017 0.606810067498263 9 19.8 19.2101235732337 0.589876426766341 10 19.5 19.3267368450873 0.173263154912725 11 23.1 23.5248146318174 -0.424814631817422 12 23.5 24.4577208066463 -0.957720806646346 13 23.5 24.4577208066463 -0.957720806646346 14 22.9 23.2915880881102 -0.391588088110195 15 21.9 22.1254553695740 -0.225455369574042 16 21.5 21.5423890103060 -0.0423890103059643 17 20.5 20.7260961073307 -0.226096107330658 18 20.2 20.4928695636234 -0.292869563623428 19 19.4 19.7931899325017 -0.393189932501737 20 19.2 19.4433501169409 -0.243350116940891 21 18.8 19.0935103013800 -0.293510301380043 22 18.8 19.2101235732337 -0.410123573233659 23 22.6 22.9417482725493 -0.341748272549346 24 23.3 23.6414279036710 -0.341427903671038 25 23 23.5248146318174 -0.524814631817424 26 21.4 21.7756155540132 -0.375615554013196 27 19.9 20.4928695636234 -0.592869563623429 28 18.8 19.6765766606481 -0.87657666064812 29 18.6 19.7931899325017 -1.19318993250173 30 18.4 19.0935103013800 -0.693510301380046 31 18.6 18.3938306702584 0.206169329741649 32 19.9 18.0439908546975 1.85600914530249 33 19.2 17.2276979517222 1.9723020482778 34 18.4 16.9944714080150 1.40552859198503 35 21.1 20.6094828354770 0.490517164522959 36 20.5 21.3091624665987 -0.809162466598734 37 19.1 20.6094828354770 -1.50948283547704 38 18.1 19.5599633887945 -1.45996338879450 39 17 18.5104439421120 -1.51044394211197 40 17.1 18.3938306702584 -1.29383067025835 41 17.4 18.3938306702584 -0.993830670258354 42 16.8 17.9273775828439 -1.12737758284389 43 15.3 17.2276979517222 -1.9276979517222 44 14.3 16.9944714080150 -2.69447140801497 45 13.4 15.9449519613324 -2.54495196133243 46 15.3 16.2947917768933 -0.994791776893277 47 22.1 19.6765766606481 2.42342333935188 48 23.7 20.1430297480626 3.55697025193742 49 22.2 19.7931899325017 2.40681006749826 50 19.5 18.8602837576728 0.639716242327186 51 16.6 18.3938306702584 -1.79383067025835 52 17.3 18.6270572139656 -1.32705721396558 53 19.8 18.8602837576728 0.939716242327187 54 21.2 18.8602837576728 2.33971624232719 55 21.5 18.7436704858192 2.7563295141808 56 20.6 18.6270572139656 1.97294278603442 57 19.1 17.6941510391367 1.40584896086334 58 19.6 18.1606041265511 1.43939587344888 59 23.5 21.4257757384523 2.07422426154765 60 24 22.1254553695740 1.87454463042596 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.000377323017492185 0.00075464603498437 0.999622676982508 6 0.000180503117759848 0.000361006235519696 0.99981949688224 7 0.00185187943696063 0.00370375887392126 0.99814812056304 8 0.00987749819779912 0.0197549963955982 0.990122501802201 9 0.00785435552587399 0.0157087110517480 0.992145644474126 10 0.00288719402216502 0.00577438804433004 0.997112805977835 11 0.00120299859540530 0.00240599719081060 0.998797001404595 12 0.000935464682578276 0.00187092936515655 0.999064535317422 13 0.000541907650212348 0.00108381530042470 0.999458092349788 14 0.000188686546155548 0.000377373092311097 0.999811313453844 15 6.16870598160802e-05 0.000123374119632160 0.999938312940184 16 1.93352713260969e-05 3.86705426521937e-05 0.999980664728674 17 6.15581007632632e-06 1.23116201526526e-05 0.999993844189924 18 2.03159360363008e-06 4.06318720726017e-06 0.999997968406396 19 7.88004758656202e-07 1.57600951731240e-06 0.99999921199524 20 2.41378286122093e-07 4.82756572244186e-07 0.999999758621714 21 7.57489735315007e-08 1.51497947063001e-07 0.999999924251026 22 2.59850303200149e-08 5.19700606400298e-08 0.99999997401497 23 7.06287625799703e-09 1.41257525159941e-08 0.999999992937124 24 1.98766210129368e-09 3.97532420258736e-09 0.999999998012338 25 7.04497373975226e-10 1.40899474795045e-09 0.999999999295503 26 2.1658151526911e-10 4.3316303053822e-10 0.999999999783418 27 9.94916078270994e-11 1.98983215654199e-10 0.999999999900508 28 1.11123379809830e-10 2.22246759619659e-10 0.999999999888877 29 3.68971765142341e-10 7.37943530284683e-10 0.999999999631028 30 1.64563525944961e-10 3.29127051889922e-10 0.999999999835437 31 6.47151735189477e-11 1.29430347037895e-10 0.999999999935285 32 1.53717592232354e-08 3.07435184464708e-08 0.99999998462824 33 3.45172301716487e-07 6.90344603432974e-07 0.999999654827698 34 6.28157841093926e-07 1.25631568218785e-06 0.99999937184216 35 3.21575791409152e-07 6.43151582818305e-07 0.999999678424209 36 6.15726902084337e-07 1.23145380416867e-06 0.999999384273098 37 1.01412209083829e-05 2.02824418167658e-05 0.999989858779092 38 7.83290552814708e-05 0.000156658110562942 0.999921670944719 39 0.000288005887631219 0.000576011775262438 0.999711994112369 40 0.000527169595296919 0.00105433919059384 0.999472830404703 41 0.000576750020500483 0.00115350004100097 0.9994232499795 42 0.000598893475572264 0.00119778695114453 0.999401106524428 43 0.00146300563690704 0.00292601127381408 0.998536994363093 44 0.0107366353929590 0.0214732707859181 0.98926336460704 45 0.0260564745853095 0.052112949170619 0.97394352541469 46 0.0191703126616077 0.0383406253232154 0.980829687338392 47 0.0391839844107632 0.0783679688215263 0.960816015589237 48 0.16165192353089 0.32330384706178 0.83834807646911 49 0.187672477027609 0.375344954055219 0.81232752297239 50 0.135380859113627 0.270761718227254 0.864619140886373 51 0.417283472934011 0.834566945868021 0.582716527065989 52 0.955757401500371 0.0884851969992579 0.0442425984996289 53 0.968259226350088 0.0634815472998246 0.0317407736499123 54 0.94566196092949 0.108676078141021 0.0543380390705107 55 0.990414320016036 0.0191713599679283 0.00958567998396414 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 37 0.725490196078431 NOK 5% type I error level 42 0.823529411764706 NOK 10% type I error level 46 0.901960784313726 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/10sl0n1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/10sl0n1258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/1q3pb1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/1q3pb1258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/2dxjw1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/2dxjw1258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/3eics1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/3eics1258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/4wm381258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/4wm381258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/5s8us1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/5s8us1258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/6sev51258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/6sev51258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/7lbtj1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/7lbtj1258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/8e6f71258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/8e6f71258646822.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/9tafb1258646822.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586468853icbaux6fs2r2ft/9tafb1258646822.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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