ws7

*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, 17 Nov 2009 11:00:39 -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/17/t1258481650g9fdjzjpg262ulv.htm/, Retrieved Tue, 17 Nov 2009 19:14:22 +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/17/t1258481650g9fdjzjpg262ulv.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:

zonder dummies of lineaire trend

Dataseries X:

» Textbox « » Textfile « » CSV «

325412 285351 326011 286602 328282 283042 317480 276687 317539 277915 313737 277128 312276 277103 309391 275037 302950 270150 300316 267140 304035 264993 333476 287259 337698 291186 335932 292300 323931 288186 313927 281477 314485 282656 313218 280190 309664 280408 302963 276836 298989 275216 298423 274352 310631 271311 329765 289802 335083 290726 327616 292300 309119 278506 295916 269826 291413 265861 291542 269034 284678 264176 276475 255198 272566 253353 264981 246057 263290 235372 296806 258556 303598 260993 286994 254663 276427 250643 266424 243422 267153 247105 268381 248541 262522 245039 255542 237080 253158 237085 243803 225554 250741 226839 280445 247934 285257 248333 270976 246969 261076 245098 255603 246263 260376 255765 263903 264319 264291 268347 263276 273046 262572 273963 256167 267430 264221 271993 293860 292710 300713 295881 287224 293299

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 Werkl_vrouwen[t] = -21931.2893358208 + 1.17481411929854Werkl_mannen[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) -21931.2893358208 28910.695913 -0.7586 0.451069 0.225535 Werkl_mannen 1.17481411929854 0.107998 10.8781 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.814587321159498 R-squared 0.663552503793807 Adjusted R-squared 0.657945045523704 F-TEST (value) 118.333917406325 F-TEST (DF numerator) 1 F-TEST (DF denominator) 60 p-value 7.7715611723761e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15319.9310318618 Sum Squared Residuals 14082017209.2601 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 325412 313303.094420138 12108.9055798623 2 326011 314772.786883380 11238.2131166195 3 328282 310590.448618678 17691.5513813222 4 317480 303124.504890536 14355.4951094645 5 317539 304567.176629034 12971.8233709659 6 313737 303642.597917146 10094.4020828538 7 312276 303613.227564164 8662.77243583628 8 309391 301186.061593693 8204.93840630707 9 302950 295444.744992681 7505.25500731906 10 300316 291908.554493592 8407.44550640767 11 304035 289386.228579458 14648.7714205416 12 333476 315544.63975976 17931.3602402403 13 337698 320158.134806245 17539.8651937549 14 335932 321466.877735144 14465.1222648563 15 323931 316633.692448349 7297.30755165052 16 313927 308751.864521976 5175.13547802445 17 314485 310136.970368629 4348.02963137147 18 313218 307239.878750438 5978.12124956167 19 309664 307495.988228445 2168.01177155459 20 302963 303299.552194311 -336.552194311008 21 298989 301396.353321047 -2407.35332104737 22 298423 300381.313921973 -1958.31392197343 23 310631 296808.704185187 13822.2958148134 24 329765 318532.192065136 11232.8079348641 25 335083 319617.720311368 15465.2796886322 26 327616 321466.877735144 6149.1222648563 27 309119 305261.491773540 3857.50822646042 28 295916 295064.105218028 851.894781971785 29 291413 290405.967235009 1007.03276499051 30 291542 294133.652435544 -2591.65243554377 31 284678 288426.405443991 -3748.40544399144 32 276475 277878.924280929 -1403.92428092911 33 272566 275711.392230823 -3145.3922308233 34 264981 267139.948416421 -2158.94841642112 35 263290 254587.059551716 8702.94044828382 36 296806 281823.950093534 14982.0499064664 37 303598 284686.972102264 18911.0278977358 38 286994 277250.398727104 9743.6012728956 39 276427 272527.645967524 3899.35403247576 40 266424 264044.313212069 2379.68678793054 41 267153 268371.153613446 -1218.15361344600 42 268381 270058.186688759 -1677.18668875871 43 262522 265943.987642975 -3421.98764297520 44 255542 256593.642067478 -1051.64206747809 45 253158 256599.516138075 -3441.51613807459 46 243803 243052.734528443 750.265471556918 47 250741 244562.370671742 6178.62932825829 48 280445 269345.074518344 11099.9254816555 49 285257 269813.825351945 15443.1746480554 50 270976 268211.378893221 2764.62110677860 51 261076 266013.301676014 -4937.30167601382 52 255603 267381.960124997 -11778.9601249966 53 260376 278545.043886571 -18169.0438865714 54 263903 288594.403863051 -24691.4038630511 55 264291 293326.555135586 -29035.5551355857 56 263276 298847.006682170 -35571.0066821695 57 262572 299924.311229566 -37352.3112295663 58 256167 292249.250588189 -36082.2505881889 59 264221 297609.927414548 -33388.9274145482 60 293860 321948.551524056 -28088.5515240561 61 300713 325673.887096352 -24960.8870963518 62 287224 322640.517040323 -35416.5170403229 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00811827756082195 0.0162365551216439 0.991881722439178 6 0.00284041686515217 0.00568083373030435 0.997159583134848 7 0.00106431488122973 0.00212862976245947 0.99893568511877 8 0.000299780776926709 0.000599561553853419 0.999700219223073 9 6.01265612033006e-05 0.000120253122406601 0.999939873438797 10 1.07467482217299e-05 2.14934964434599e-05 0.999989253251778 11 1.37765083216038e-05 2.75530166432076e-05 0.999986223491678 12 7.59318651739134e-06 1.51863730347827e-05 0.999992406813483 13 2.43235915092601e-06 4.86471830185201e-06 0.999997567640849 14 6.92000677261004e-07 1.38400135452201e-06 0.999999307999323 15 1.17159227930219e-06 2.34318455860439e-06 0.99999882840772 16 1.57218257481535e-06 3.1443651496307e-06 0.999998427817425 17 2.16668247710164e-06 4.33336495420327e-06 0.999997833317523 18 1.32733984988609e-06 2.65467969977219e-06 0.99999867266015 19 2.13565885091290e-06 4.27131770182581e-06 0.99999786434115 20 4.28718402389106e-06 8.57436804778212e-06 0.999995712815976 21 8.99499733744246e-06 1.79899946748849e-05 0.999991005002663 22 1.07329183548332e-05 2.14658367096664e-05 0.999989267081645 23 1.06881091563575e-05 2.1376218312715e-05 0.999989311890844 24 8.49607206808389e-06 1.69921441361678e-05 0.999991503927932 25 1.62579197194497e-05 3.25158394388994e-05 0.99998374208028 26 3.70368215287395e-05 7.4073643057479e-05 0.999962963178471 27 4.93171207163528e-05 9.86342414327056e-05 0.999950682879284 28 4.80794882803616e-05 9.61589765607232e-05 0.99995192051172 29 3.67097292420779e-05 7.34194584841559e-05 0.999963290270758 30 4.43576366215563e-05 8.87152732431125e-05 0.999955642363378 31 3.68591096526156e-05 7.37182193052313e-05 0.999963140890347 32 1.74185603706717e-05 3.48371207413435e-05 0.99998258143963 33 7.62166731968274e-06 1.52433346393655e-05 0.99999237833268 34 3.29915728941395e-06 6.5983145788279e-06 0.99999670084271 35 1.08914171767746e-05 2.17828343535492e-05 0.999989108582823 36 8.46952843494122e-05 0.000169390568698824 0.99991530471565 37 0.00308331822046730 0.00616663644093461 0.996916681779533 38 0.00700052953927865 0.0140010590785573 0.992999470460721 39 0.00662467206952394 0.0132493441390479 0.993375327930476 40 0.00435915951201 0.00871831902402 0.99564084048799 41 0.00295723115343102 0.00591446230686205 0.997042768846569 42 0.00207884577296683 0.00415769154593366 0.997921154227033 43 0.00129489771578928 0.00258979543157855 0.99870510228421 44 0.000666069080354648 0.00133213816070930 0.999333930919645 45 0.000356964567747544 0.000713929135495088 0.999643035432253 46 0.000241165008039900 0.000482330016079799 0.99975883499196 47 0.000169146132011131 0.000338292264022262 0.999830853867989 48 0.000648526025659457 0.00129705205131891 0.99935147397434 49 0.0304172667470327 0.0608345334940655 0.969582733252967 50 0.111430120056517 0.222860240113034 0.888569879943483 51 0.237336715075474 0.474673430150948 0.762663284924526 52 0.462656169270677 0.925312338541354 0.537343830729323 53 0.814777019816323 0.370445960367354 0.185222980183677 54 0.95432298849814 0.0913540230037172 0.0456770115018586 55 0.981702314433052 0.0365953711338964 0.0182976855669482 56 0.96968598428929 0.0606280314214208 0.0303140157107104 57 0.946364472597604 0.107271054804793 0.0536355274023963 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 41 0.773584905660377 NOK 5% type I error level 45 0.849056603773585 NOK 10% type I error level 48 0.90566037735849 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/10k2731258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/10k2731258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/1j4zi1258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/1j4zi1258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/2q8h81258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/2q8h81258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/3dhrr1258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/3dhrr1258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/4ive21258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/4ive21258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/5cv871258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/5cv871258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/698q61258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/698q61258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/7a2tm1258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/7a2tm1258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/8h6b31258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/8h6b31258480834.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/9zqxf1258480834.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258481650g9fdjzjpg262ulv/9zqxf1258480834.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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