met lineaire trend en seizoenaliteit

*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: Sat, 12 Dec 2009 04:35:34 -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/Dec/12/t1260618151s453v4t6ekrgnaf.htm/, Retrieved Sat, 12 Dec 2009 12:42:43 +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/Dec/12/t1260618151s453v4t6ekrgnaf.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:

» Textbox « » Textfile « » CSV «

17192.4 0 15386.1 0 14287.1 0 17526.6 0 14497 0 14398.3 0 16629.6 0 16670.7 0 16614.8 0 16869.2 0 15663.9 0 16359.9 0 18447.7 0 16889 0 16505 0 18320.9 0 15052.1 0 15699.8 0 18135.3 0 16768.7 0 18883 0 19021 0 18101.9 0 17776.1 0 21489.9 0 17065.3 0 18690 0 18953.1 0 16398.9 0 16895.6 0 18553 0 19270 0 19422.1 0 17579.4 0 18637.3 0 18076.7 0 20438.6 0 18075.2 0 19563 0 19899.2 0 19227.5 0 17789.6 0 19220.8 0 21968.9 0 21131.5 0 19484.6 0 22168.7 1 20866.8 1 22176.2 1 23533.8 1 21479.6 1 24347.7 1 22751.6 1 20328.3 1 23650.4 1 23335.7 1 19614.9 1 18042.3 1 17282.5 1 16847.2 1 18159.5 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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Invoer[t] = + 14662.8712459016 + 649.502540983608Dummy[t] + 2134.04735519127M1[t] + 1185.18154644809M2[t] + 1015.16744262295M3[t] + 2634.65333879781M4[t] + 325.499234972678M5[t] -322.674868852462M6[t] + 1807.75102732240M7[t] + 2087.65692349727M8[t] + 1533.04281967213M9[t] + 514.008715846994M10[t] + 470.594103825136M11[t] + 85.0741038251365t + 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) 14662.8712459016 874.69592 16.7634 0 0 Dummy 649.502540983608 749.613386 0.8665 0.390645 0.195323 M1 2134.04735519127 994.864285 2.1451 0.037147 0.018573 M2 1185.18154644809 1044.110171 1.1351 0.262084 0.131042 M3 1015.16744262295 1043.034819 0.9733 0.335396 0.167698 M4 2634.65333879781 1042.279454 2.5278 0.014899 0.00745 M5 325.499234972678 1041.844774 0.3124 0.756099 0.378049 M6 -322.674868852462 1041.731179 -0.3097 0.758121 0.379061 M7 1807.75102732240 1041.938774 1.735 0.089297 0.044648 M8 2087.65692349727 1042.467368 2.0026 0.051007 0.025503 M9 1533.04281967213 1043.316473 1.4694 0.148388 0.074194 M10 514.008715846994 1044.485306 0.4921 0.624928 0.312464 M11 470.594103825136 1037.157503 0.4537 0.652108 0.326054 t 85.0741038251365 18.292678 4.6507 2.7e-05 1.4e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.796759760684484 R-squared 0.634826116245995 Adjusted R-squared 0.533820573931058 F-TEST (value) 6.28506220249374 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.19807218634804e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1639.6349161624 Sum Squared Residuals 126354924.940048 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 17192.4 16881.992704918 310.407295082008 2 15386.1 16018.201 -632.101000000004 3 14287.1 15933.261 -1646.16100000000 4 17526.6 17637.821 -111.221000000008 5 14497 15413.741 -916.740999999999 6 14398.3 14850.641 -452.341000000005 7 16629.6 17066.141 -436.541000000004 8 16670.7 17431.121 -760.421000000002 9 16614.8 16961.581 -346.781000000002 10 16869.2 16027.621 841.579 11 15663.9 16069.2804918033 -405.380491803281 12 16359.9 15683.7604918033 676.139508196718 13 18447.7 17902.8819508197 544.818049180318 14 16889 17039.0902459016 -150.090245901640 15 16505 16954.1502459016 -449.15024590164 16 18320.9 18658.7102459016 -337.810245901638 17 15052.1 16434.6302459016 -1382.53024590164 18 15699.8 15871.5302459016 -171.730245901640 19 18135.3 18087.0302459016 48.2697540983598 20 16768.7 18452.0102459016 -1683.31024590164 21 18883 17982.4702459016 900.52975409836 22 19021 17048.5102459016 1972.48975409836 23 18101.9 17090.1697377049 1011.73026229508 24 17776.1 16704.6497377049 1071.45026229508 25 21489.9 18923.7711967213 2566.12880327868 26 17065.3 18059.9794918033 -994.679491803278 27 18690 17975.0394918033 714.960508196722 28 18953.1 19679.5994918033 -726.499491803279 29 16398.9 17455.5194918033 -1056.61949180328 30 16895.6 16892.4194918033 3.18050819672222 31 18553 19107.9194918033 -554.919491803277 32 19270 19472.8994918033 -202.899491803279 33 19422.1 19003.3594918033 418.74050819672 34 17579.4 18069.3994918033 -489.999491803277 35 18637.3 18111.0589836066 526.241016393441 36 18076.7 17725.5389836066 351.161016393443 37 20438.6 19944.6604426230 493.93955737704 38 18075.2 19080.8687377049 -1005.66873770491 39 19563 18995.9287377049 567.071262295085 40 19899.2 20700.4887377049 -801.288737704914 41 19227.5 18476.4087377049 751.091262295082 42 17789.6 17913.3087377049 -123.708737704916 43 19220.8 20128.8087377049 -908.008737704917 44 21968.9 20493.7887377049 1475.11126229509 45 21131.5 20024.2487377049 1107.25126229508 46 19484.6 19090.2887377049 394.311262295082 47 22168.7 19781.4507704918 2387.2492295082 48 20866.8 19395.9307704918 1470.86922950820 49 22176.2 21615.0522295082 561.147770491796 50 23533.8 20751.2605245902 2782.53947540984 51 21479.6 20666.3205245902 813.279475409836 52 24347.7 22370.8805245902 1976.81947540984 53 22751.6 20146.8005245902 2604.79947540983 54 20328.3 19583.7005245902 744.599475409839 55 23650.4 21799.2005245902 1851.19947540984 56 23335.7 22164.1805245902 1171.51947540984 57 19614.9 21694.6405245902 -2079.74052459016 58 18042.3 20760.6805245902 -2718.38052459016 59 17282.5 20802.3400163934 -3519.84001639344 60 16847.2 20416.8200163934 -3569.62001639344 61 18159.5 22635.9414754098 -4476.44147540984 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.030107764554522 0.060215529109044 0.969892235445478 18 0.00696450882045687 0.0139290176409137 0.993035491179543 19 0.00154911865385187 0.00309823730770375 0.998450881346148 20 0.00204415422158258 0.00408830844316517 0.997955845778417 21 0.00138036628027631 0.00276073256055262 0.998619633719724 22 0.00064331889340712 0.00128663778681424 0.999356681106593 23 0.000374189022780842 0.000748378045561685 0.99962581097722 24 0.000100350508808642 0.000200701017617284 0.999899649491191 25 0.000181724036926885 0.000363448073853769 0.999818275963073 26 0.000305450394537932 0.000610900789075864 0.999694549605462 27 0.000186469338092348 0.000372938676184696 0.999813530661908 28 0.000199850984894159 0.000399701969788319 0.999800149015106 29 0.000219443581057527 0.000438887162115054 0.999780556418942 30 0.000107126155933114 0.000214252311866229 0.999892873844067 31 0.000129813101182350 0.000259626202364700 0.999870186898818 32 0.000330309978159995 0.00066061995631999 0.99966969002184 33 0.000301379362712603 0.000602758725425206 0.999698620637287 34 0.00676527101762989 0.0135305420352598 0.99323472898237 35 0.00358275254587115 0.0071655050917423 0.996417247454129 36 0.00218169684976053 0.00436339369952105 0.99781830315024 37 0.00136866723137229 0.00273733446274458 0.998631332768628 38 0.00324235199631999 0.00648470399263998 0.99675764800368 39 0.00174555097266997 0.00349110194533995 0.99825444902733 40 0.00308056917888291 0.00616113835776582 0.996919430821117 41 0.00701544133473669 0.0140308826694734 0.992984558665263 42 0.00523962243877023 0.0104792448775405 0.99476037756123 43 0.296607247610690 0.593214495221379 0.70339275238931 44 0.942559346608787 0.114881306782426 0.057440653391213 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.75 NOK 5% type I error level 25 0.892857142857143 NOK 10% type I error level 26 0.928571428571429 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/105hu71260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/105hu71260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/17a4l1260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/17a4l1260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/22n681260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/22n681260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/36q311260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/36q311260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/4updx1260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/4updx1260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/5sqwa1260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/5sqwa1260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/6m8ts1260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/6m8ts1260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/73kky1260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/73kky1260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/8gej01260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/8gej01260617728.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/9xcvg1260617728.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t1260618151s453v4t6ekrgnaf/9xcvg1260617728.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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