Workshop 8

*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, 26 Nov 2010 12:51:45 +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/26/t1290776683ldoqtf82zfzbzpo.htm/, Retrieved Fri, 26 Nov 2010 14:04:43 +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/2010/Nov/26/t1290776683ldoqtf82zfzbzpo.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:

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Dataseries X:

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13768040,14 14731798,37 17487530,67 16471559,62 16198106,13 15213975,95 17535166,38 17637387,4 16571771,60 17972385,83 16198892,67 16896235,55 16554237,93 16697955,94 19554176,37 19691579,52 15903762,33 15930700,75 18003781,65 17444615,98 18329610,38 17699369,88 16260733,42 15189796,81 14851949,20 15672722,75 18174068,44 17180794,3 18406552,23 17664893,45 18466459,42 17862884,98 16016524,60 16162288,88 17428458,32 17463628,82 17167191,42 16772112,17 19629987,60 19106861,48 17183629,01 16721314,25 18344657,85 18161267,85 19301440,71 18509941,2 18147463,68 17802737,97 16192909,22 16409869,75 18374420,60 17967742,04 20515191,95 20286602,27 18957217,20 19537280,81 16471529,53 18021889,62 18746813,27 20194317,23 19009453,59 19049596,62 19211178,55 20244720,94 20547653,75 21473302,24 19325754,03 19673603,19 20605542,58 21053177,29 20056915,06 20159479,84 16141449,72 18203628,31 20359793,22 21289464,94 19711553,27 20432335,71 15638580,70 17180395,07 14384486,00 15816786,32 13 etc...

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2563937.55483184 + 0.87548284030003X[t] -1626909.38512229M1[t] -36997.8580407108M2[t] -213108.916594054M3[t] -692140.797373351M4[t] -1569112.80964333M5[t] -1281363.69594057M6[t] -536563.73776368M7[t] -394936.994381191M8[t] -692625.977911235M9[t] -418327.260387958M10[t] + 36178.4923909261M11[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) 2563937.55483184 738771.406353 3.4705 0.001124 0.000562 X 0.87548284030003 0.041091 21.306 0 0 M1 -1626909.38512229 356911.097022 -4.5583 3.7e-05 1.8e-05 M2 -36997.8580407108 355349.001374 -0.1041 0.91752 0.45876 M3 -213108.916594054 356664.866431 -0.5975 0.553039 0.276519 M4 -692140.797373351 354730.075748 -1.9512 0.057011 0.028506 M5 -1569112.80964333 353473.634092 -4.4391 5.4e-05 2.7e-05 M6 -1281363.69594057 353572.455378 -3.624 0.00071 0.000355 M7 -536563.73776368 353735.540929 -1.5168 0.136002 0.068001 M8 -394936.994381191 360762.429133 -1.0947 0.279214 0.139607 M9 -692625.977911235 353791.414673 -1.9577 0.056214 0.028107 M10 -418327.260387958 353812.283514 -1.1823 0.243017 0.121509 M11 36178.4923909261 359053.804905 0.1008 0.920169 0.460085 Multiple Linear Regression - Regression Statistics Multiple R 0.968053822102862 R-squared 0.93712820248796 Adjusted R-squared 0.921075828655098 F-TEST (value) 58.3794155459752 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 558881.641490906 Sum Squared Residuals 14680388392191.8 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13768040.14 13834464.8494045 -66424.7094044972 2 17487530.67 16947507.49708 540023.17292 3 16198106.13 15670403.5152001 527702.614799876 4 17535166.38 17313026.7738824 222139.606117558 5 16571771.6 16729340.1386049 -157568.538604919 6 16198892.67 16074938.1485836 123954.521416399 7 16554237.93 16646147.7106241 -91909.7806241096 8 19554176.37 19408640.5286141 145535.841385858 9 15903762.33 15818366.7175004 85395.6124995842 10 18003781.65 17418072.2405576 585709.409442432 11 18329610.38 18095610.6612860 233999.718714040 12 16260733.42 15862344.0096310 398389.410369032 13 14851949.2 14658227.9981144 193721.201885555 14 18174068.44 17568430.2891657 605638.150834313 15 18406552.23 17816139.7294412 590412.500558827 16 18466459.42 17510446.0357016 956013.384298375 17 16016524.6 15144631.3196005 871893.280399501 18 17428458.32 16571681.2201703 856777.099829678 19 17167191.42 16711070.2174905 456121.20250955 20 19629987.6 18896729.9181803 733257.681819725 21 17183629.01 16510535.2700600 673093.739940038 22 18344657.85 18045488.6552115 299169.194788506 23 19301440.71 18805251.9427853 496188.767214697 24 18147463.68 18149929.1579246 -2465.47792462261 25 16192909.22 15303587.5473931 889321.67260691 26 18374420.6 18257389.5317486 117031.068251425 27 20515191.95 20111400.8136144 403791.136385585 28 18957217.2 18976350.8527366 -19133.6527365521 29 16471529.53 16772679.8572797 -301150.327279737 30 18746813.27 18962352.0653315 -215538.795331496 31 19009453.59 18704968.7725156 304484.817484395 32 19211178.55 19892906.3500833 -681727.800083336 33 20547653.75 20670819.2126168 -123165.462616793 34 19325754.03 19369512.2941608 -43758.2641608061 35 20605542.58 21031811.4984120 -426268.918412049 36 20056915.06 20213216.2241262 -156301.164126229 37 16141449.72 16873992.3863144 -732542.666314381 38 20359793.22 21165500.9309302 -805707.710930232 39 19711553.27 20238987.9395923 -527434.66959231 40 15638580.7 16912937.8308187 -1274357.13081872 41 14384486 14842149.7570408 -457663.757040767 42 13855616.12 14477693.4221113 -622077.302111349 43 14308336.46 14740365.7330503 -432029.273050255 44 15290621.44 15886756.7996708 -596135.359670817 45 14423755.53 14431762.4269988 -8006.89699876563 46 13779681.49 14298194.7183241 -518513.228324083 47 15686348.94 16140284.3568643 -453935.416864285 48 14733828.17 15029266.1524868 -295437.982486823 49 12522497.94 12806573.4387736 -284075.498773586 50 16189383.57 16646368.2510755 -456984.681075505 51 16059123.25 17053594.8321520 -994471.582151978 52 16007123.26 15891785.4668607 115337.793139335 53 15806842.33 15762352.9874741 44489.3425259217 54 15159951.13 15303066.6538032 -143115.523803232 55 15692144.17 15928811.1363196 -236666.966319581 56 18908869.11 18509799.4734514 399069.63654857 57 16969881.42 17597198.4128241 -627316.992824064 58 16997477.78 17320084.8917461 -322607.111746049 59 19858875.65 19708859.8006524 150015.849347598 60 17681170.13 17625354.9158314 55815.2141686426 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.167445256231528 0.334890512463055 0.832554743768472 17 0.354910823516949 0.709821647033898 0.645089176483051 18 0.421978537395240 0.843957074790481 0.57802146260476 19 0.371440355031618 0.742880710063235 0.628559644968382 20 0.364225006490683 0.728450012981366 0.635774993509317 21 0.396041784641198 0.792083569282397 0.603958215358802 22 0.319273614351437 0.638547228702874 0.680726385648563 23 0.279328717770798 0.558657435541596 0.720671282229202 24 0.201582889829833 0.403165779659667 0.798417110170167 25 0.444687314478979 0.889374628957957 0.555312685521022 26 0.453601499656558 0.907202999313116 0.546398500343442 27 0.531528449224756 0.936943101550488 0.468471550775244 28 0.529056286034573 0.941887427930855 0.470943713965427 29 0.502896017619224 0.994207964761552 0.497103982380776 30 0.436249938172412 0.872499876344824 0.563750061827588 31 0.425285592796258 0.850571185592515 0.574714407203742 32 0.577384018812844 0.845231962374313 0.422615981187156 33 0.480365913408043 0.960731826816086 0.519634086591957 34 0.414343214885843 0.828686429771687 0.585656785114157 35 0.360260339616586 0.720520679233172 0.639739660383414 36 0.271640470349725 0.543280940699449 0.728359529650275 37 0.293207328898501 0.586414657797002 0.706792671101499 38 0.302930568612554 0.605861137225108 0.697069431387446 39 0.24578570415117 0.49157140830234 0.75421429584883 40 0.840144562703691 0.319710874592618 0.159855437296309 41 0.807676830767938 0.384646338464124 0.192323169232062 42 0.7899110416715 0.420177916657 0.2100889583285 43 0.686888905335747 0.626222189328506 0.313111094664253 44 0.748835945544277 0.502328108911446 0.251164054455723 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/102s6o1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/102s6o1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/1e9ru1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/1e9ru1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/2oiqf1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/2oiqf1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/3oiqf1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/3oiqf1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/4oiqf1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/4oiqf1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/5z9qi1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/5z9qi1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/6z9qi1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/6z9qi1290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/791p31290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/791p31290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/891p31290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/891p31290775897.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/92s6o1290775897.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/26/t1290776683ldoqtf82zfzbzpo/92s6o1290775897.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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