multiple

*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, 24 Nov 2009 14:03:26 -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/24/t125909664709bwct50kpk84xb.htm/, Retrieved Tue, 24 Nov 2009 22:04:18 +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/24/t125909664709bwct50kpk84xb.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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2,4 0 1,7 1 1,2 1,4 2 0 2,4 1,7 1 1,2 2,1 0 2 2,4 1,7 1 2 0 2,1 2 2,4 1,7 1,8 0 2 2,1 2 2,4 2,7 0 1,8 2 2,1 2 2,3 0 2,7 1,8 2 2,1 1,9 0 2,3 2,7 1,8 2 2 0 1,9 2,3 2,7 1,8 2,3 0 2 1,9 2,3 2,7 2,8 0 2,3 2 1,9 2,3 2,4 0 2,8 2,3 2 1,9 2,3 0 2,4 2,8 2,3 2 2,7 0 2,3 2,4 2,8 2,3 2,7 0 2,7 2,3 2,4 2,8 2,9 0 2,7 2,7 2,3 2,4 3 0 2,9 2,7 2,7 2,3 2,2 0 3 2,9 2,7 2,7 2,3 0 2,2 3 2,9 2,7 2,8 0 2,3 2,2 3 2,9 2,8 0 2,8 2,3 2,2 3 2,8 0 2,8 2,8 2,3 2,2 2,2 0 2,8 2,8 2,8 2,3 2,6 0 2,2 2,8 2,8 2,8 2,8 0 2,6 2,2 2,8 2,8 2,5 0 2,8 2,6 2,2 2,8 2,4 0 2,5 2,8 2,6 2,2 2,3 0 2,4 2,5 2,8 2,6 1,9 0 2,3 2,4 2,5 2,8 1,7 0 1,9 2,3 2,4 2,5 2 0 1,7 1,9 2,3 2,4 2,1 0 2 1,7 1,9 2,3 1,7 0 2,1 2 1,7 1,9 1,8 0 1,7 2,1 2 1,7 1,8 0 1,8 1,7 2,1 2 1,8 0 1,8 1,8 1,7 2,1 1,3 1 1,8 1,8 1,8 1,7 1,3 1 1,3 1,8 1,8 1,8 1,3 1 1,3 1,3 1,8 1,8 1,2 1 1,3 1,3 1,3 1,8 1,4 1 1,2 1,3 1,3 1,3 2,2 1 1,4 1,2 1,3 1,3 2,9 1 2,2 1,4 1,2 1,3 3,1 1 2,9 2,2 1,4 1,2 3,5 1 3,1 2,9 2,2 1,4 3,6 1 3,5 3,1 2,9 2,2 4,4 1 3,6 3,5 3,1 2,9 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 0.264585623036714 + 0.136019702329563x[t] + 1.1180927364462y1[t] -0.26871069986116y2[t] + 0.274416880874272y3[t] -0.284488421665747y4[t] + 0.289579993080271M1[t] + 0.0893609542088653M2[t] + 0.0956344083568998M3[t] -0.074459306273987M4[t] + 0.0764602746631292M5[t] + 0.105570035656608M6[t] -0.0471979987964152M7[t] + 0.110257357802178M8[t] + 0.027425940682833M9[t] + 0.152467811299498M10[t] + 0.216128371847527M11[t] + 0.00063940059680417t + 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) 0.264585623036714 0.327744 0.8073 0.424521 0.212261 x 0.136019702329563 0.280344 0.4852 0.630329 0.315164 y1 1.1180927364462 0.155389 7.1954 0 0 y2 -0.26871069986116 0.234661 -1.1451 0.259329 0.129665 y3 0.274416880874272 0.238875 1.1488 0.257822 0.128911 y4 -0.284488421665747 0.187206 -1.5197 0.136876 0.068438 M1 0.289579993080271 0.340349 0.8508 0.400192 0.200096 M2 0.0893609542088653 0.337754 0.2646 0.792768 0.396384 M3 0.0956344083568998 0.33734 0.2835 0.778336 0.389168 M4 -0.074459306273987 0.337874 -0.2204 0.826758 0.413379 M5 0.0764602746631292 0.339282 0.2254 0.822908 0.411454 M6 0.105570035656608 0.339343 0.3111 0.757424 0.378712 M7 -0.0471979987964152 0.338018 -0.1396 0.889688 0.444844 M8 0.110257357802178 0.338332 0.3259 0.7463 0.37315 M9 0.027425940682833 0.350686 0.0782 0.938074 0.469037 M10 0.152467811299498 0.350388 0.4351 0.665922 0.332961 M11 0.216128371847527 0.348872 0.6195 0.53928 0.26964 t 0.00063940059680417 0.008795 0.0727 0.942425 0.471213 Multiple Linear Regression - Regression Statistics Multiple R 0.934795605561394 R-squared 0.873842824176893 Adjusted R-squared 0.81740408762445 F-TEST (value) 15.4830330647980 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 3.88156173869447e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.492188623641708 Sum Squared Residuals 9.2054863672081 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.4 2.11786843552825 0.282131564471752 2 2 2.51487053102147 -0.51487053102147 3 2.1 2.13543830223016 -0.0354383022301558 4 2 2.17822746323113 -0.178227463231126 5 1.8 1.88219745361858 -0.0821974536185777 6 2.7 1.85643619465946 0.843563805340538 7 2.3 2.70844263332305 -0.408442633323055 8 1.9 2.15102613205665 -0.251026132056648 9 2 2.03295417802008 -0.0329541780200847 10 2.3 2.01212267097376 0.287877329026242 11 2.8 2.38900799938292 0.410992000617075 12 2.4 2.79318924315068 -0.393189243150680 13 2.3 2.5556924144144 -0.255692414414402 14 2.7 2.40364969637706 0.296350303622945 15 2.7 2.63265975250391 0.0673402474960914 16 2.9 2.44207483910423 0.457925160895766 17 3 2.95546796244368 0.0445320375563233 18 2.2 2.92948888904005 -0.72948888904005 19 2.3 1.91089837221561 0.389101627784392 20 2.8 2.36631496669883 0.433685033301168 21 2.8 2.56831590154728 0.231684098452717 22 2.8 2.8146742482502 -0.0146742482501972 23 2.2 2.98773380766559 -0.78773380766559 24 2.6 1.95914498371428 0.640855016285725 25 2.8 2.85782789188653 -0.0578278918865263 26 2.5 2.60973239243214 -0.109732392432137 27 2.4 2.50793509162004 -0.107935091620041 28 2.3 2.24837272140824 0.0516272785917584 29 1.9 2.17577075068823 -0.275770750688227 30 1.7 1.84305872609844 -0.143058726098443 31 2 1.57580297897660 0.424197021023405 32 2.1 2.04174978689495 0.058250213105049 33 1.7 2.04966582655013 -0.349665826550127 34 1.8 1.84046168179443 -0.0404616817944307 35 1.8 2.06615035811605 -0.26615035811605 36 1.8 1.68557472236293 0.114425277637072 37 1.3 2.25305087512329 -0.953050875123293 38 1.3 1.46597602645902 -0.165976026459016 39 1.3 1.60724423113443 -0.307244231134435 40 1.2 1.30058147666322 -0.100581476663216 41 1.4 1.48257539538539 -0.0825753953853896 42 2.2 1.76281417425103 0.437185825748971 43 2.9 2.42397590149211 0.476024098507889 44 3.1 3.23309923265235 -0.133099232652350 45 3.5 3.34906409388251 0.150935906117495 46 3.6 3.83274139898162 -0.232741398981615 47 4.4 3.75710783483543 0.642892165164565 48 4.1 4.46209105077212 -0.362091050772117 49 5.1 4.11556038304753 0.984439616952469 50 5.8 5.30577135371032 0.494228646289679 51 5.9 5.51672262251146 0.38327737748854 52 5.4 5.63074349959318 -0.230743499593182 53 5.5 5.10398843786413 0.396011562135871 54 4.8 5.20820201595102 -0.408202015951016 55 3.2 4.08088011399263 -0.88088011399263 56 2.7 2.80780988169722 -0.107809881697220 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.426112916016004 0.852225832032008 0.573887083983996 22 0.283148230297610 0.566296460595221 0.71685176970239 23 0.433825899197754 0.867651798395508 0.566174100802246 24 0.436471438853296 0.872942877706592 0.563528561146704 25 0.361635188100864 0.723270376201728 0.638364811899136 26 0.298371880372255 0.596743760744509 0.701628119627745 27 0.23943716999724 0.47887433999448 0.76056283000276 28 0.227443647291865 0.45488729458373 0.772556352708135 29 0.185453082070598 0.370906164141195 0.814546917929402 30 0.128533867041127 0.257067734082253 0.871466132958873 31 0.149878514711973 0.299757029423946 0.850121485288027 32 0.232665691484637 0.465331382969274 0.767334308515363 33 0.168029419546517 0.336058839093034 0.831970580453483 34 0.122624466132881 0.245248932265761 0.87737553386712 35 0.0843248786943932 0.168649757388786 0.915675121305607 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/2009/Nov/24/t125909664709bwct50kpk84xb/10cgkv1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/10cgkv1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/1xl6n1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/1xl6n1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/2lyjw1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/2lyjw1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/33sr51259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/33sr51259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/4rt7t1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/4rt7t1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/5vvys1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/5vvys1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/6qio61259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/6qio61259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/77yba1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/77yba1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/8rdbl1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/8rdbl1259096602.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/9vohf1259096602.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t125909664709bwct50kpk84xb/9vohf1259096602.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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