*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: Wed, 18 Nov 2009 10:10:28 -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/18/t1258564624mxbhrpyn2asgicu.htm/, Retrieved Wed, 18 Nov 2009 18:17:16 +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/18/t1258564624mxbhrpyn2asgicu.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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8.9 1.6 8.8 1.8 8.3 1.6 7.5 1.5 7.2 1.5 7.4 1.3 8.8 1.4 9.3 1.4 9.3 1.3 8.7 1.3 8.2 1.2 8.3 1.1 8.5 1.4 8.6 1.2 8.5 1.5 8.2 1.1 8.1 1.3 7.9 1.5 8.6 1.1 8.7 1.4 8.7 1.3 8.5 1.5 8.4 1.6 8.5 1.7 8.7 1.1 8.7 1.6 8.6 1.3 8.5 1.7 8.3 1.6 8 1.7 8.2 1.9 8.1 1.8 8.1 1.9 8 1.6 7.9 1.5 7.9 1.6 8 1.6 8 1.7 7.9 2 8 2 7.7 1.9 7.2 1.7 7.5 1.8 7.3 1.9 7 1.7 7 2 7 2.1 7.2 2.4 7.3 2.5 7.1 2.5 6.8 2.6 6.4 2.2 6.1 2.5 6.5 2.8 7.7 2.8 7.9 2.9 7.5 3 6.9 3.1 6.6 2.9 6.9 2.7

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 inflatie[t] = + 6.49479693937611 -0.59211300765156graad[t] + 0.0478987639788073M1[t] + 0.144214243672748M2[t] + 0.0539493819894059M3[t] -0.223684520306063M4[t] -0.305791642142437M5[t] -0.313160682754562M6[t] + 0.136845203060624M7[t] + 0.27605650382578M8[t] + 0.153160682754562M9[t] + 0.0355267804590934M10[t] -0.122895821071219M11[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) 6.49479693937611 0.603913 10.7545 0 0 graad -0.59211300765156 0.074532 -7.9444 0 0 M1 0.0478987639788073 0.248807 0.1925 0.848169 0.424085 M2 0.144214243672748 0.24836 0.5807 0.56424 0.28212 M3 0.0539493819894059 0.246533 0.2188 0.827728 0.413864 M4 -0.223684520306063 0.245788 -0.9101 0.367429 0.183715 M5 -0.305791642142437 0.246655 -1.2398 0.22122 0.11061 M6 -0.313160682754562 0.24723 -1.2667 0.211513 0.105756 M7 0.136845203060624 0.247572 0.5527 0.583054 0.291527 M8 0.27605650382578 0.248579 1.1105 0.272417 0.136209 M9 0.153160682754562 0.24723 0.6195 0.538575 0.269288 M10 0.0355267804590934 0.245811 0.1445 0.885701 0.44285 M11 -0.122895821071219 0.245992 -0.4996 0.619692 0.309846 Multiple Linear Regression - Regression Statistics Multiple R 0.762889719864329 R-squared 0.582000724674674 Adjusted R-squared 0.475277505442676 F-TEST (value) 5.45336552685413 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 1.05276384025910e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.388596739751561 Sum Squared Residuals 7.09734902884049 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.6 1.27288993525605 0.327110064743952 2 1.8 1.42841671571513 0.371583284284873 3 1.6 1.63420835785756 -0.0342083578575625 4 1.5 1.83026486168334 -0.330264861683343 5 1.5 1.92579164214244 -0.425791642142438 6 1.3 1.8 -0.5 7 1.4 1.421047675103 -0.021047675103001 8 1.4 1.26420247204238 0.135797527957623 9 1.3 1.14130665097116 0.158693349028842 10 1.3 1.37894055326663 -0.0789405532666269 11 1.2 1.51657445556210 -0.316574455562096 12 1.1 1.58025897586816 -0.480258975868157 13 1.4 1.50973513831665 -0.109735138316653 14 1.2 1.54683931724544 -0.346839317245438 15 1.5 1.51578575632725 -0.0157857563272512 16 1.1 1.41578575632725 -0.315785756327251 17 1.3 1.39288993525603 -0.0928899352560326 18 1.5 1.50394349617422 -0.00394349617421958 19 1.1 1.53947027663331 -0.439470276633314 20 1.4 1.61947027663331 -0.219470276633314 21 1.3 1.49657445556210 -0.196574455562095 22 1.5 1.49736315479694 0.00263684520306104 23 1.6 1.39815185403178 0.201848145968217 24 1.7 1.46183637433785 0.238163625662154 25 1.1 1.39131253678634 -0.291312536786341 26 1.6 1.48762801648028 0.112371983519718 27 1.3 1.45657445556210 -0.156574455562095 28 1.7 1.23815185403178 0.461848145968217 29 1.6 1.27446733372572 0.32553266627428 30 1.7 1.44473219540906 0.255267804590936 31 1.9 1.77631547969394 0.123684520306062 32 1.8 1.97473808122425 -0.174738081224250 33 1.9 1.85184226015303 0.0481577398469687 34 1.6 1.79341965862272 -0.193419658622719 35 1.5 1.69420835785756 -0.194208357857563 36 1.6 1.81710417892878 -0.217104178928781 37 1.6 1.80579164214243 -0.205791642142433 38 1.7 1.90210712183637 -0.202107121836374 39 2 1.87105356091819 0.128946439081813 40 2 1.53420835785756 0.465791642142437 41 1.9 1.62973513831666 0.270264861683343 42 1.7 1.91842260153031 -0.218422601530312 43 1.8 2.19079458505003 -0.39079458505003 44 1.9 2.4484284873455 -0.548428487345498 45 1.7 2.50316656856975 -0.803166568569748 46 2 2.38553266627428 -0.38553266627428 47 2.1 2.22711006474397 -0.127110064743967 48 2.4 2.23158328428487 0.168416715715126 49 2.5 2.22027074749853 0.279729252501475 50 2.5 2.43500882872278 0.0649911712772213 51 2.6 2.52237786933490 0.0776221306650962 52 2.2 2.48158917010006 -0.281589170100059 53 2.5 2.57711595055915 -0.0771159505591534 54 2.8 2.33290170688640 0.467098293113596 55 2.8 2.07237198351972 0.727628016480282 56 2.9 2.09316068275456 0.806839317245438 57 3 2.20711006474397 0.792889935256033 58 3.1 2.44474396703944 0.655256032960565 59 2.9 2.46395526780459 0.436044732195408 60 2.7 2.40921718658034 0.290782813419658 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.340523366763010 0.681046733526019 0.65947663323699 17 0.190631672118788 0.381263344237575 0.809368327881212 18 0.120872496787426 0.241744993574852 0.879127503212574 19 0.0918613788941034 0.183722757788207 0.908138621105897 20 0.0466448579465752 0.0932897158931503 0.953355142053425 21 0.0221573365431769 0.0443146730863538 0.977842663456823 22 0.0113052098023253 0.0226104196046507 0.988694790197675 23 0.0100507047525413 0.0201014095050825 0.989949295247459 24 0.0161866934512411 0.0323733869024822 0.98381330654876 25 0.0155884306828729 0.0311768613657457 0.984411569317127 26 0.0078012151007799 0.0156024302015598 0.99219878489922 27 0.00500861981846809 0.0100172396369362 0.994991380181532 28 0.00411414812951484 0.00822829625902968 0.995885851870485 29 0.00198377694342023 0.00396755388684045 0.99801622305658 30 0.00110171219642977 0.00220342439285953 0.99889828780357 31 0.00273764954004487 0.00547529908008975 0.997262350459955 32 0.00215394006198671 0.00430788012397341 0.997846059938013 33 0.00195784044134873 0.00391568088269747 0.998042159558651 34 0.00103699861268964 0.00207399722537929 0.99896300138731 35 0.000562896715405222 0.00112579343081044 0.999437103284595 36 0.000365964542630008 0.000731929085260016 0.99963403545737 37 0.000226472700034684 0.000452945400069369 0.999773527299965 38 0.000116706619912909 0.000233413239825819 0.999883293380087 39 8.8398809469723e-05 0.000176797618939446 0.99991160119053 40 8.23543010185547e-05 0.000164708602037109 0.999917645698981 41 5.48794215708293e-05 0.000109758843141659 0.999945120578429 42 0.000315931455492030 0.000631862910984061 0.999684068544508 43 0.000299767406377822 0.000599534812755643 0.999700232593622 44 0.000185421575543637 0.000370843151087274 0.999814578424456 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.586206896551724 NOK 5% type I error level 24 0.827586206896552 NOK 10% type I error level 25 0.862068965517241 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/101gqe1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/101gqe1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/11qnw1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/11qnw1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/2jdtu1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/2jdtu1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/3i2ov1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/3i2ov1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/4nw9c1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/4nw9c1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/590dv1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/590dv1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/6oquy1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/6oquy1258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/7x4w51258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/7x4w51258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/8i4s51258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/8i4s51258564224.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/9c4jr1258564224.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258564624mxbhrpyn2asgicu/9c4jr1258564224.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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