trend

*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, 28 Nov 2009 06:21:44 -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/28/t1259414869oqmvp9ufuk22bh3.htm/, Retrieved Sat, 28 Nov 2009 14:28:01 +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/28/t1259414869oqmvp9ufuk22bh3.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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4.3 29 3.9 31 4 31 4.3 33 4.8 37 4.4 30 4.3 20 4.7 19 4.7 17 4.9 22 5 12 4.2 25 4.3 25 4.8 29 4.8 32 4.8 31 4.2 28 4.6 28 4.8 28 4.5 32 4.4 35 4.3 30 3.9 32 3.7 38 4 37 4.1 28 3.7 34 3.8 35 3.8 32 3.8 39 3.3 37 3.3 38 3.3 35 3.2 25 3.4 25 4.2 26 4.9 13 5.1 19 5.5 17 5.6 21 6.4 23 6.1 18 7.1 12 7.8 7 7.9 4 7.4 14 7.5 16 6.8 13 5.2 13 4.7 10 4.1 19 3.9 13 2.6 14 2.7 25 1.8 28 1 30 0.3 31 1.3 42 1 41 1.1 38

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 Consumentenprijsindex[t] = + 8.77006033367237 -0.127910580491935Consumentenvertrouwen[t] -0.411561028009571M1[t] -0.398545359123509M2[t] -0.0562158326632557M3[t] + 0.0367998362228047M4[t] -0.0246023787927469M5[t] + 0.121905986683635M6[t] -0.288810085906110M7[t] -0.230212300921662M8[t] -0.43952509642915M9[t] -0.0251061504608312M10[t] -0.231165294263479M11[t] -0.0330156688860606t + 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) 8.77006033367237 0.863181 10.1602 0 0 Consumentenvertrouwen -0.127910580491935 0.017724 -7.2168 0 0 M1 -0.411561028009571 0.782238 -0.5261 0.601323 0.300662 M2 -0.398545359123509 0.780763 -0.5105 0.612173 0.306086 M3 -0.0562158326632557 0.773942 -0.0726 0.942411 0.471205 M4 0.0367998362228047 0.772864 0.0476 0.962229 0.481115 M5 -0.0246023787927469 0.771719 -0.0319 0.974706 0.487353 M6 0.121905986683635 0.770194 0.1583 0.874929 0.437465 M7 -0.288810085906110 0.772205 -0.374 0.710118 0.355059 M8 -0.230212300921662 0.771228 -0.2985 0.766665 0.383333 M9 -0.43952509642915 0.771835 -0.5695 0.571818 0.285909 M10 -0.0251061504608312 0.768855 -0.0327 0.974092 0.487046 M11 -0.231165294263479 0.769878 -0.3003 0.76533 0.382665 t -0.0330156688860606 0.009545 -3.4589 0.00118 0.00059 Multiple Linear Regression - Regression Statistics Multiple R 0.740997145924228 R-squared 0.549076770267851 Adjusted R-squared 0.421641944473983 F-TEST (value) 4.30868694524689 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.000109703653049742 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.21441275420385 Sum Squared Residuals 67.8407235283572 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.3 4.61607680251063 -0.316076802510629 2 3.9 4.34025564152675 -0.440255641526751 3 4 4.64956949910094 -0.649569499100941 4 4.3 4.45374833811707 -0.153748338117071 5 4.8 3.84768813224772 0.952311867752282 6 4.4 4.85655489228158 -0.456554892281584 7 4.3 5.69192895572513 -1.39192895572513 8 4.7 5.84542165231545 -1.14542165231545 9 4.7 5.85891434890577 -1.15891434890577 10 4.9 5.60076472352836 -0.700764723528356 11 5 6.640795715759 -1.640795715759 12 4.2 5.17610779474126 -0.976107794741261 13 4.3 4.73153109784563 -0.431531097845629 14 4.8 4.19988877587789 0.60011122412211 15 4.8 4.12547089197628 0.674529108023722 16 4.8 4.31338147246821 0.486618527531787 17 4.2 4.60269533004241 -0.402695330042405 18 4.6 4.71618802663273 -0.116188026632728 19 4.8 4.27245628515692 0.527543714843078 20 4.5 3.78639607928757 0.713603920712431 21 4.4 3.16033587341822 1.23966412658178 22 4.3 4.18129205296015 0.118707947039852 23 3.9 3.68639607928757 0.213603920712431 24 3.7 3.11708222171338 0.582917778286623 25 4 2.80041610530968 1.19958389469032 26 4.1 3.9316113297371 0.168388670262902 27 3.7 3.47346170435968 0.22653829564032 28 3.8 3.40555112386774 0.394448876132255 29 3.8 3.69486498144194 0.105135018558062 30 3.8 2.91298361458871 0.887016385411286 31 3.3 2.72507303409678 0.574926965903221 32 3.3 2.62274456970323 0.67725543029677 33 3.3 2.76414784678549 0.535852153214513 34 3.2 4.42465692878710 -1.22465692878710 35 3.4 4.18558211609839 -0.785582116098387 36 4.2 4.25582116098387 -0.0558211609838709 37 4.9 5.47408201048339 -0.574082010483395 38 5.1 4.68661852753179 0.413381472468213 39 5.5 5.25175354608985 0.248246453910151 40 5.6 4.80011122412211 0.79988877587789 41 6.4 4.44987217923662 1.95012782076338 42 6.1 5.20291777828662 0.897082221713376 43 7.1 5.52664951976243 1.57335048023757 44 7.8 6.19178453832049 1.60821546167951 45 7.9 6.33318781540275 1.56681218459725 46 7.4 5.43548528756565 1.96451471243435 47 7.5 4.94058931389308 2.55941068610692 48 6.8 5.5224706807463 1.2775293192537 49 5.2 5.07789398385067 0.122106016149333 50 4.7 5.44162572532647 -0.741625725326475 51 4.1 4.59974435847325 -0.499744358473252 52 3.9 5.42720784142486 -1.52720784142486 53 2.6 5.20487937703131 -2.60487937703131 54 2.7 3.91135568821035 -1.21135568821035 55 1.8 3.08389220525874 -1.28389220525874 56 1 2.85365316037326 -1.85365316037326 57 0.3 2.48341411548777 -2.18341411548777 58 1.3 1.45780100715874 -0.157801007158744 59 1 1.34663677496197 -0.34663677496197 60 1.1 1.92851814181519 -0.828518141815194 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00601105728789791 0.0120221145757958 0.993988942712102 18 0.00149221966434616 0.00298443932869231 0.998507780335654 19 0.00325494868084089 0.00650989736168179 0.99674505131916 20 0.00449610903192174 0.00899221806384347 0.995503890968078 21 0.00196897975083746 0.00393795950167491 0.998031020249163 22 0.00128928427086131 0.00257856854172261 0.998710715729139 23 0.00105031149637098 0.00210062299274197 0.998949688503629 24 0.000351217552992441 0.000702435105984882 0.999648782447008 25 0.000123671394672695 0.00024734278934539 0.999876328605327 26 4.93473101351366e-05 9.86946202702732e-05 0.999950652689865 27 2.96017413468292e-05 5.92034826936584e-05 0.999970398258653 28 1.53669387616298e-05 3.07338775232595e-05 0.999984633061238 29 6.84612636738422e-06 1.36922527347684e-05 0.999993153873633 30 2.20473932456749e-06 4.40947864913499e-06 0.999997795260675 31 1.30367942266505e-06 2.60735884533011e-06 0.999998696320577 32 7.80742602943792e-07 1.56148520588758e-06 0.999999219257397 33 4.98082476407894e-07 9.96164952815788e-07 0.999999501917524 34 4.68111827051134e-06 9.36223654102267e-06 0.99999531888173 35 7.73488289885961e-05 0.000154697657977192 0.999922651171011 36 0.000736018450537507 0.00147203690107501 0.999263981549462 37 0.0107152848602091 0.0214305697204182 0.989284715139791 38 0.018149448436161 0.036298896872322 0.981850551563839 39 0.136318486445771 0.272636972891542 0.863681513554229 40 0.215844643746220 0.431689287492441 0.78415535625378 41 0.548621916185744 0.902756167628512 0.451378083814256 42 0.483478757072884 0.966957514145769 0.516521242927116 43 0.442249107176133 0.884498214352267 0.557750892823867 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.703703703703704 NOK 5% type I error level 22 0.814814814814815 NOK 10% type I error level 22 0.814814814814815 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/10dhfd1259414499.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/10dhfd1259414499.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/1buor1259414498.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/1buor1259414498.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/2map01259414498.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/2map01259414498.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/3qw5n1259414498.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/3qw5n1259414498.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/4zycu1259414498.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/4zycu1259414498.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/5a7dg1259414499.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/5a7dg1259414499.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/6zmo01259414499.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/6zmo01259414499.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/71dyf1259414499.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/71dyf1259414499.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/87y5b1259414499.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/87y5b1259414499.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/9efd51259414499.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t1259414869oqmvp9ufuk22bh3/9efd51259414499.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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