ws777

*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, 20 Nov 2009 05:04:49 -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/20/t12587187798hgeaf8pu1d13bk.htm/, Retrieved Fri, 20 Nov 2009 13:06:34 +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/20/t12587187798hgeaf8pu1d13bk.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 «

100 0 95.84395716 0 105.5073942 1 118.1540031 1 101.8612953 1 109.8419174 1 105.6348802 1 112.927078 1 133.0698623 1 125.6756757 1 146.736359 1 142.5803162 1 106.1448241 1 126.5170831 1 132.7893932 1 121.2391637 1 114.5079041 1 146.1499235 1 146.1244263 1 128.5058644 1 155.5838858 1 125.0382458 1 136.8944416 1 142.2233554 1 117.7715451 1 120.627231 1 127.7664457 1 135.1096379 1 105.7113717 1 117.9245283 1 120.754717 1 107.572667 1 130.4436512 1 107.2157063 1 105.0739419 1 130.1121877 1 109.6379398 1 116.7261601 1 97.11881693 0 140.8975013 1 108.2865885 1 97.65425803 0 112.0346762 1 123.0494646 1 112.4171341 1 116.4966854 1 104.6914839 1 122.2335543 1 99.79602244 0 96.71086181 0 112.3151453 1 102.5497195 1 104.5385008 1 122.0805711 1 80.64762876 0 91.40744518 0 99.51555329 0 106.527282 1 98.49566548 0 106.7567568 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 103.383194533056 + 25.3980395469445X[t] -11.9519519732223M1[t] -7.33695962722222M2[t] -8.60218710461113M3[t] -5.19122898000001M4[t] -21.800102M5[t] -4.97138650461111M6[t] -10.6623604786111M7[t] -11.0091223346111M8[t] + 2.50439116738889M9[t] -12.59051504M10[t] -5.32324779461111M11[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) 103.383194533056 7.422665 13.928 0 0 X 25.3980395469445 4.751874 5.3448 3e-06 1e-06 M1 -11.9519519732223 8.285176 -1.4426 0.155773 0.077886 M2 -7.33695962722222 8.285176 -0.8856 0.380367 0.190184 M3 -8.60218710461113 8.120006 -1.0594 0.29484 0.14742 M4 -5.19122898000001 8.064198 -0.6437 0.522874 0.261437 M5 -21.800102 8.064198 -2.7033 0.009525 0.004763 M6 -4.97138650461111 8.120006 -0.6122 0.54333 0.271665 M7 -10.6623604786111 8.120006 -1.3131 0.195525 0.097763 M8 -11.0091223346111 8.120006 -1.3558 0.181641 0.090821 M9 2.50439116738889 8.120006 0.3084 0.759124 0.379562 M10 -12.59051504 8.064198 -1.5613 0.125165 0.062583 M11 -5.32324779461111 8.120006 -0.6556 0.515296 0.257648 Multiple Linear Regression - Regression Statistics Multiple R 0.700889678005733 R-squared 0.49124634073498 Adjusted R-squared 0.361351789433272 F-TEST (value) 3.78188565888308 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.000490109125249605 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.7506163904545 Sum Squared Residuals 7641.17626181678 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 91.4312425598335 8.5687574401665 2 95.84395716 96.0462349058333 -0.202277745833311 3 105.5073942 120.179046975389 -14.6716527753889 4 118.1540031 123.5900051 -5.436002 5 101.8612953 106.98113208 -5.11983677999997 6 109.8419174 123.809847575389 -13.9679301753889 7 105.6348802 118.118873601389 -12.4839934013889 8 112.927078 117.772111745389 -4.84503374538888 9 133.0698623 131.285625247389 1.78423705261114 10 125.6756757 116.19071904 9.48495666000002 11 146.736359 123.457986285389 23.2783727146111 12 142.5803162 128.78123408 13.7990821200000 13 106.1448241 116.829282106778 -10.6844580067777 14 126.5170831 121.444274452778 5.07280864722221 15 132.7893932 120.179046975389 12.6103462246111 16 121.2391637 123.5900051 -2.35084139999999 17 114.5079041 106.98113208 7.52677202 18 146.1499235 123.809847575389 22.3400759246111 19 146.1244263 118.118873601389 28.0055526986111 20 128.5058644 117.772111745389 10.7337526546111 21 155.5838858 131.285625247389 24.2982605526111 22 125.0382458 116.19071904 8.84752676 23 136.8944416 123.457986285389 13.4364553146111 24 142.2233554 128.78123408 13.44212132 25 117.7715451 116.829282106778 0.942262993222257 26 120.627231 121.444274452778 -0.817043452777788 27 127.7664457 120.179046975389 7.58739872461112 28 135.1096379 123.5900051 11.5196328 29 105.7113717 106.98113208 -1.26976038000000 30 117.9245283 123.809847575389 -5.88531927538889 31 120.754717 118.118873601389 2.6358433986111 32 107.572667 117.772111745389 -10.1994447453889 33 130.4436512 131.285625247389 -0.841974047388886 34 107.2157063 116.19071904 -8.97501274000001 35 105.0739419 123.457986285389 -18.3840443853889 36 130.1121877 128.78123408 1.33095361999999 37 109.6379398 116.829282106778 -7.19134230677774 38 116.7261601 121.444274452778 -4.71811435277778 39 97.11881693 94.7810074284444 2.33780950155557 40 140.8975013 123.5900051 17.3074962 41 108.2865885 106.98113208 1.30545641999999 42 97.65425803 98.4118080284444 -0.757549998444434 43 112.0346762 118.118873601389 -6.08419740138889 44 123.0494646 117.772111745389 5.2773528546111 45 112.4171341 131.285625247389 -18.8684911473889 46 116.4966854 116.19071904 0.305966360000001 47 104.6914839 123.457986285389 -18.7665023853889 48 122.2335543 128.78123408 -6.54767978000001 49 99.79602244 91.4312425598333 8.36477988016673 50 96.71086181 96.0462349058333 0.664626904166679 51 112.3151453 120.179046975389 -7.86390167538889 52 102.5497195 123.5900051 -21.0402856 53 104.5385008 106.98113208 -2.44263128000001 54 122.0805711 123.809847575389 -1.72927647538890 55 80.64762876 92.7208340544444 -12.0732052944444 56 91.40744518 92.3740721984444 -0.966627018444427 57 99.51555329 105.887585700444 -6.37203241044442 58 106.527282 116.19071904 -9.66343704 59 98.49566548 98.0599467384444 0.435718741555576 60 106.7567568 128.78123408 -22.02447728 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.594949849927688 0.810100300144623 0.405050150072312 17 0.492496357067971 0.984992714135943 0.507503642932029 18 0.77666262533138 0.44667474933724 0.22333737466862 19 0.944301637559277 0.111396724881446 0.055698362440723 20 0.927217396528094 0.145565206943812 0.0727826034719059 21 0.967081791740485 0.0658364165190301 0.0329182082595150 22 0.953087572762044 0.093824854475913 0.0469124272379565 23 0.968044488099713 0.0639110238005738 0.0319555119002869 24 0.97379443843920 0.0524111231215985 0.0262055615607992 25 0.955695348943648 0.0886093021127034 0.0443046510563517 26 0.927631238181818 0.144737523636364 0.0723687618181819 27 0.91330213914412 0.173395721711760 0.0866978608558802 28 0.913578570672558 0.172842858654885 0.0864214293274425 29 0.868053996828366 0.263892006343268 0.131946003171634 30 0.821116195183794 0.357767609632412 0.178883804816206 31 0.802244469340347 0.395511061319305 0.197755530659653 32 0.772711125744375 0.454577748511251 0.227288874255625 33 0.779319097926239 0.441361804147522 0.220680902073761 34 0.745566740058642 0.508866519882715 0.254433259941358 35 0.81026436976723 0.379471260465539 0.189735630232769 36 0.808465055523225 0.383069888953551 0.191534944476775 37 0.748063010851904 0.503873978296192 0.251936989148096 38 0.650223267793376 0.699553464413248 0.349776732206624 39 0.5483229644695 0.903354071061 0.4516770355305 40 0.911553620355287 0.176892759289427 0.0884463796447134 41 0.845193106663552 0.309613786672896 0.154806893336448 42 0.746371062184199 0.507257875631601 0.253628937815801 43 0.699043094793905 0.601913810412191 0.300956905206095 44 0.755852097940665 0.48829580411867 0.244147902059335 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 5 0.172413793103448 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/1065cq1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/1065cq1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/1u2xv1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/1u2xv1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/2e4ej1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/2e4ej1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/315ba1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/315ba1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/449nb1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/449nb1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/585221258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/585221258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/6d3sm1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/6d3sm1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/7e87e1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/7e87e1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/8alwc1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/8alwc1258718684.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/9ta1h1258718684.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587187798hgeaf8pu1d13bk/9ta1h1258718684.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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