workshop 3

*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 08:22:30 -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/t1258730623wo2mbbhzm8o6iip.htm/, Retrieved Fri, 20 Nov 2009 16:23:55 +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/t1258730623wo2mbbhzm8o6iip.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:

model 4

Dataseries X:

» Textbox « » Textfile « » CSV «

109.5 120.1 109.5 110.2 108.8 108.2 116 132.9 109.5 109.5 110.2 108.8 111.2 128.1 116 109.5 109.5 110.2 112.1 129.3 111.2 116 109.5 109.5 114 132.5 112.1 111.2 116 109.5 119.1 131 114 112.1 111.2 116 114.1 124.9 119.1 114 112.1 111.2 115.1 120.8 114.1 119.1 114 112.1 115.4 122 115.1 114.1 119.1 114 110.8 122.1 115.4 115.1 114.1 119.1 116 127.4 110.8 115.4 115.1 114.1 119.2 135.2 116 110.8 115.4 115.1 126.5 137.3 119.2 116 110.8 115.4 127.8 135 126.5 119.2 116 110.8 131.3 136 127.8 126.5 119.2 116 140.3 138.4 131.3 127.8 126.5 119.2 137.3 134.7 140.3 131.3 127.8 126.5 143 138.4 137.3 140.3 131.3 127.8 134.5 133.9 143 137.3 140.3 131.3 139.9 133.6 134.5 143 137.3 140.3 159.3 141.2 139.9 134.5 143 137.3 170.4 151.8 159.3 139.9 134.5 143 175 155.4 170.4 159.3 139.9 134.5 175.8 156.6 175 170.4 159.3 139.9 180.9 161.6 175.8 175 170.4 159.3 180.3 160.7 180.9 175.8 175 170.4 169.6 156 180.3 180.9 175.8 175 172.3 159.5 169.6 180.3 180.9 175.8 184.8 168.7 172.3 169.6 180.3 180.9 177.7 169.9 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] = -28.8691767006593 + 0.722631087194356X[t] + 0.68166492131029Y1[t] + 0.0357487587076676Y2[t] -0.136738073603211Y3[t] -0.0259058241864742Y4[t] -4.2607956589844M1[t] -2.90302129402693M2[t] -6.12901499458149M3[t] -3.79239104204496M4[t] -4.25182903906736M5[t] -13.9408302154570M6[t] -11.8293883425912M7[t] -0.0234283729133178M8[t] -2.79353232120853M9[t] -2.46252649385462M10[t] + 5.15904113856754M11[t] -0.3517093324785t + 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) -28.8691767006593 11.622676 -2.4839 0.017522 0.008761 X 0.722631087194356 0.141729 5.0987 1e-05 5e-06 Y1 0.68166492131029 0.161933 4.2095 0.000151 7.5e-05 Y2 0.0357487587076676 0.198751 0.1799 0.858212 0.429106 Y3 -0.136738073603211 0.201583 -0.6783 0.501679 0.250839 Y4 -0.0259058241864742 0.132452 -0.1956 0.845976 0.422988 M1 -4.2607956589844 6.930106 -0.6148 0.542335 0.271168 M2 -2.90302129402693 7.122116 -0.4076 0.68585 0.342925 M3 -6.12901499458149 7.187253 -0.8528 0.399135 0.199567 M4 -3.79239104204496 7.243078 -0.5236 0.603605 0.301803 M5 -4.25182903906736 7.064731 -0.6018 0.550856 0.275428 M6 -13.9408302154570 6.960589 -2.0028 0.052368 0.026184 M7 -11.8293883425912 7.515352 -1.574 0.123771 0.061885 M8 -0.0234283729133178 7.466075 -0.0031 0.997513 0.498756 M9 -2.79353232120853 7.628985 -0.3662 0.716266 0.358133 M10 -2.46252649385462 7.717026 -0.3191 0.751396 0.375698 M11 5.15904113856754 7.651288 0.6743 0.504221 0.25211 t -0.3517093324785 0.176384 -1.994 0.053365 0.026682 Multiple Linear Regression - Regression Statistics Multiple R 0.990946497626736 R-squared 0.981974961158695 Adjusted R-squared 0.973911127992848 F-TEST (value) 121.775208013687 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 10.1208126903771 Sum Squared Residuals 3892.3722815205 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 109.5 114.208021387977 -4.70802138797678 2 116 124.231763407891 -8.23176340789096 3 111.2 121.675701642503 -10.4757016425032 4 112.1 121.506282953435 -9.40628295343544 5 114 122.560662011918 -8.56066201191806 6 119.1 113.251297001668 5.84870299833185 7 114.1 114.148677340249 -0.0486773402491406 8 115.1 119.131017001195 -4.03101700119537 9 115.4 116.632696911496 -1.23269691149615 10 110.8 117.476075414857 -6.67607541485681 11 116 125.443735513845 -9.44373551384466 12 119.2 128.882793577405 -9.68279357740541 13 126.5 128.776258553842 -2.27625855384229 14 127.8 132.618950847725 -4.81895084772499 15 131.3 130.338737116856 0.961262883144313 16 140.3 135.409180382386 4.89081961761399 17 137.3 137.81753096529 -0.517530965289984 18 143 128.215038714425 14.7849612855745 19 134.5 129.179862090702 5.32013790929774 20 139.9 135.004001298371 4.89599870162908 21 159.3 140.049620859356 19.2503791406442 22 170.4 162.120760076697 8.27923992330312 23 175 179.733910744141 -4.73391074414088 24 175.8 175.830177358901 -0.0301773589012391 25 180.9 173.520238424300 7.37976157569961 26 180.3 176.464475796908 3.83552420309158 27 169.6 169.035169120544 0.564830879455514 28 172.3 165.515939797812 6.78406020218753 29 184.8 172.760905180676 12.0390948193237 30 177.7 173.683326023397 4.01667397660301 31 184.6 170.958096626394 13.6419033736063 32 211.4 196.644944783576 14.7550552164243 33 215.3 216.226327676505 -0.926327676504908 34 215.9 222.675778177690 -6.77577817768954 35 244.7 238.285085533756 6.4149144662443 36 259.3 262.184172001775 -2.88417200177477 37 289 285.858135863837 3.14186413616275 38 310.9 307.580188792929 3.31981120707104 39 321 313.781592840499 7.21840715950129 40 315.1 318.489033401315 -3.38903340131464 41 333.2 324.055412474194 9.14458752580584 42 314.1 321.953370901288 -7.85337090128819 43 284.7 294.39778947713 -9.69778947712996 44 273.9 273.339708124726 0.560291875273653 45 216 233.091354552643 -17.0913545526431 46 196.4 191.227386330757 5.17261366924323 47 190.9 183.137268208259 7.76273179174125 48 206.4 193.802857061919 12.5971429380814 49 196.3 199.837345770043 -3.53734577004328 50 199.5 193.604621154547 5.89537884545333 51 198.9 197.168799279598 1.73120072040208 52 214.4 213.279563465051 1.12043653494857 53 214.2 226.305489367921 -12.1054893679215 54 187.6 204.396967359221 -16.7969673592212 55 180.6 189.815574465525 -9.21557446552495 56 172.2 188.380328792132 -16.1803287921317 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.247942564743233 0.495885129486466 0.752057435256767 22 0.154035439609145 0.308070879218290 0.845964560390855 23 0.128076090521194 0.256152181042387 0.871923909478806 24 0.0607124989087606 0.121424997817521 0.93928750109124 25 0.0536044603532564 0.107208920706513 0.946395539646744 26 0.0369283376177803 0.0738566752355605 0.96307166238222 27 0.0813211139393945 0.162642227878789 0.918678886060605 28 0.100083360059658 0.200166720119316 0.899916639940342 29 0.125349907437759 0.250699814875518 0.874650092562241 30 0.737100226378976 0.525799547242048 0.262899773621024 31 0.677962727552354 0.644074544895292 0.322037272447646 32 0.607099831481262 0.785800337037475 0.392900168518738 33 0.638170225038837 0.723659549922326 0.361829774961163 34 0.738999189889592 0.522001620220816 0.261000810110408 35 0.765695037312388 0.468609925375225 0.234304962687612 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 1 0.0666666666666667 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/10fipx1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/10fipx1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/1znvo1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/1znvo1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/2ad2n1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/2ad2n1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/3lc261258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/3lc261258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/44q2m1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/44q2m1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/5avg21258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/5avg21258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/62r9i1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/62r9i1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/7ly4d1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/7ly4d1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/8s94o1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/8s94o1258730546.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/9og1o1258730546.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258730623wo2mbbhzm8o6iip/9og1o1258730546.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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