VerbeteringModel5

*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: Mon, 23 Nov 2009 12:19:36 -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/23/t12590041247d26hs51fgzkxc2.htm/, Retrieved Mon, 23 Nov 2009 20:22:16 +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/23/t12590041247d26hs51fgzkxc2.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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User-defined keywords:

Dataseries X:

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100.49 1.9 100.16 100.03 99.72 2 100.49 100.25 100.14 2.3 99.72 99.6 98.48 2.8 100.14 100.16 100.38 2.4 98.48 100.49 101.45 2.3 100.38 99.72 98.42 2.7 101.45 100.14 98.6 2.7 98.42 98.48 100.06 2.9 98.6 100.38 98.62 3 100.06 101.45 100.84 2.2 98.62 98.42 100.02 2.3 100.84 98.6 97.95 2.8 100.02 100.06 98.32 2.8 97.95 98.62 98.27 2.8 98.32 100.84 97.22 2.2 98.27 100.02 99.28 2.6 97.22 97.95 100.38 2.8 99.28 98.32 99.02 2.5 100.38 98.27 100.32 2.4 99.02 97.22 99.81 2.3 100.32 99.28 100.6 1.9 99.81 100.38 101.19 1.7 100.6 99.02 100.47 2 101.19 100.32 101.77 2.1 100.47 99.81 102.32 1.7 101.77 100.6 102.39 1.8 102.32 101.19 101.16 1.8 102.39 100.47 100.63 1.8 101.16 101.77 101.48 1.3 100.63 102.32 101.44 1.3 101.48 102.39 100.09 1.3 101.44 101.16 100.7 1.2 100.09 100.63 100.78 1.4 100.7 101.48 99.81 2.2 100.78 101.44 98.45 2.9 99.81 100.09 98.49 3.1 98.45 100.7 97.48 3.5 98.49 100.78 97.91 3.6 97.48 99.81 96.94 4.4 97.91 98.45 98.53 4.1 96.94 98.49 96.82 5.1 98.53 97.48 95.7 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] = + 57.3427945489103 -0.49999670903731X[t] + 0.571499960545084Y1[t] -0.139851953533137Y4[t] + 0.650113977389304M1[t] + 0.71191289906699M2[t] + 1.20339151560654M3[t] -0.388902182650499M4[t] + 1.70117362711875M5[t] + 1.07641684010856M6[t] -0.460083159944192M7[t] + 0.358538583379871M8[t] + 1.46442617656104M9[t] + 0.701154255385232M10[t] + 1.33629220612652M11[t] -0.00920527843402831t + 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) 57.3427945489103 13.295668 4.3129 0.000102 5.1e-05 X -0.49999670903731 0.129826 -3.8513 0.000415 0.000207 Y1 0.571499960545084 0.132264 4.3209 1e-04 5e-05 Y4 -0.139851953533137 0.101748 -1.3745 0.176942 0.088471 M1 0.650113977389304 0.606476 1.072 0.290166 0.145083 M2 0.71191289906699 0.603108 1.1804 0.24481 0.122405 M3 1.20339151560654 0.607928 1.9795 0.054671 0.027335 M4 -0.388902182650499 0.589216 -0.66 0.513015 0.256508 M5 1.70117362711875 0.625057 2.7216 0.009573 0.004787 M6 1.07641684010856 0.589786 1.8251 0.07546 0.03773 M7 -0.460083159944192 0.596563 -0.7712 0.445107 0.222553 M8 0.358538583379871 0.615889 0.5821 0.563734 0.281867 M9 1.46442617656104 0.658338 2.2244 0.031827 0.015914 M10 0.701154255385232 0.640525 1.0947 0.280215 0.140108 M11 1.33629220612652 0.623133 2.1445 0.038124 0.019062 t -0.00920527843402831 0.008074 -1.1401 0.261005 0.130503 Multiple Linear Regression - Regression Statistics Multiple R 0.899016799661889 R-squared 0.808231206074305 Adjusted R-squared 0.73631790835217 F-TEST (value) 11.2389673631324 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 5.60008373007292e-10 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.870721422195195 Sum Squared Residuals 30.3262318027849 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100.49 100.285754636971 0.204245363029248 2 99.72 100.446176166513 -0.726176166513105 3 100.14 100.429299292084 -0.289299292084255 4 98.48 98.739514850325 -0.259514850324912 5 100.38 100.025542986104 0.354457013895707 6 101.45 100.63511652082 0.814883479180028 7 98.42 99.4421796960176 -1.02217969601759 8 98.6 98.752105523321 -0.152105523321035 9 100.06 99.5859397774459 0.474060222554142 10 98.62 99.4482112590477 -0.828211259047656 11 100.84 100.074932774605 0.765067225394747 12 100.02 99.922992179915 0.0970078200848986 13 97.95 99.6410887045464 -1.69108870454636 14 98.32 98.7120642425494 -0.392064242549426 15 98.27 99.095321229213 -0.825321229213055 16 97.22 97.8799238818143 -0.659923881814293 17 99.28 99.4502143147758 -0.170214314775844 18 100.38 99.8417976034398 0.538202396560212 19 99.02 99.0817338919404 -0.0617338919404416 20 100.32 99.3107546326027 1.00924536739731 21 99.81 100.912291542684 -1.10229154268390 22 100.6 99.8945108979245 0.705489102075445 23 101.19 101.262126537675 -0.0721265376749542 24 100.47 99.9220074775317 0.547992522468268 25 101.77 100.172761030293 1.59723896970728 26 102.32 101.057820262569 1.26217973743127 27 102.39 101.721906255486 0.668093744514241 28 101.16 100.261105682577 0.898894317423284 29 100.63 101.457223722848 -0.827223722848403 30 101.48 100.693446458391 0.786553541609285 31 101.44 99.62372650962 1.81627349038006 32 100.09 100.582300878934 -0.492300878933925 33 100.7 101.031579453221 -0.3315794532215 34 100.78 100.388843727234 0.391156272766464 35 99.81 100.666093107296 -0.856093107295881 36 98.45 98.6050431019502 -0.155043101950216 37 98.49 98.2834028211015 0.206597178898490 38 97.48 98.1476696228694 -0.667669622869383 39 97.91 98.1383847248478 -0.228384724847794 40 96.94 96.5728320207663 0.367167979233672 41 98.53 98.2437525249427 0.286247475057317 42 96.82 98.1597291607963 -1.33972916079632 43 95.76 95.226624913432 0.53337508656794 44 95.27 95.5159081441677 -0.245908144167734 45 97.32 96.3601892266487 0.959810773351254 46 96.68 96.9484341157943 -0.268434115794253 47 97.87 97.706847580424 0.163152419576088 48 97.42 97.909957240603 -0.489957240602947 49 97.94 98.2569928070887 -0.316992807088660 50 99.52 98.9962697054993 0.523730294500647 51 100.99 100.315088498369 0.674911501630864 52 99.92 100.266623564518 -0.346623564517751 53 101.97 101.613266451329 0.356733548671224 54 101.58 102.379910256553 -0.799910256553205 55 99.54 100.80573498899 -1.26573498898997 56 100.83 100.948930820975 -0.118930820974616 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.303963966758769 0.607927933517538 0.696036033241231 20 0.365638215830075 0.73127643166015 0.634361784169925 21 0.517645400948254 0.964709198103492 0.482354599051746 22 0.469888852319322 0.939777704638644 0.530111147680678 23 0.407348377867632 0.814696755735263 0.592651622132368 24 0.372634449459053 0.745268898918107 0.627365550540947 25 0.711305071737905 0.57738985652419 0.288694928262095 26 0.718494030811575 0.56301193837685 0.281505969188425 27 0.624431433943117 0.751137132113766 0.375568566056883 28 0.550201835912548 0.899596328174904 0.449798164087452 29 0.626577771299234 0.746844457401533 0.373422228700766 30 0.566666647168356 0.866666705663287 0.433333352831644 31 0.821574191812326 0.356851616375347 0.178425808187673 32 0.800072654544613 0.399854690910774 0.199927345455387 33 0.80722012553282 0.38555974893436 0.19277987446718 34 0.729837799786123 0.540324400427754 0.270162200213877 35 0.625255334815668 0.749489330368665 0.374744665184333 36 0.524650119011385 0.95069976197723 0.475349880988615 37 0.807635883135567 0.384728233728866 0.192364116864433 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/23/t12590041247d26hs51fgzkxc2/1089ia1259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/1089ia1259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/1bl7h1259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/1bl7h1259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/27pxr1259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/27pxr1259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/3nat91259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/3nat91259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/473991259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/473991259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/5ifht1259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/5ifht1259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/6u9qt1259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/6u9qt1259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/7ol7q1259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/7ol7q1259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/8qbo71259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/8qbo71259003972.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/9v5961259003972.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12590041247d26hs51fgzkxc2/9v5961259003972.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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