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*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 08:27:12 -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/t12589901520rgtu8m74dc34in.htm/, Retrieved Mon, 23 Nov 2009 16:29:24 +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/t12589901520rgtu8m74dc34in.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:

1=lichten aan 0=lichten niet aan

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

105,29 0 101,23 0 102,33 0 100,26 0 104,13 0 103,54 0 100,02 0 98,66 0 108,64 0 105,67 0 102,66 0 100,3 0 95,13 0 93,2 0 102,84 0 101,36 0 102,55 0 103,12 0 96,3 0 99,13 0 102,23 0 104,3 0 99,58 0 98,45 0 96,23 0 97,62 0 102,32 0 105,23 0 100,05 0 102,66 0 100,98 0 99,2 0 98,36 0 102,56 0 97,33 0 96,22 0 99,22 0 102,32 0 104,22 0 100,06 0 107,23 0 99,62 0 98,32 1 101,23 1 102,33 1 100,6 1 95,63 1 94,63 1 95,66 1 100,78 1 90,36 1 95,45 1 103,65 1 99,89 1 97,68 1 99,62 1 98,33 1 96,23 1 102,65 1 99,35 1 92,65 1 100,6 1 97,67 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 98.8061568627452 -2.54039215686275X[t] -0.596026143790822M1[t] + 1.33230718954248M2[t] + 1.99730718954249M3[t] + 2.17392156862745M4[t] + 5.22392156862744M5[t] + 3.46792156862745M6[t] + 0.870000000000002M7[t] + 1.77800000000000M8[t] + 4.188M9[t] + 4.08199999999999M10[t] + 1.78000000000000M11[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) 98.8061568627452 1.449077 68.1856 0 0 X -2.54039215686275 0.853877 -2.9751 0.004501 0.002251 M1 -0.596026143790822 1.907629 -0.3124 0.756003 0.378001 M2 1.33230718954248 1.907629 0.6984 0.488157 0.244078 M3 1.99730718954249 1.907629 1.047 0.30013 0.150065 M4 2.17392156862745 1.998875 1.0876 0.281999 0.140999 M5 5.22392156862744 1.998875 2.6134 0.011811 0.005905 M6 3.46792156862745 1.998875 1.7349 0.088913 0.044456 M7 0.870000000000002 1.991566 0.4368 0.664107 0.332053 M8 1.77800000000000 1.991566 0.8928 0.37626 0.18813 M9 4.188 1.991566 2.1029 0.040532 0.020266 M10 4.08199999999999 1.991566 2.0496 0.045663 0.022831 M11 1.78000000000000 1.991566 0.8938 0.375728 0.187864 Multiple Linear Regression - Regression Statistics Multiple R 0.602856662143188 R-squared 0.363436155090425 Adjusted R-squared 0.210660832312128 F-TEST (value) 2.37889305995989 F-TEST (DF numerator) 12 F-TEST (DF denominator) 50 p-value 0.0162632840102594 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.14894272598015 Sum Squared Residuals 495.792014575166 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 105.29 98.210130718954 7.0798692810459 2 101.23 100.138464052288 1.09153594771242 3 102.33 100.803464052288 1.52653594771242 4 100.26 100.980078431373 -0.720078431372547 5 104.13 104.030078431373 0.0999215686274367 6 103.54 102.274078431373 1.26592156862745 7 100.02 99.6761568627451 0.343843137254901 8 98.66 100.584156862745 -1.9241568627451 9 108.64 102.994156862745 5.6458431372549 10 105.67 102.888156862745 2.78184313725490 11 102.66 100.586156862745 2.07384313725490 12 100.3 98.806156862745 1.49384313725490 13 95.13 98.2101307189543 -3.08013071895428 14 93.2 100.138464052288 -6.93846405228758 15 102.84 100.803464052288 2.03653594771242 16 101.36 100.980078431373 0.379921568627447 17 102.55 104.030078431373 -1.48007843137255 18 103.12 102.274078431373 0.845921568627452 19 96.3 99.6761568627451 -3.37615686274510 20 99.13 100.584156862745 -1.45415686274510 21 102.23 102.994156862745 -0.764156862745095 22 104.3 102.888156862745 1.4118431372549 23 99.58 100.586156862745 -1.0061568627451 24 98.45 98.806156862745 -0.356156862745098 25 96.23 98.2101307189543 -1.98013071895427 26 97.62 100.138464052288 -2.51846405228758 27 102.32 100.803464052288 1.51653594771241 28 105.23 100.980078431373 4.24992156862745 29 100.05 104.030078431373 -3.98007843137255 30 102.66 102.274078431373 0.385921568627444 31 100.98 99.6761568627451 1.30384313725490 32 99.2 100.584156862745 -1.38415686274510 33 98.36 102.994156862745 -4.6341568627451 34 102.56 102.888156862745 -0.328156862745096 35 97.33 100.586156862745 -3.2561568627451 36 96.22 98.806156862745 -2.5861568627451 37 99.22 98.2101307189543 1.00986928104572 38 102.32 100.138464052288 2.18153594771241 39 104.22 100.803464052288 3.41653594771242 40 100.06 100.980078431373 -0.92007843137255 41 107.23 104.030078431373 3.19992156862746 42 99.62 102.274078431373 -2.65407843137255 43 98.32 97.1357647058824 1.18423529411764 44 101.23 98.0437647058824 3.18623529411765 45 102.33 100.453764705882 1.87623529411765 46 100.6 100.347764705882 0.252235294117645 47 95.63 98.0457647058823 -2.41576470588235 48 94.63 96.2657647058823 -1.63576470588236 49 95.66 95.6697385620915 -0.00973856209153345 50 100.78 97.5980718954248 3.18192810457517 51 90.36 98.2630718954248 -7.90307189542483 52 95.45 98.4396862745098 -2.9896862745098 53 103.65 101.489686274510 2.16031372549021 54 99.89 99.7336862745098 0.156313725490196 55 97.68 97.1357647058824 0.544235294117656 56 99.62 98.0437647058824 1.57623529411765 57 98.33 100.453764705882 -2.12376470588235 58 96.23 100.347764705882 -4.11776470588235 59 102.65 98.0457647058823 4.60423529411766 60 99.35 96.2657647058823 3.08423529411764 61 92.65 95.6697385620915 -3.01973856209152 62 100.6 97.5980718954248 3.00192810457516 63 97.67 98.2630718954248 -0.593071895424833 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.965978782538898 0.0680424349222045 0.0340212174611023 17 0.931244270057462 0.137511459885077 0.0687557299425384 18 0.875807929424863 0.248384141150274 0.124192070575137 19 0.850533668034231 0.298932663931538 0.149466331965769 20 0.777266571822519 0.445466856354963 0.222733428177481 21 0.80043552422608 0.39912895154784 0.19956447577392 22 0.735725761139287 0.528548477721427 0.264274238860713 23 0.669663595360444 0.660672809279111 0.330336404639556 24 0.580841775475548 0.838316449048904 0.419158224524452 25 0.546787257171822 0.906425485656356 0.453212742828178 26 0.508726190575336 0.982547618849327 0.491273809424664 27 0.438452309356656 0.876904618713312 0.561547690643344 28 0.519883574000156 0.960232851999688 0.480116425999844 29 0.581830338434398 0.836339323131203 0.418169661565602 30 0.498803507657454 0.997607015314908 0.501196492342546 31 0.435302772645746 0.870605545291492 0.564697227354254 32 0.380843631763855 0.76168726352771 0.619156368236145 33 0.520672769961918 0.958654460076164 0.479327230038082 34 0.448216214859036 0.896432429718071 0.551783785140964 35 0.484684114606503 0.969368229213005 0.515315885393497 36 0.505192458342895 0.98961508331421 0.494807541657105 37 0.410383267136553 0.820766534273105 0.589616732863447 38 0.415467822687557 0.830935645375114 0.584532177312443 39 0.51579122452925 0.9684175509415 0.48420877547075 40 0.44619186801919 0.89238373603838 0.55380813198081 41 0.425070932389267 0.850141864778534 0.574929067610733 42 0.341456983828012 0.682913967656025 0.658543016171988 43 0.242650265376071 0.485300530752142 0.757349734623929 44 0.169869038316013 0.339738076632026 0.830130961683987 45 0.134960780446649 0.269921560893298 0.86503921955335 46 0.116815636931679 0.233631273863358 0.883184363068321 47 0.186316615577615 0.37263323115523 0.813683384422385 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 0.03125 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/10g1tf1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/10g1tf1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/14ewi1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/14ewi1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/2qs241258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/2qs241258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/346r11258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/346r11258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/4lozp1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/4lozp1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/544py1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/544py1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/61j2f1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/61j2f1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/7uu4o1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/7uu4o1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/8306e1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/8306e1258990028.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/96n0i1258990028.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/23/t12589901520rgtu8m74dc34in/96n0i1258990028.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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