WS7(2)

*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 12:01:46 -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/t1258744030tvtrkdmau1nwkad.htm/, Retrieved Fri, 20 Nov 2009 20:07:22 +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/t1258744030tvtrkdmau1nwkad.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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8.1 10.9 7.7 10 7.5 9.2 7.6 9.2 7.8 9.5 7.8 9.6 7.8 9.5 7.5 9.1 7.5 8.9 7.1 9 7.5 10.1 7.5 10.3 7.6 10.2 7.7 9.6 7.7 9.2 7.9 9.3 8.1 9.4 8.2 9.4 8.2 9.2 8.2 9 7.9 9 7.3 9 6.9 9.8 6.6 10 6.7 9.8 6.9 9.3 7 9 7.1 9 7.2 9.1 7.1 9.1 6.9 9.1 7 9.2 6.8 8.8 6.4 8.3 6.7 8.4 6.6 8.1 6.4 7.7 6.3 7.9 6.2 7.9 6.5 8 6.8 7.9 6.8 7.6 6.4 7.1 6.1 6.8 5.8 6.5 6.1 6.9 7.2 8.2 7.3 8.7 6.9 8.3 6.1 7.9 5.8 7.5 6.2 7.8 7.1 8.3 7.7 8.4 7.9 8.2 7.7 7.7 7.4 7.2 7.5 7.3 8 8.1 8.1 8.5

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 Werkl_Vrouwen[t] = + 2.77768360598676 + 0.878437173686043Werkl_Mannen[t] + 0.330274973894885M1[t] + 0.0659624086320924M2[t] -0.226193873999304M3[t] -0.319450052210233M4[t] -0.438118691263488M5[t] -0.563531152105813M6[t] -0.69325617821093M7[t] -0.830274973894884M8[t] -0.917018795683954M9[t] -0.721331360946745M10[t] -0.235137486947442M11[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) 2.77768360598676 1.197584 2.3194 0.024768 0.012384 Werkl_Mannen 0.878437173686043 0.159008 5.5245 1e-06 1e-06 M1 0.330274973894885 0.482294 0.6848 0.496833 0.248417 M2 0.0659624086320924 0.484177 0.1362 0.892217 0.446108 M3 -0.226193873999304 0.485898 -0.4655 0.643711 0.321855 M4 -0.319450052210233 0.482797 -0.6617 0.511417 0.255708 M5 -0.438118691263488 0.482975 -0.9071 0.368968 0.184484 M6 -0.563531152105813 0.48448 -1.1632 0.250634 0.125317 M7 -0.69325617821093 0.483394 -1.4341 0.158151 0.079076 M8 -0.830274973894884 0.482294 -1.7215 0.091735 0.045867 M9 -0.917018795683954 0.48264 -1.9 0.063577 0.031789 M10 -0.721331360946745 0.485148 -1.4868 0.143738 0.071869 M11 -0.235137486947442 0.482168 -0.4877 0.628053 0.314026 Multiple Linear Regression - Regression Statistics Multiple R 0.698814048315905 R-squared 0.488341074123664 Adjusted R-squared 0.357704752623323 F-TEST (value) 3.73817226721581 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.000545406954329142 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.762308273255654 Sum Squared Residuals 27.3123534632788 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.9 10.2232996867386 0.676700313261408 2 10 9.60761225200139 0.392387747998611 3 9.2 9.13976853463279 0.0602314653672115 4 9.2 9.13435607379046 0.0656439262095371 5 9.5 9.19137486947442 0.308625130525582 6 9.6 9.0659624086321 0.534037591367908 7 9.5 8.93623738252697 0.563762617473026 8 9.1 8.5356874347372 0.564312565262791 9 8.9 8.44894361294814 0.451056387051862 10 9 8.29325617821093 0.706743821789071 11 10.1 9.13082492168465 0.96917507831535 12 10.3 9.3659624086321 0.934037591367908 13 10.2 9.78408109989558 0.415918900104417 14 9.6 9.6076122520014 -0.00761225200139373 15 9.2 9.31545596937 -0.115455969369997 16 9.3 9.39788722589628 -0.0978872258962753 17 9.4 9.45490602158023 -0.0549060215802293 18 9.4 9.4173372781065 -0.0173372781065079 19 9.2 9.28761225200139 -0.0876122520013917 20 9 9.15059345631744 -0.150593456317437 21 9 8.80031848242256 0.199681517577444 22 9 8.46894361294814 0.531056387051862 23 9.8 8.60376261747302 1.19623738252698 24 10 8.57536895231465 1.42463104768535 25 9.8 8.99348764357814 0.806512356421857 26 9.3 8.90486251305256 0.395137486947441 27 9 8.70054994778977 0.299450052210234 28 9 8.69513748694744 0.304862513052559 29 9.1 8.6643125652628 0.435687434737208 30 9.1 8.45105638705186 0.648943612948138 31 9.1 8.14564392620954 0.954356073790463 32 9.2 8.09646884789419 1.10353115210581 33 8.8 7.8340375913679 0.965962408632092 34 8.3 7.6783501566307 0.6216498433693 35 8.4 8.42807518273582 -0.0280751827358157 36 8.1 8.57536895231465 -0.475368952314654 37 7.7 8.72995649147233 -1.02995649147233 38 7.9 8.37780020884093 -0.477800208840933 39 7.9 7.99780020884093 -0.0978002088409323 40 8 8.16807518273582 -0.168075182735816 41 7.9 8.31293769578837 -0.412937695788374 42 7.6 8.18752523494605 -0.58752523494605 43 7.1 7.70642533936652 -0.606425339366516 44 6.8 7.30587539157675 -0.505875391576749 45 6.5 6.95560041768187 -0.455600417681866 46 6.9 7.41481900452489 -0.514819004524887 47 8.2 8.86729376957884 -0.667293769578838 48 8.7 9.19027497389488 -0.490274973894884 49 8.3 9.16917507831535 -0.869175078315351 50 7.9 8.20211277410372 -0.302112774103725 51 7.5 7.64642533936652 -0.146425339366515 52 7.8 7.90454403063 -0.104544030630004 53 8.3 8.57646884789419 -0.276468847894187 54 8.4 8.97811869126349 -0.578118691263488 55 8.2 9.02408109989558 -0.82408109989558 56 7.7 8.71137486947442 -1.01137486947442 57 7.2 8.36109989557953 -1.16109989557953 58 7.3 8.64463104768535 -1.34463104768535 59 8.1 9.57004350852767 -1.47004350852767 60 8.5 9.89302471284372 -1.39302471284372 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0242321571037121 0.0484643142074242 0.975767842896288 17 0.010456623607015 0.02091324721403 0.989543376392985 18 0.00572176808226737 0.0114435361645347 0.994278231917733 19 0.00294811009585729 0.00589622019171457 0.997051889904143 20 0.000880051349548117 0.00176010269909623 0.999119948650452 21 0.00024199581140274 0.00048399162280548 0.999758004188597 22 7.07054821847148e-05 0.000141410964369430 0.999929294517815 23 3.76748107288849e-05 7.53496214577698e-05 0.999962325189271 24 2.45846793086990e-05 4.91693586173979e-05 0.99997541532069 25 4.45039281119642e-05 8.90078562239284e-05 0.999955496071888 26 2.08899626572351e-05 4.17799253144702e-05 0.999979110037343 27 7.28157948620034e-06 1.45631589724007e-05 0.999992718420514 28 2.41898386373480e-06 4.83796772746961e-06 0.999997581016136 29 8.90327494515459e-07 1.78065498903092e-06 0.999999109672505 30 4.51573473905689e-07 9.03146947811378e-07 0.999999548426526 31 8.02995025736654e-07 1.60599005147331e-06 0.999999197004974 32 3.33353053533942e-05 6.66706107067884e-05 0.999966664694647 33 0.00223786739732554 0.00447573479465108 0.997762132602674 34 0.144742976989470 0.289485953978940 0.85525702301053 35 0.872782205280755 0.254435589438490 0.127217794719245 36 0.98148171378611 0.0370365724277798 0.0185182862138899 37 0.99618791670814 0.00762416658371981 0.00381208329185991 38 0.9929087265525 0.0141825468950006 0.0070912734475003 39 0.98525184865727 0.0294963026854587 0.0147481513427294 40 0.966964287091856 0.0660714258162882 0.0330357129081441 41 0.944364421352509 0.111271157294982 0.0556355786474911 42 0.930000778327505 0.139998443344990 0.0699992216724952 43 0.932009389511683 0.135981220976633 0.0679906104883167 44 0.908066603775598 0.183866792448804 0.0919333962244018 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.551724137931034 NOK 5% type I error level 22 0.758620689655172 NOK 10% type I error level 23 0.793103448275862 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/10k13k1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/10k13k1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/15vdw1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/15vdw1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/23d6m1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/23d6m1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/3b75g1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/3b75g1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/45c171258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/45c171258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/5nbcy1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/5nbcy1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/6aw151258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/6aw151258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/753jy1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/753jy1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/8og2b1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/8og2b1258743702.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/9fj8s1258743702.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744030tvtrkdmau1nwkad/9fj8s1258743702.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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