Multiple regression (MR)

*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: Tue, 14 Dec 2010 21:27:06 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y.htm/, Retrieved Tue, 14 Dec 2010 22:26:08 +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/2010/Dec/14/t1292361958306fov635e81a6y.htm/}, year = {2010}, } @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 = {2010}, 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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3.18 0.22 6.62 3.64 3.14 0.22 6.56 3.62 3.02 0.23 6.59 3.61 3.02 0.24 6.56 3.6 3.03 0.25 6.57 3.6 3.04 0.25 6.62 3.63 3.09 0.24 6.69 3.59 3.06 0.24 6.69 3.55 3.06 0.22 6.64 3.54 3.09 0.21 6.6 3.53 3.11 0.21 6.66 3.53 3.1 0.21 6.62 3.53 3.09 0.2 6.64 3.52 3.19 0.2 6.64 3.52 3.22 0.2 6.73 3.48 3.22 0.2 6.73 3.49 3.25 0.2 6.69 3.47 3.25 0.2 6.78 3.46 3.27 0.2 6.77 3.4 3.28 0.2 6.8 3.36 3.24 0.2 6.8 3.3 3.23 0.2 6.74 3.28 3.2 0.2 6.84 3.28 3.19 0.2 6.83 3.24 3.23 0.2 6.89 3.23 3.19 0.2 6.9 3.2 3.16 0.2 6.86 3.15 3.11 0.2 6.78 3.1 3.11 0.2 6.82 3.07 3.07 0.2 6.81 3.03 3.05 0.21 6.81 2.96 3 0.2 6.78 2.88 2.95 0.2 6.79 2.83 2.9 0.19 6.83 2.8 2.88 0.18 6.9 2.8 2.9 0.18 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 10 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Mayonaise[t] = + 2.47865060894444 -0.465444129700099Eieren[t] -0.0988998564557219Olijfolie[t] + 0.413491668082886Mosterd[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) 2.47865060894444 0.818087 3.0298 0.003806 0.001903 Eieren -0.465444129700099 1.326341 -0.3509 0.727064 0.363532 Olijfolie -0.0988998564557219 0.098111 -1.008 0.318104 0.159052 Mosterd 0.413491668082886 0.091171 4.5354 3.4e-05 1.7e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.831522318599948 R-squared 0.691429366329834 Adjusted R-squared 0.673627214387324 F-TEST (value) 38.8396508783175 F-TEST (DF numerator) 3 F-TEST (DF denominator) 52 p-value 2.58681964737661e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.112734946676432 Sum Squared Residuals 0.660876746511169 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.18 3.22664552249524 -0.0466455224952405 2 3.14 3.22430968052093 -0.08430968052093 3 3.02 3.21255332684943 -0.192553326849429 4 3.02 3.20673096456527 -0.186730964565271 5 3.03 3.20108752470371 -0.171087524703713 6 3.04 3.20854728192341 -0.168547281923413 7 3.09 3.1897390665452 -0.099739066545198 8 3.06 3.17319939982188 -0.113199399821882 9 3.06 3.18331835855784 -0.123318358557842 10 3.09 3.18779387743224 -0.0977938774322427 11 3.11 3.1818598860449 -0.0718598860448994 12 3.1 3.18581588030313 -0.085815880303128 13 3.09 3.18435740779019 -0.094357407790186 14 3.19 3.18435740779019 0.00564259220981405 15 3.22 3.15891675398586 0.0610832460141448 16 3.22 3.16305167066668 0.0569483293333158 17 3.25 3.15873783156326 0.0912621684367445 18 3.25 3.14570192780141 0.104298072198588 19 3.27 3.12188142628100 0.148118573719004 20 3.28 3.10237476386401 0.177625236135991 21 3.24 3.07756526377904 0.162434736220965 22 3.23 3.07522942180472 0.154770578195279 23 3.2 3.06533943615915 0.134660563840851 24 3.19 3.04978876800039 0.140211231999609 25 3.23 3.03971985993222 0.190280140067781 26 3.19 3.02632611132517 0.163673888674825 27 3.16 3.00960752217926 0.150392477820741 28 3.11 2.99684492729157 0.113155072708427 29 3.11 2.98048418299086 0.129515817009143 30 3.07 2.9649335148321 0.105066485167901 31 3.05 2.93133465676930 0.118665343230703 32 3 2.90587676031334 0.094123239686662 33 2.95 2.88421317834464 0.0657868216553636 34 2.9 2.87250687534092 0.0274931246590779 35 2.88 2.87023832668602 0.00976167331397747 36 2.9 2.87698239421532 0.0230176057846768 37 2.89 2.87273584843131 0.0172641515686910 38 2.89 2.86761193318592 0.0223880668140772 39 2.91 2.86976885273764 0.0402311472623627 40 2.9 2.86365593892769 0.0363440610723058 41 2.9 2.86761193318592 0.032388066814077 42 2.88 2.84484772354948 0.0351522764505207 43 2.83 2.83145397494244 -0.00145397494243526 44 2.8 2.80933816167698 -0.00933816167697668 45 2.77 2.79531325935058 -0.0253132593505756 46 2.78 2.78740127083412 -0.00740127083411803 47 2.75 2.78146727944677 -0.0314672794467746 48 2.74 2.79124563598916 -0.0512456359891613 49 2.73 2.78711071930833 -0.0571107193083328 50 2.69 2.79421263168283 -0.104212631682833 51 2.67 2.78711071930833 -0.117110719308333 52 2.66 2.79106671356656 -0.131066713566561 53 2.67 2.77463867594643 -0.104638675946431 54 2.65 2.77642050800132 -0.126420508001318 55 2.64 2.78523695911129 -0.14523695911129 56 2.63 2.79378010134711 -0.163780101347105 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.146002669927434 0.292005339854868 0.853997330072566 8 0.097144699141351 0.194289398282702 0.90285530085865 9 0.0741305099985305 0.148261019997061 0.92586949000147 10 0.0510292958698748 0.102058591739750 0.948970704130125 11 0.038082401495465 0.07616480299093 0.961917598504535 12 0.0468802807533737 0.0937605615067475 0.953119719246626 13 0.110268538791776 0.220537077583553 0.889731461208224 14 0.314506075558127 0.629012151116253 0.685493924441873 15 0.47329071588329 0.94658143176658 0.52670928411671 16 0.564994284914115 0.87001143017177 0.435005715085885 17 0.860726074682 0.278547850636001 0.139273925318000 18 0.894628035440256 0.210743929119487 0.105371964559743 19 0.926734001824748 0.146531996350503 0.0732659981752517 20 0.911928708075615 0.17614258384877 0.088071291924385 21 0.874523131408735 0.250953737182529 0.125476868591265 22 0.844550301916743 0.310899396166513 0.155449698083257 23 0.89589788953775 0.208204220924500 0.104102110462250 24 0.906878235103624 0.186243529792753 0.0931217648963763 25 0.879210853963584 0.241578292072832 0.120789146036416 26 0.879842459540326 0.240315080919347 0.120157540459674 27 0.857803492874558 0.284393014250884 0.142196507125442 28 0.86129523706117 0.277409525877661 0.138704762938831 29 0.850565794663001 0.298868410673998 0.149434205336999 30 0.904574523511161 0.190850952977677 0.0954254764888386 31 0.888559355350757 0.222881289298486 0.111440644649243 32 0.847936142528735 0.304127714942530 0.152063857471265 33 0.818102736782469 0.363794526435062 0.181897263217531 34 0.922076759693822 0.155846480612355 0.0779232403061775 35 0.994173242352802 0.0116535152943963 0.00582675764719816 36 0.996027473743152 0.0079450525136965 0.00397252625684825 37 0.998171139001843 0.00365772199631354 0.00182886099815677 38 0.99853801125671 0.0029239774865818 0.0014619887432909 39 0.998275892840308 0.00344821431938471 0.00172410715969235 40 0.998158748145203 0.0036825037095944 0.0018412518547972 41 0.999234127982965 0.00153174403406991 0.000765872017034953 42 0.998954642482063 0.00209071503587427 0.00104535751793714 43 0.998522776923168 0.00295444615366455 0.00147722307683228 44 0.998148007205653 0.00370398558869442 0.00185199279434721 45 0.99659898908974 0.00680202182052002 0.00340101091026001 46 0.99485843189506 0.0102831362098816 0.00514156810494078 47 0.992097026108705 0.0158059477825902 0.00790297389129508 48 0.990611365310828 0.0187772693783448 0.00938863468917238 49 0.99791908209403 0.00416183581193943 0.00208091790596972 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.255813953488372 NOK 5% type I error level 15 0.348837209302326 NOK 10% type I error level 17 0.395348837209302 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/10j1ll1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/10j1ll1292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/1v0o91292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/1v0o91292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/2nrou1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/2nrou1292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/3nrou1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/3nrou1292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/4nrou1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/4nrou1292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/5g15f1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/5g15f1292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/6g15f1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/6g15f1292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/79s401292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/79s401292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/89s401292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/89s401292362015.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/9j1ll1292362015.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/14/t1292361958306fov635e81a6y/9j1ll1292362015.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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