maand

*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, 19 Nov 2010 12:05:06 +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/Nov/19/t1290168240jpyrpbhf36rasls.htm/, Retrieved Fri, 19 Nov 2010 13:04:20 +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/Nov/19/t1290168240jpyrpbhf36rasls.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:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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6 101.82 107.34 93.63 99.85 101.76 6 101.68 107.34 93.63 99.91 102.37 6 101.68 107.34 93.63 99.87 102.38 6 102.45 107.34 96.13 99.86 102.86 6 102.45 107.34 96.13 100.10 102.87 6 102.45 107.34 96.13 100.10 102.92 6 102.45 107.34 96.13 100.12 102.95 6 102.45 107.34 96.13 99.95 103.02 6 102.45 112.60 96.13 99.94 104.08 6 102.52 112.60 96.13 100.18 104.16 6 102.52 112.60 96.13 100.31 104.24 6 102.85 112.60 96.13 100.65 104.33 7 102.85 112.61 96.13 100.65 104.73 7 102.85 112.61 96.13 100.69 104.86 7 103.25 112.61 96.13 101.26 105.03 7 103.25 112.61 98.73 101.26 105.62 7 103.25 112.61 98.73 101.38 105.63 7 103.25 112.61 98.73 101.38 105.63 7 104.45 112.61 98.73 101.38 105.94 7 104.45 112.61 98.73 101.44 106.61 7 104.45 118.65 98.73 101.40 107.69 7 104.80 118.65 98.73 101.40 107.78 7 104.80 118.65 98.73 100.58 107.93 7 105.29 118.65 98.73 100.58 108.48 8 105.29 114.29 98.73 100.58 108.14 8 105.29 114.29 98.73 100.59 108.48 8 105.29 114.29 98.73 100.81 108.48 8 106.04 114.29 101.67 100.75 1 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation And.dienstenrecr.&cultuur[t] = + 43.1196518887856 + 1.06911178353784maand[t] + 0.095509296468249Bioscoop[t] + 0.293827546929862Schouwburgabonnement[t] + 0.271011310340615Eendagsattracties[t] -0.140287440271954HuurvaneenDVD[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) 43.1196518887856 11.20716 3.8475 0.000328 0.000164 maand 1.06911178353784 0.142107 7.5233 0 0 Bioscoop 0.095509296468249 0.030258 3.1565 0.002656 0.001328 Schouwburgabonnement 0.293827546929862 0.024122 12.1808 0 0 Eendagsattracties 0.271011310340615 0.040701 6.6585 0 0 HuurvaneenDVD -0.140287440271954 0.134669 -1.0417 0.302359 0.15118 Multiple Linear Regression - Regression Statistics Multiple R 0.996983707496076 R-squared 0.99397651301262 Adjusted R-squared 0.993397331571527 F-TEST (value) 1716.17466045675 F-TEST (DF numerator) 5 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.452997679974222 Sum Squared Residuals 10.6707586992254 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.76 102.165616119899 -0.405616119898775 2 102.37 102.143827571977 0.226172428023504 3 102.38 102.149439069587 0.230560930412621 4 102.86 102.901912378122 -0.0419123781221807 5 102.87 102.868243392457 0.00175660754309176 6 102.92 102.868243392457 0.0517566075430885 7 102.95 102.865437643651 0.0845623563485303 8 103.02 102.889286508498 0.130713491502291 9 104.08 104.436222279751 -0.356222279751498 10 104.16 104.409238944839 -0.249238944839006 11 104.24 104.391001577604 -0.151001577603655 12 104.33 104.374821915746 -0.0448219157457087 13 104.73 105.446871974753 -0.716871974752847 14 104.86 105.441260477142 -0.581260477141975 15 105.03 105.399500354774 -0.369500354774259 16 105.62 106.104129761660 -0.484129761659856 17 105.63 106.087295268827 -0.457295268827233 18 105.63 106.087295268827 -0.457295268827233 19 105.94 106.201906424589 -0.261906424589129 20 106.61 106.193489178173 0.41651082182719 21 107.69 107.97381905924 -0.283819059240057 22 107.78 108.007247313004 -0.227247313003940 23 107.93 108.122283014027 -0.192283014026938 24 108.48 108.169082569296 0.310917430703617 25 108.14 107.95710624822 0.182893751779967 26 108.48 107.955703373817 0.524296626182691 27 108.48 107.924840136957 0.55515986304252 28 108.89 108.801662608126 0.0883373918736056 29 108.93 108.792111678480 0.137888321520438 30 109.21 108.762651316022 0.447348683977534 31 109.47 108.713550711927 0.756449288072724 32 109.8 108.699729017437 1.10027098256326 33 111.73 111.400283640225 0.329716359774716 34 111.85 111.430217631844 0.419782368155679 35 112.12 111.520951463489 0.599048536510853 36 112.15 111.541218076831 0.608781923168917 37 112.17 112.592092493134 -0.422092493133577 38 112.67 112.633549741964 0.0364502580356471 39 112.8 112.612506625924 0.187493374076436 40 113.44 114.311747541759 -0.871747541759218 41 113.53 114.306136044148 -0.776136044148335 42 114.53 114.349115227559 0.180884772440952 43 114.51 114.294403125853 0.215596874147016 44 115.05 114.651398233561 0.398601766439361 45 116.67 116.440010998665 0.229989001334733 46 117.07 116.545662722606 0.524337277394328 47 116.92 116.548468471411 0.371531528588895 48 117 116.569151795373 0.4308482046267 49 117.02 117.649724694487 -0.629724694487338 50 117.35 117.676019516060 -0.326019516060413 51 117.36 117.858631308768 -0.498631308768381 52 117.82 118.489531563584 -0.66953156358371 53 117.88 118.503291116439 -0.62329111643869 54 118.24 118.551105294761 -0.311105294761424 55 118.5 118.554418355094 -0.0544183550935037 56 118.8 118.568447099121 0.231552900879299 57 119.76 119.705559705739 0.0544402942607398 58 120.09 119.691530961712 0.398469038287933 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.00502072491588504 0.0100414498317701 0.994979275084115 10 0.00669688092433359 0.0133937618486672 0.993303119075666 11 0.00174015818935123 0.00348031637870245 0.998259841810649 12 0.00741959649460561 0.0148391929892112 0.992580403505394 13 0.00279602970144358 0.00559205940288716 0.997203970298556 14 0.00110194305715356 0.00220388611430711 0.998898056942847 15 0.000476665719152708 0.000953331438305415 0.999523334280847 16 0.00064992572400615 0.0012998514480123 0.999350074275994 17 0.000320809571281239 0.000641619142562478 0.999679190428719 18 0.000172828219287652 0.000345656438575304 0.999827171780712 19 0.000119458066808872 0.000238916133617744 0.99988054193319 20 0.00186151063345025 0.0037230212669005 0.99813848936655 21 0.00171554117483303 0.00343108234966607 0.998284458825167 22 0.00179872864038976 0.00359745728077952 0.99820127135961 23 0.00384167320135373 0.00768334640270746 0.996158326798646 24 0.00965278179992464 0.0193055635998493 0.990347218200075 25 0.00793658829058407 0.0158731765811681 0.992063411709416 26 0.0101081415990090 0.0202162831980180 0.989891858400991 27 0.0092300943078133 0.0184601886156266 0.990769905692187 28 0.0122062673198159 0.0244125346396317 0.987793732680184 29 0.0124905485051112 0.0249810970102224 0.987509451494889 30 0.0114283557611547 0.0228567115223094 0.988571644238845 31 0.0190947554300832 0.0381895108601665 0.980905244569917 32 0.0535432595351537 0.107086519070307 0.946456740464846 33 0.0592902142808539 0.118580428561708 0.940709785719146 34 0.0416617970909572 0.0833235941819145 0.958338202909043 35 0.0409780145515126 0.0819560291030251 0.959021985448487 36 0.0371994480109550 0.0743988960219099 0.962800551989045 37 0.146917170391747 0.293834340783493 0.853082829608253 38 0.133094861081878 0.266189722163757 0.866905138918121 39 0.107505251252865 0.215010502505729 0.892494748747135 40 0.392622786231283 0.785245572462565 0.607377213768717 41 0.769805673907981 0.460388652184037 0.230194326092019 42 0.692911364555103 0.614177270889794 0.307088635444897 43 0.60160505552487 0.796789888950261 0.398394944475131 44 0.845896298592991 0.308207402814018 0.154103701407009 45 0.969961559340197 0.0600768813196053 0.0300384406598027 46 0.95948545337447 0.081029093251062 0.040514546625531 47 0.933478182701915 0.133043634596170 0.0665218172980848 48 0.857549983752715 0.28490003249457 0.142450016247285 49 0.777445777511192 0.445108444977617 0.222554222488808 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.292682926829268 NOK 5% type I error level 23 0.560975609756098 NOK 10% type I error level 28 0.682926829268293 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/1033j51290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/1033j51290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/1ek5c1290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/1ek5c1290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/2pb4f1290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/2pb4f1290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/3pb4f1290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/3pb4f1290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/4pb4f1290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/4pb4f1290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/50k301290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/50k301290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/60k301290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/60k301290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/7auk31290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/7auk31290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/8auk31290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/8auk31290168296.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/933j51290168296.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290168240jpyrpbhf36rasls/933j51290168296.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; 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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