Workshop 7

*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: Thu, 19 Nov 2009 09:33:52 -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/19/t12586485083emtcip43bfdwns.htm/, Retrieved Thu, 19 Nov 2009 17:35:20 +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/2009/Nov/19/t12586485083emtcip43bfdwns.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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2.11 0 2.09 0 2.05 0 2.08 0 2.06 0 2.06 0 2.08 0 2.07 0 2.06 0 2.07 0 2.06 0 2.09 0 2.07 0 2.09 0 2.28 0 2.33 0 2.35 0 2.52 0 2.63 0 2.58 0 2.70 0 2.81 0 2.97 0 3.04 0 3.28 0 3.33 0 3.50 0 3.56 0 3.57 0 3.69 0 3.82 0 3.79 0 3.96 0 4.06 0 4.05 0 4.03 0 3.94 0 4.02 0 3.88 0 4.02 0 4.03 0 4.09 0 3.99 0 4.01 0 4.01 0 4.19 0 4.30 0 4.27 0 3.82 0 3.15 1 2.49 1 1.81 1 1.26 1 1.06 1 0.84 1 0.78 1 0.70 1 0.36 1 0.35 1 0.36 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] = + 3.11142857142857 -1.91506493506493X[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) 3.11142857142857 0.123665 25.1602 0 0 X -1.91506493506493 0.288818 -6.6307 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.656645782998115 R-squared 0.431183684329208 Adjusted R-squared 0.421376506472815 F-TEST (value) 43.9661328307749 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.21933612096115e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.865651583823886 Sum Squared Residuals 43.4624545454545 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.11 3.11142857142857 -1.00142857142857 2 2.09 3.11142857142857 -1.02142857142857 3 2.05 3.11142857142857 -1.06142857142857 4 2.08 3.11142857142857 -1.03142857142857 5 2.06 3.11142857142857 -1.05142857142857 6 2.06 3.11142857142857 -1.05142857142857 7 2.08 3.11142857142857 -1.03142857142857 8 2.07 3.11142857142857 -1.04142857142857 9 2.06 3.11142857142857 -1.05142857142857 10 2.07 3.11142857142857 -1.04142857142857 11 2.06 3.11142857142857 -1.05142857142857 12 2.09 3.11142857142857 -1.02142857142857 13 2.07 3.11142857142857 -1.04142857142857 14 2.09 3.11142857142857 -1.02142857142857 15 2.28 3.11142857142857 -0.831428571428572 16 2.33 3.11142857142857 -0.781428571428571 17 2.35 3.11142857142857 -0.761428571428571 18 2.52 3.11142857142857 -0.591428571428571 19 2.63 3.11142857142857 -0.481428571428572 20 2.58 3.11142857142857 -0.531428571428571 21 2.7 3.11142857142857 -0.411428571428571 22 2.81 3.11142857142857 -0.301428571428571 23 2.97 3.11142857142857 -0.141428571428571 24 3.04 3.11142857142857 -0.0714285714285714 25 3.28 3.11142857142857 0.168571428571428 26 3.33 3.11142857142857 0.218571428571429 27 3.5 3.11142857142857 0.388571428571429 28 3.56 3.11142857142857 0.448571428571429 29 3.57 3.11142857142857 0.458571428571428 30 3.69 3.11142857142857 0.578571428571429 31 3.82 3.11142857142857 0.708571428571428 32 3.79 3.11142857142857 0.678571428571429 33 3.96 3.11142857142857 0.848571428571429 34 4.06 3.11142857142857 0.948571428571428 35 4.05 3.11142857142857 0.938571428571428 36 4.03 3.11142857142857 0.91857142857143 37 3.94 3.11142857142857 0.828571428571429 38 4.02 3.11142857142857 0.908571428571428 39 3.88 3.11142857142857 0.768571428571428 40 4.02 3.11142857142857 0.908571428571428 41 4.03 3.11142857142857 0.91857142857143 42 4.09 3.11142857142857 0.978571428571428 43 3.99 3.11142857142857 0.87857142857143 44 4.01 3.11142857142857 0.898571428571428 45 4.01 3.11142857142857 0.898571428571428 46 4.19 3.11142857142857 1.07857142857143 47 4.3 3.11142857142857 1.18857142857143 48 4.27 3.11142857142857 1.15857142857143 49 3.82 3.11142857142857 0.708571428571428 50 3.15 1.19636363636364 1.95363636363636 51 2.49 1.19636363636364 1.29363636363636 52 1.81 1.19636363636364 0.613636363636364 53 1.26 1.19636363636364 0.0636363636363636 54 1.06 1.19636363636364 -0.136363636363636 55 0.84 1.19636363636364 -0.356363636363637 56 0.78 1.19636363636364 -0.416363636363636 57 0.7 1.19636363636364 -0.496363636363637 58 0.36 1.19636363636364 -0.836363636363637 59 0.35 1.19636363636364 -0.846363636363636 60 0.36 1.19636363636364 -0.836363636363637 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 4.92583207289033e-05 9.85166414578066e-05 0.999950741679271 6 1.74754472862918e-06 3.49508945725836e-06 0.999998252455271 7 5.0105776046226e-08 1.00211552092452e-07 0.999999949894224 8 1.40386465382395e-09 2.80772930764790e-09 0.999999998596135 9 4.86784975658016e-11 9.73569951316031e-11 0.999999999951321 10 1.36409523544758e-12 2.72819047089517e-12 0.999999999998636 11 4.89555080371057e-14 9.79110160742114e-14 0.999999999999951 12 2.36464312045412e-15 4.72928624090825e-15 0.999999999999998 13 8.04500219998019e-17 1.60900043999604e-16 1 14 4.64620433407642e-18 9.29240866815284e-18 1 15 1.79591377650468e-12 3.59182755300937e-12 0.999999999998204 16 7.02073602644622e-11 1.40414720528924e-10 0.999999999929793 17 4.52415508815239e-10 9.04831017630479e-10 0.999999999547585 18 2.27204815422638e-08 4.54409630845276e-08 0.999999977279519 19 6.26834000981579e-07 1.25366800196316e-06 0.999999373166 20 2.87666122074002e-06 5.75332244148004e-06 0.99999712333878 21 2.06819084352559e-05 4.13638168705118e-05 0.999979318091565 22 0.000159119039839074 0.000318238079678149 0.99984088096016 23 0.00133411334809071 0.00266822669618141 0.99866588665191 24 0.00671486398023413 0.0134297279604683 0.993285136019766 25 0.0339940255371765 0.0679880510743529 0.966005974462824 26 0.0934139401488708 0.186827880297742 0.90658605985113 27 0.204376644876570 0.408753289753139 0.79562335512343 28 0.330512624073092 0.661025248146183 0.669487375926908 29 0.438591629083093 0.877183258166186 0.561408370916907 30 0.54104403872371 0.91791192255258 0.45895596127629 31 0.632210094355862 0.735579811288276 0.367789905644138 32 0.685411289436822 0.629177421126356 0.314588710563178 33 0.737571041957803 0.524857916084394 0.262428958042197 34 0.778658702898026 0.442682594203949 0.221341297101974 35 0.798962531130838 0.402074937738323 0.201037468869161 36 0.804712999579105 0.390574000841790 0.195287000420895 37 0.795214307541464 0.409571384917071 0.204785692458536 38 0.784054562142551 0.431890875714897 0.215945437857449 39 0.75883688283009 0.48232623433982 0.24116311716991 40 0.734124665089604 0.531750669820793 0.265875334910396 41 0.702858261809966 0.594283476380068 0.297141738190034 42 0.668139425560929 0.663721148878143 0.331860574439071 43 0.620333600903233 0.759332798193533 0.379666399096767 44 0.566904227371427 0.866191545257147 0.433095772628573 45 0.507837716443704 0.984324567112591 0.492162283556296 46 0.453108446907733 0.906216893815467 0.546891553092266 47 0.403527410647877 0.807054821295754 0.596472589352123 48 0.352327763264358 0.704655526528717 0.647672236735642 49 0.273222751017958 0.546445502035917 0.726777248982042 50 0.649056657089955 0.70188668582009 0.350943342910045 51 0.911643779671042 0.176712440657916 0.088356220328958 52 0.977926625721444 0.0441467485571113 0.0220733742785557 53 0.984141685423091 0.0317166291538175 0.0158583145769087 54 0.98436710186168 0.0312657962766398 0.0156328981383199 55 0.969840039468292 0.0603199210634169 0.0301599605317084 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.372549019607843 NOK 5% type I error level 23 0.450980392156863 NOK 10% type I error level 25 0.490196078431373 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/10kcb71258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/10kcb71258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/1pqh41258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/1pqh41258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/228zd1258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/228zd1258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/39gxd1258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/39gxd1258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/4ui2i1258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/4ui2i1258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/5uy701258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/5uy701258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/698aj1258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/698aj1258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/77j591258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/77j591258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/8dza11258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/8dza11258648428.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/9vdpo1258648428.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t12586485083emtcip43bfdwns/9vdpo1258648428.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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