WS7-3

*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 04:00:16 -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/t12587148985ab1jqgblrk880o.htm/, Retrieved Fri, 20 Nov 2009 12:01:50 +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/t12587148985ab1jqgblrk880o.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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9.5 101.6 9.2 9.2 10 10.9 9.6 94.6 9.5 9.2 9.2 10 9.5 95.9 9.6 9.5 9.2 9.2 9.1 104.7 9.5 9.6 9.5 9.2 8.9 102.8 9.1 9.5 9.6 9.5 9 98.1 8.9 9.1 9.5 9.6 10.1 113.9 9 8.9 9.1 9.5 10.3 80.9 10.1 9 8.9 9.1 10.2 95.7 10.3 10.1 9 8.9 9.6 113.2 10.2 10.3 10.1 9 9.2 105.9 9.6 10.2 10.3 10.1 9.3 108.8 9.2 9.6 10.2 10.3 9.4 102.3 9.3 9.2 9.6 10.2 9.4 99 9.4 9.3 9.2 9.6 9.2 100.7 9.4 9.4 9.3 9.2 9 115.5 9.2 9.4 9.4 9.3 9 100.7 9 9.2 9.4 9.4 9 109.9 9 9 9.2 9.4 9.8 114.6 9 9 9 9.2 10 85.4 9.8 9 9 9 9.8 100.5 10 9.8 9 9 9.3 114.8 9.8 10 9.8 9 9 116.5 9.3 9.8 10 9.8 9 112.9 9 9.3 9.8 10 9.1 102 9 9 9.3 9.8 9.1 106 9.1 9 9 9.3 9.1 105.3 9.1 9.1 9 9 9.2 118.8 9.1 9.1 9.1 9 8.8 106.1 9.2 9.1 9.1 9.1 8.3 109.3 8.8 9.2 9.1 9.1 8.4 117.2 8.3 8.8 9.2 9.1 8.1 92.5 8.4 8.3 8.8 9.2 7.7 104.2 8.1 8.4 8.3 8.8 7.9 112.5 7.7 8.1 8.4 8.3 7.9 122.4 7.9 7.7 8.1 8.4 8 113.3 7.9 7.9 7.7 8.1 7.9 100 8 7.9 7.9 7.7 7.6 110.7 7.9 8 7.9 7.9 7.1 112.8 7.6 7.9 8 7.9 6.8 109.8 7.1 7.6 7.9 8 6.5 117.3 6.8 7.1 7.6 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 2.22376675354193 -0.0079494985633593X[t] + 1.43718399412033Y1[t] -0.559931494666128Y2[t] -0.368554988553163Y3[t] + 0.368298781202975Y4[t] -0.276786896503478M1[t] -0.470758496794736M2[t] -0.348945862635600M3[t] -0.228263175107274M4[t] -0.364395505657626M5[t] -0.153384005778434M6[t] + 0.537200243224335M7[t] -0.634309077860441M8[t] -0.512368949548809M9[t] + 0.0643622420032612M10[t] -0.134758856878862M11[t] -0.00497891142074613t + 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) 2.22376675354193 0.985077 2.2575 0.029808 0.014904 X -0.0079494985633593 0.004484 -1.773 0.084248 0.042124 Y1 1.43718399412033 0.147512 9.7428 0 0 Y2 -0.559931494666128 0.27011 -2.073 0.045006 0.022503 Y3 -0.368554988553163 0.267209 -1.3793 0.175874 0.087937 Y4 0.368298781202975 0.14153 2.6023 0.013131 0.006565 M1 -0.276786896503478 0.13933 -1.9866 0.054217 0.027109 M2 -0.470758496794736 0.14209 -3.3131 0.002033 0.001017 M3 -0.348945862635600 0.13989 -2.4944 0.017081 0.008541 M4 -0.228263175107274 0.13444 -1.6979 0.097707 0.048853 M5 -0.364395505657626 0.130901 -2.7838 0.008327 0.004164 M6 -0.153384005778434 0.130524 -1.1751 0.247249 0.123624 M7 0.537200243224335 0.131918 4.0722 0.000228 0.000114 M8 -0.634309077860441 0.195992 -3.2364 0.002511 0.001255 M9 -0.512368949548809 0.188841 -2.7132 0.009958 0.004979 M10 0.0643622420032612 0.175997 0.3657 0.716617 0.358309 M11 -0.134758856878862 0.142327 -0.9468 0.34971 0.174855 t -0.00497891142074613 0.003513 -1.4173 0.164548 0.082274 Multiple Linear Regression - Regression Statistics Multiple R 0.985830567255625 R-squared 0.971861907335547 Adjusted R-squared 0.959273813248818 F-TEST (value) 77.2048493314117 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.184535380982358 Sum Squared Residuals 1.29402565970356 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.5 9.53396171613988 -0.0339617161398841 2 9.6 9.78518798036736 -0.185187980367362 3 9.5 9.5727872810232 -0.0727872810232039 4 9.1 9.30825742432862 -0.208257424328622 5 8.9 8.73700391695196 0.162996083048038 6 9 8.9906203246762 0.00937967532380436 7 10.1 9.91692040060337 0.183079599396632 8 10.3 10.4540663499839 -0.154066349983899 9 10.2 10.0143718877325 0.185628112267515 10 9.6 9.82272263537158 -0.222722635371578 11 9.2 9.20175437918828 -0.00175437918828520 12 9.3 9.18008133306011 0.119918666939888 13 9.4 9.5019813780878 -0.101981378087810 14 9.4 9.3434321882798 0.0565678117202089 15 9.2 9.20658360265735 -0.0065836026573519 16 9 8.91717238046813 0.0828276195318734 17 9 8.7550930954642 0.244906904535796 18 9 9.0736875937836 -0.073687593783602 19 9.8 9.72198152958787 0.0780184704121272 20 10 9.85370609418812 0.146293905811884 21 9.8 9.69012148586344 0.109878514136560 22 9.3 9.4539288479389 -0.153928847938904 23 9 8.85063701920313 0.149362980796870 24 9 9.00521646253753 -0.0052164625375295 25 9.1 9.08869737538975 0.0113026246102521 26 9.1 8.9280843748008 0.171915625199199 27 9.1 8.88399996270604 0.216000037293963 28 9.2 8.85553000935295 0.34446999064705 29 8.8 8.99592567666885 -0.195925676668844 30 8.3 8.5456531226098 -0.245653122609795 31 8.4 8.63698252349225 -0.236982523492247 32 8.1 8.26478292578836 -0.164782925788359 33 7.7 7.83854464358062 -0.138544643580618 34 7.9 7.71641704693096 0.183582953069038 35 7.9 8.0924227722276 -0.192422772227601 36 8 8.21948921673943 -0.219489216739433 37 7.9 7.9661396289281 -0.0661396289280972 38 7.6 7.5560776899501 0.0439223100499016 39 7.1 7.24419991808063 -0.144199918080629 40 6.8 6.90682501819357 -0.106825018193571 41 6.5 6.6286397045399 -0.128639704539894 42 6.9 6.71047029129731 0.189529708702688 43 8.2 8.01128919066093 0.188710809339073 44 8.7 8.6374394362613 0.062560563738707 45 8.3 8.45696198282346 -0.156961982823456 46 7.9 7.70693146975856 0.193068530241444 47 7.5 7.45518582938098 0.0448141706190159 48 7.8 7.69521298766293 0.104787012337074 49 8.3 8.10921990145446 0.190780098545540 50 8.4 8.48721776660195 -0.0872177666019477 51 8.2 8.19242923553278 0.00757076446722211 52 7.7 7.81221516765673 -0.112215167656729 53 7.2 7.2833376063751 -0.0833376063750966 54 7.3 7.1795686676331 0.120431332366905 55 8.1 8.31282635565558 -0.212826355655585 56 8.5 8.39000519377833 0.109994806221667 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.241882390412166 0.483764780824333 0.758117609587834 22 0.17554594220179 0.35109188440358 0.82445405779821 23 0.127396435491555 0.254792870983109 0.872603564508445 24 0.0962173068693836 0.192434613738767 0.903782693130616 25 0.0496126086543224 0.0992252173086449 0.950387391345678 26 0.024613896375093 0.049227792750186 0.975386103624907 27 0.0222856860722953 0.0445713721445907 0.977714313927705 28 0.367942932834228 0.735885865668456 0.632057067165772 29 0.419734473304647 0.839468946609295 0.580265526695352 30 0.394482249343766 0.788964498687532 0.605517750656234 31 0.819593464751866 0.360813070496268 0.180406535248134 32 0.766954234423433 0.466091531153134 0.233045765576567 33 0.74424653225016 0.51150693549968 0.25575346774984 34 0.746861183292919 0.506277633414162 0.253138816707081 35 0.758907988698053 0.482184022603894 0.241092011301947 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 2 0.133333333333333 NOK 10% type I error level 3 0.2 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/108c1d1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/108c1d1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/1ejvc1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/1ejvc1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/2vf671258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/2vf671258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/3tccj1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/3tccj1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/4edae1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/4edae1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/5l6yb1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/5l6yb1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/687bk1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/687bk1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/7ay951258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/7ay951258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/89ykk1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/89ykk1258714811.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/9aduq1258714811.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587148985ab1jqgblrk880o/9aduq1258714811.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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