model 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: Sun, 28 Nov 2010 16:10:50 +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/28/t12909606340lxvm6xc2htor3x.htm/, Retrieved Sun, 28 Nov 2010 17:10:44 +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/28/t12909606340lxvm6xc2htor3x.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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53.470 62.653 65.566 56.493 62.027 59.600 53.470 62.653 65.566 56.493 42.542 59.600 53.470 62.653 65.566 42.018 42.542 59.600 53.470 62.653 44.038 42.018 42.542 59.600 53.470 44.988 44.038 42.018 42.542 59.600 43.309 44.988 44.038 42.018 42.542 26.843 43.309 44.988 44.038 42.018 69.770 26.843 43.309 44.988 44.038 64.886 69.770 26.843 43.309 44.988 79.354 64.886 69.770 26.843 43.309 63.025 79.354 64.886 69.770 26.843 54.003 63.025 79.354 64.886 69.770 55.926 54.003 63.025 79.354 64.886 45.629 55.926 54.003 63.025 79.354 40.361 45.629 55.926 54.003 63.025 43.039 40.361 45.629 55.926 54.003 44.570 43.039 40.361 45.629 55.926 43.269 44.570 43.039 40.361 45.629 25.563 43.269 44.570 43.039 40.361 68.707 25.563 43.269 44.570 43.039 60.223 68.707 25.563 43.269 44.570 74.283 60.223 68.707 25.563 43.269 61.232 74.283 60.223 68.707 25.563 61.531 61.232 74.283 60.223 68.707 65.305 61.531 61.232 74.283 60.223 51.699 65.305 61.531 61.232 74.283 44.599 51.699 65.305 61.531 61.232 35.221 44.599 51 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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Yt[t] = + 22.6312382261078 + 0.196506125470823Yt_1[t] + 0.424055921103169Yt_2[t] + 0.0585369260350396Yt_3[t] -0.0637881636311812Yt_4[t] -8.84011801809233M1[t] -1.02335139651173M2[t] -10.12932203986M3[t] -14.9652615598999M4[t] -7.75591787526218M5[t] + 1.77509448248112M6[t] -7.78890250187808M7[t] -23.7349262723345M8[t] + 20.4649326275848M9[t] + 15.9869643501035M10[t] + 9.80486419820637M11[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) 22.6312382261078 11.842034 1.9111 0.063177 0.031589 Yt_1 0.196506125470823 0.159535 1.2317 0.225237 0.112619 Yt_2 0.424055921103169 0.161949 2.6184 0.012415 0.006207 Yt_3 0.0585369260350396 0.16068 0.3643 0.717548 0.358774 Yt_4 -0.0637881636311812 0.156935 -0.4065 0.68657 0.343285 M1 -8.84011801809233 6.751331 -1.3094 0.197874 0.098937 M2 -1.02335139651173 6.634976 -0.1542 0.878199 0.4391 M3 -10.12932203986 8.147073 -1.2433 0.220992 0.110496 M4 -14.9652615598999 8.008265 -1.8687 0.068996 0.034498 M5 -7.75591787526218 7.925687 -0.9786 0.333669 0.166835 M6 1.77509448248112 8.999435 0.1972 0.844635 0.422317 M7 -7.78890250187808 7.283428 -1.0694 0.2913 0.14565 M8 -23.7349262723345 6.589109 -3.6021 0.000863 0.000431 M9 20.4649326275848 8.272967 2.4737 0.017715 0.008857 M10 15.9869643501035 9.127053 1.7516 0.087507 0.043754 M11 9.80486419820637 7.634489 1.2843 0.206431 0.103215 Multiple Linear Regression - Regression Statistics Multiple R 0.957491183981792 R-squared 0.916789367402854 Adjusted R-squared 0.885585380178925 F-TEST (value) 29.3805198939381 F-TEST (DF numerator) 15 F-TEST (DF denominator) 40 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.45950708969358 Sum Squared Residuals 795.488139321092 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 53.47 53.2568071471356 0.213192852864406 2 59.6 58.9178923477949 0.682107652205067 3 42.542 46.3731306559266 -3.83113065592662 4 42.018 40.4329227728457 1.5850772271543 5 44.038 41.2503494087788 2.78765059122119 6 44.988 49.5665545099503 -4.57855450995029 7 43.309 42.1032564513951 1.20574354860492 8 26.843 26.3818216096547 0.461178390345328 9 69.77 66.5607787452375 3.20922125476254 10 64.886 63.3768418646949 1.50915813530507 11 79.354 73.5816856238379 5.77231437616213 12 63.025 68.1119334565327 -5.08693345653268 13 54.003 59.174179135197 -5.17117913519705 14 55.926 59.4521119941359 -3.5261119941359 15 45.629 45.0194534932331 0.609546506766854 16 40.361 39.489026712747 0.871973287253035 17 43.039 41.984735629851 1.05426437014901 18 44.57 49.0826454331881 -4.5126454331881 19 43.269 41.3035752781967 1.96542472180330 20 25.563 26.2439245876426 -0.680924587642592 21 68.707 66.3315446081756 2.37545539182441 22 60.223 62.6494862496638 -2.42648624966381 23 74.283 72.1422303778552 2.14076962214485 24 61.232 65.1575022312387 -3.92550223123871 25 61.531 56.4663052081523 5.06469479184771 26 65.305 60.1716812952308 5.13331870476922 27 51.699 50.2732904874815 1.42570951251845 28 44.599 45.214077534964 -0.615077534963977 29 35.221 45.4603685641597 -10.2393685641597 30 55.066 49.1005594922283 5.96544050777172 31 45.335 39.9117197192491 5.4232802807509 32 28.702 30.3728212655533 -1.67082126555329 33 69.517 68.93757630796 0.579423692040007 34 69.24 63.5911844713535 5.64881552864646 35 71.525 74.309572472081 -2.7845724720811 36 77.74 68.2864344422276 9.45356555777243 37 62.107 59.0168411465388 3.09015885346122 38 65.45 66.5485612556061 -1.09856125560611 39 51.493 51.6882954165115 -0.195295416511469 40 43.067 44.2157876458496 -1.14878764584959 41 49.172 45.0437115322147 4.12828846778535 42 54.483 51.1710548870519 3.31194511294805 43 38.158 45.6366225944323 -7.47862259443227 44 27.898 29.6296443228439 -1.73164432284385 45 58.648 64.812100338627 -6.16410033862696 46 56 60.7314874142877 -4.73148741428772 47 62.381 67.5095115262259 -5.12851152622588 48 59.849 60.290129870001 -0.441129870001028 49 48.345 51.5418673629763 -3.19686736297629 50 55.376 56.5667531072323 -1.19075310723227 51 45.4 43.4088299468472 1.99117005315279 52 38.389 39.0821853335938 -0.693185333593776 53 44.098 41.8288348649959 2.26916513500415 54 48.29 48.4761856775814 -0.186185677581383 55 41.267 42.3828259567269 -1.11582595672685 56 31.238 27.6157882143056 3.6222117856944 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0236248044042237 0.0472496088084475 0.976375195595776 20 0.00649898661215612 0.0129979732243122 0.993501013387844 21 0.00199837857884460 0.00399675715768919 0.998001621421155 22 0.00514960480560866 0.0102992096112173 0.99485039519439 23 0.00442425693950561 0.00884851387901121 0.995575743060494 24 0.00294163776650927 0.00588327553301854 0.99705836223349 25 0.0365855396460566 0.0731710792921132 0.963414460353943 26 0.0319177720268938 0.0638355440537876 0.968082227973106 27 0.0173837932407248 0.0347675864814496 0.982616206759275 28 0.00910696782217191 0.0182139356443438 0.990893032177828 29 0.232681903613932 0.465363807227865 0.767318096386068 30 0.876088777715175 0.247822444569650 0.123911222284825 31 0.840174266084345 0.319651467831311 0.159825733915655 32 0.750073985517742 0.499852028964516 0.249926014482258 33 0.656944925860914 0.686110148278171 0.343055074139086 34 0.584951086974931 0.830097826050138 0.415048913025069 35 0.486254055189193 0.972508110378386 0.513745944810807 36 0.821611386435873 0.356777227128255 0.178388613564128 37 0.702961286068748 0.594077427862504 0.297038713931252 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 3 0.157894736842105 NOK 5% type I error level 8 0.421052631578947 NOK 10% type I error level 10 0.526315789473684 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/10cr6q1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/10cr6q1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/1nq9f1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/1nq9f1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/2gi9i1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/2gi9i1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/3gi9i1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/3gi9i1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/4gi9i1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/4gi9i1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/58rql1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/58rql1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/68rql1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/68rql1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/7107o1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/7107o1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/8107o1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/8107o1290960643.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/9cr6q1290960643.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/28/t12909606340lxvm6xc2htor3x/9cr6q1290960643.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; par4 = 12 ;

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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