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: Fri, 20 Nov 2009 06:49:22 -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/t125872503786d3utviag5nux4.htm/, Retrieved Fri, 20 Nov 2009 14:50:49 +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/t125872503786d3utviag5nux4.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:

» Textbox « » Textfile « » CSV «

2.09 0 2.11 2.05 0 2.09 2.08 0 2.05 2.06 0 2.08 2.06 0 2.06 2.08 0 2.06 2.07 0 2.08 2.06 0 2.07 2.07 0 2.06 2.06 0 2.07 2.09 0 2.06 2.07 0 2.09 2.09 0 2.07 2.28 0 2.09 2.33 0 2.28 2.35 0 2.33 2.52 0 2.35 2.63 0 2.52 2.58 0 2.63 2.70 0 2.58 2.81 0 2.70 2.97 0 2.81 3.04 0 2.97 3.28 0 3.04 3.33 0 3.28 3.50 0 3.33 3.56 0 3.50 3.57 0 3.56 3.69 0 3.57 3.82 0 3.69 3.79 0 3.82 3.96 0 3.79 4.06 0 3.96 4.05 0 4.06 4.03 0 4.05 3.94 0 4.03 4.02 0 3.94 3.88 0 4.02 4.02 0 3.88 4.03 0 4.02 4.09 0 4.03 3.99 0 4.09 4.01 0 3.99 4.01 0 4.01 4.19 0 4.01 4.30 0 4.19 4.27 0 4.30 3.82 1 4.27 3.15 1 3.82 2.49 1 3.15 1.81 1 2.49 1.26 1 1.81 1.06 1 1.26 0.84 1 1.06 0.78 1 0.84 0.70 1 0.78 0.36 1 0.70 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] = + 0.252734874848809 -0.80413602302362X[t] + 0.865561772779888Y1[t] + 0.00859069835627082t + 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) 0.252734874848809 0.045219 5.5891 1e-06 0 X -0.80413602302362 0.070863 -11.3478 0 0 Y1 0.865561772779888 0.018757 46.1468 0 0 t 0.00859069835627082 0.001465 5.8646 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.996265720835123 R-squared 0.992545386511127 Adjusted R-squared 0.992138771229916 F-TEST (value) 2440.99381497532 F-TEST (DF numerator) 3 F-TEST (DF denominator) 55 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.101479087661148 Sum Squared Residuals 0.566390287789648 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.09 2.08766091377065 0.00233908622935031 2 2.05 2.07894037667132 -0.0289403766713187 3 2.08 2.05290860411639 0.0270913958836053 4 2.06 2.08746615565606 -0.0274661556560608 5 2.06 2.07874561855673 -0.0187456185567337 6 2.08 2.08733631691300 -0.00733631691300454 7 2.07 2.11323825072487 -0.0432382507248734 8 2.06 2.11317333135334 -0.0531733313533449 9 2.07 2.11310841198182 -0.0431084119818172 10 2.06 2.13035472806589 -0.0703547280658865 11 2.09 2.13028980869436 -0.0402898086943589 12 2.07 2.16484736023403 -0.094847360234026 13 2.09 2.1561268231347 -0.0661268231346992 14 2.28 2.18202875694657 0.0979712430534322 15 2.33 2.35507619213102 -0.0250761921310170 16 2.35 2.40694497912628 -0.0569449791262825 17 2.52 2.43284691293815 0.0871530870618489 18 2.63 2.58858311266700 0.0414168873329971 19 2.58 2.69238560602906 -0.112385606029061 20 2.7 2.65769821574634 0.0423017842536624 21 2.81 2.77015632683620 0.0398436731638048 22 2.97 2.87395882019825 0.0960411798017466 23 3.04 3.02103940219931 0.0189605978006934 24 3.28 3.09021942465017 0.189780575349830 25 3.33 3.30654494847361 0.0234550515263869 26 3.5 3.35841373546888 0.141586264531121 27 3.56 3.51414993519773 0.0458500648022698 28 3.57 3.57467433992079 -0.00467433992079452 29 3.69 3.59192065600486 0.0980793439951361 30 3.82 3.70437876709472 0.115621232905279 31 3.79 3.82549249591238 -0.0354924959123775 32 3.96 3.80811634108525 0.151883658914748 33 4.06 3.9638525408141 0.096147459185896 34 4.05 4.05899941644836 -0.00899941644836286 35 4.03 4.05893449707683 -0.0289344970768345 36 3.94 4.05021395997751 -0.110213959977508 37 4.02 3.98090409878359 0.0390959012164106 38 3.88 4.05873973896225 -0.178739738962251 39 4.02 3.94615178912934 0.0738482108706624 40 4.03 4.07592113567479 -0.0459211356747917 41 4.09 4.09316745175886 -0.00316745175886247 42 3.99 4.15369185648193 -0.163691856481926 43 4.01 4.07572637756021 -0.0657263775602087 44 4.01 4.10162831137208 -0.0916283113720769 45 4.19 4.11021900972835 0.0797809902716529 46 4.3 4.274610827185 0.0253891728150011 47 4.27 4.37841332054706 -0.108413320547057 48 3.82 3.55690114269631 0.26309885730369 49 3.15 3.17598904330163 -0.0259890433016315 50 2.49 2.60465335389538 -0.114653353895377 51 1.81 2.04197328221692 -0.231973282216922 52 1.26 1.46198197508287 -0.201981975082869 53 1.06 0.994513698410202 0.0654863015897982 54 0.84 0.829992042210495 0.0100079577895047 55 0.78 0.64815915055519 0.131840849444809 56 0.7 0.604816142544668 0.0951838574553317 57 0.36 0.544161899078548 -0.184161899078548 58 0.35 0.258461594689657 0.0915384053103433 59 0.36 0.258396675318129 0.101603324681871 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.00845704609728774 0.0169140921945755 0.991542953902712 8 0.00132041742828532 0.00264083485657064 0.998679582571715 9 0.000191596592842584 0.000383193185685168 0.999808403407157 10 2.56453403692804e-05 5.12906807385607e-05 0.99997435465963 11 1.04763752144645e-05 2.09527504289289e-05 0.999989523624786 12 1.56333470366407e-06 3.12666940732814e-06 0.999998436665296 13 4.05246149306026e-07 8.10492298612051e-07 0.99999959475385 14 0.00887519845222265 0.0177503969044453 0.991124801547777 15 0.00416232499581136 0.00832464999162271 0.995837675004189 16 0.00223819733157385 0.0044763946631477 0.997761802668426 17 0.00411987210470433 0.00823974420940865 0.995880127895296 18 0.00186275479756203 0.00372550959512405 0.998137245202438 19 0.00790686211078896 0.0158137242215779 0.992093137889211 20 0.00555646416743298 0.0111129283348660 0.994443535832567 21 0.00325767959881975 0.00651535919763951 0.99674232040118 22 0.00237739588143147 0.00475479176286294 0.997622604118569 23 0.00142430612284783 0.00284861224569566 0.998575693877152 24 0.00277319321348477 0.00554638642696955 0.997226806786515 25 0.00256428856806164 0.00512857713612329 0.997435711431938 26 0.00159557600083858 0.00319115200167717 0.998404423999161 27 0.00114931597347746 0.00229863194695493 0.998850684026523 28 0.00129682730436169 0.00259365460872337 0.998703172695638 29 0.000670662874955207 0.00134132574991041 0.999329337125045 30 0.000381397835594182 0.000762795671188364 0.999618602164406 31 0.000670040546743614 0.00134008109348723 0.999329959453256 32 0.000701902023774788 0.00140380404754958 0.999298097976225 33 0.000515911770282785 0.00103182354056557 0.999484088229717 34 0.000556567315492352 0.00111313463098470 0.999443432684508 35 0.000599311140237335 0.00119862228047467 0.999400688859763 36 0.00156867146671554 0.00313734293343108 0.998431328533284 37 0.00109368264394095 0.0021873652878819 0.99890631735606 38 0.00541064860252408 0.0108212972050482 0.994589351397476 39 0.00501579929741831 0.0100315985948366 0.994984200702582 40 0.00356484984822628 0.00712969969645255 0.996435150151774 41 0.00269341655398726 0.00538683310797453 0.997306583446013 42 0.00412240975639714 0.00824481951279428 0.995877590243603 43 0.00239464251005516 0.00478928502011032 0.997605357489945 44 0.00147867284087031 0.00295734568174063 0.99852132715913 45 0.00155670064645438 0.00311340129290877 0.998443299353546 46 0.00120225756749505 0.00240451513499009 0.998797742432505 47 0.000721054708588349 0.00144210941717670 0.999278945291412 48 0.0144139611504366 0.0288279223008732 0.985586038849563 49 0.0457665677024691 0.0915331354049383 0.95423343229753 50 0.143222207450501 0.286444414901002 0.856777792549499 51 0.212644394908250 0.425288789816501 0.78735560509175 52 0.144795109041367 0.289590218082733 0.855204890958633 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 35 0.760869565217391 NOK 5% type I error level 42 0.91304347826087 NOK 10% type I error level 43 0.934782608695652 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/10gm4x1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/10gm4x1258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/15usw1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/15usw1258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/2d7mv1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/2d7mv1258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/3yqm01258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/3yqm01258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/4ih6b1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/4ih6b1258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/508441258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/508441258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/6ey8q1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/6ey8q1258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/74fnc1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/74fnc1258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/800721258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/800721258724958.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/9zvtk1258724958.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125872503786d3utviag5nux4/9zvtk1258724958.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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