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 05:10:13 -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/t1258719088shbo7iksepadd7b.htm/, Retrieved Fri, 20 Nov 2009 13:11:47 +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/t1258719088shbo7iksepadd7b.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:

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User-defined keywords:

Dataseries X:

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8.3 98.6 8.2 8.7 9.3 9.3 8.5 96.5 8.3 8.2 8.7 9.3 8.6 95.9 8.5 8.3 8.2 8.7 8.5 103.7 8.6 8.5 8.3 8.2 8.2 103.1 8.5 8.6 8.5 8.3 8.1 103.7 8.2 8.5 8.6 8.5 7.9 112.1 8.1 8.2 8.5 8.6 8.6 86.9 7.9 8.1 8.2 8.5 8.7 95 8.6 7.9 8.1 8.2 8.7 111.8 8.7 8.6 7.9 8.1 8.5 108.8 8.7 8.7 8.6 7.9 8.4 109.3 8.5 8.7 8.7 8.6 8.5 101.4 8.4 8.5 8.7 8.7 8.7 100.5 8.5 8.4 8.5 8.7 8.7 100.7 8.7 8.5 8.4 8.5 8.6 113.5 8.7 8.7 8.5 8.4 8.5 106.1 8.6 8.7 8.7 8.5 8.3 111.6 8.5 8.6 8.7 8.7 8 114.9 8.3 8.5 8.6 8.7 8.2 88.6 8 8.3 8.5 8.6 8.1 99.5 8.2 8 8.3 8.5 8.1 115.1 8.1 8.2 8 8.3 8 118 8.1 8.1 8.2 8 7.9 111.4 8 8.1 8.1 8.2 7.9 107.3 7.9 8 8.1 8.1 8 105.3 7.9 7.9 8 8.1 8 105.3 8 7.9 7.9 8 7.9 117.9 8 8 7.9 7.9 8 110.2 7.9 8 8 7.9 7.7 112.4 8 7.9 8 8 7.2 117.5 7.7 8 7.9 8 7.5 93 7.2 7.7 8 7.9 7.3 103.5 7.5 7.2 7.7 8 7 116.3 7.3 7.5 7.2 7.7 7 120 7 7.3 7.5 7.2 7 114.3 7 7 7.3 7.5 7.2 104.7 7 7 7 7.3 7.3 109.8 7.2 7 7 7 7.1 112.6 7.3 7.2 7 7 6.8 114.4 7.1 7.3 7.2 7 6.4 115.7 6.8 7.1 7.3 7.2 6.1 114.7 6.4 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] = + 2.42428661862928 -0.0117846612829053X[t] + 1.46617466855599Y1[t] -0.818010407458793Y2[t] -0.0404602516786089Y3[t] + 0.251899520029868Y4[t] + 0.0759909965765519M1[t] + 0.00922654169473366M2[t] -0.169015605044706M3[t] + 0.0500030192168811M4[t] -0.0214506584890375M5[t] -0.125440393745722M6[t] + 0.0393717349680023M7[t] + 0.336062911357132M8[t] -0.489194385002157M9[t] + 0.0466595620924516M10[t] + 0.149763779550361M11[t] -0.00298100774336668t + 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) 2.42428661862928 1.067172 2.2717 0.028852 0.014426 X -0.0117846612829053 0.004771 -2.4702 0.018106 0.009053 Y1 1.46617466855599 0.153913 9.526 0 0 Y2 -0.818010407458793 0.289833 -2.8224 0.007544 0.003772 Y3 -0.0404602516786089 0.288756 -0.1401 0.889306 0.444653 Y4 0.251899520029868 0.157692 1.5974 0.118457 0.059228 M1 0.0759909965765519 0.116485 0.6524 0.51809 0.259045 M2 0.00922654169473366 0.123248 0.0749 0.940718 0.470359 M3 -0.169015605044706 0.126806 -1.3329 0.190511 0.095255 M4 0.0500030192168811 0.11772 0.4248 0.673405 0.336702 M5 -0.0214506584890375 0.113407 -0.1891 0.850984 0.425492 M6 -0.125440393745722 0.107946 -1.1621 0.252457 0.126228 M7 0.0393717349680023 0.110079 0.3577 0.72257 0.361285 M8 0.336062911357132 0.148666 2.2605 0.0296 0.0148 M9 -0.489194385002157 0.156086 -3.1341 0.003316 0.001658 M10 0.0466595620924516 0.166212 0.2807 0.780445 0.390223 M11 0.149763779550361 0.14106 1.0617 0.295073 0.147536 t -0.00298100774336668 0.002959 -1.0076 0.320033 0.160016 Multiple Linear Regression - Regression Statistics Multiple R 0.97972581791468 R-squared 0.95986267828859 Adjusted R-squared 0.941906508049273 F-TEST (value) 53.455868678887 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.160319121295799 Sum Squared Residuals 0.976684384816172 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.3 8.20765593790238 0.0923440620976185 2 8.5 8.74255708556345 -0.242557085563449 3 8.6 8.64892903463709 -0.0489290346370851 4 8.5 8.6260658933297 -0.126065893329691 5 8.2 8.34738139871594 -0.147381398715937 6 8.1 7.92162237796333 0.178377622036665 7 7.9 8.11248397671017 -0.212483976710174 8 8.6 8.47868184022043 0.121318159779571 9 8.7 8.67338829836609 0.0266117016339067 10 8.7 8.5651912081317 0.134808791868295 11 8.5 8.54016528076808 -0.0401652807680848 12 8.4 8.26057686797475 0.139423132025247 13 8.5 8.46886024758204 0.0311397524179634 14 8.7 8.64623153804867 0.0537684619513331 15 8.7 8.62775146543648 0.0722485345635147 16 8.6 8.50010735887091 0.0998926411290883 17 8.5 8.3833596017268 0.116640398273208 18 8.3 8.19713669956702 0.102863300432985 19 8 8.11269057050633 -0.112690570506327 20 8.2 8.41894308498233 -0.218943084982334 21 8.1 7.98379212717758 0.116207872822419 22 8.1 7.98436297366575 0.115637026334248 23 8 8.04844980006107 -0.0484498000610658 24 7.9 7.88129223955275 0.0187077604472520 25 7.9 7.91261296153314 -0.0126129615331387 26 8 7.9522838873875 0.0477161126124956 27 8 7.89653427292517 0.103465727074829 28 7.9 7.85709416452992 0.0429058354700816 29 8 7.72273787893555 0.277262121064455 30 7.7 7.84344934071757 -0.143449340717567 31 7.2 7.42757127300029 -0.227571273000291 32 7.5 7.49308545386603 0.00691454613397222 33 7.3 7.42729383809563 -0.127293838095629 34 7 7.21534532690719 -0.215345326907191 35 7 6.85752713528143 0.142472864718572 36 7 7.10101994588258 -0.101019945882580 37 7.2 7.24892085452927 -0.0489208545292653 38 7.3 7.3367386970635 -0.0367386970635021 39 7.1 7.1055338763524 -0.00553387635240147 40 6.8 6.91723107776859 -0.117231077768592 41 6.4 6.5975598924146 -0.197559892414603 42 6.1 6.19458906785141 -0.0945890678514077 43 6.5 6.16192687598934 0.338073124010659 44 7.7 7.5050638191059 0.194936180894101 45 7.9 7.9155257363607 -0.0155257363606966 46 7.5 7.53510049129535 -0.0351004912953519 47 6.9 6.95385778388942 -0.0538577838894216 48 6.6 6.65711094658992 -0.0571109465899182 49 6.9 6.96194999845318 -0.061949998453178 50 7.7 7.52218879193688 0.177811208063122 51 8 8.12125135064886 -0.121251350648857 52 8 7.89950150550089 0.100498494499114 53 7.7 7.74896122820712 -0.0489612282071236 54 7.3 7.34320251390068 -0.0432025139006756 55 7.4 7.18532730379387 0.214672696206132 56 8.1 8.2042258018253 -0.104225801825310 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.724374317667148 0.551251364665705 0.275625682332853 22 0.597862304208288 0.804275391583425 0.402137695791712 23 0.462094661380365 0.92418932276073 0.537905338619635 24 0.323558929560569 0.647117859121138 0.676441070439431 25 0.242095008985189 0.484190017970377 0.757904991014811 26 0.147464648512949 0.294929297025899 0.85253535148705 27 0.0992976801490494 0.198595360298099 0.90070231985095 28 0.0593390775665934 0.118678155133187 0.940660922433407 29 0.398156282693252 0.796312565386504 0.601843717306748 30 0.406069258918802 0.812138517837604 0.593930741081198 31 0.646502891293402 0.706994217413196 0.353497108706598 32 0.582804787341909 0.834390425316182 0.417195212658091 33 0.496355725269014 0.992711450538028 0.503644274730986 34 0.432167775379326 0.864335550758652 0.567832224620674 35 0.444949414085393 0.889898828170786 0.555050585914607 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/10jamx1258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/10jamx1258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/18p2i1258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/18p2i1258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/2ih691258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/2ih691258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/3qd2a1258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/3qd2a1258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/4xj341258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/4xj341258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/5q9691258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/5q9691258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/6sfoj1258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/6sfoj1258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/7p3fg1258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/7p3fg1258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/8tc471258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/8tc471258719009.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/9fyj01258719009.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719088shbo7iksepadd7b/9fyj01258719009.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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