*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: Wed, 18 Nov 2009 09:41:26 -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/18/t12585626458e5kr8lnewskxhl.htm/, Retrieved Wed, 18 Nov 2009 17:44:17 +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/18/t12585626458e5kr8lnewskxhl.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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337302 488180 317756 318672 326225 327532 338653 344744 349420 520564 337302 317756 318672 326225 327532 338653 336923 501492 349420 337302 317756 318672 326225 327532 330758 485025 336923 349420 337302 317756 318672 326225 321002 464196 330758 336923 349420 337302 317756 318672 320820 460170 321002 330758 336923 349420 337302 317756 327032 467037 320820 321002 330758 336923 349420 337302 324047 460070 327032 320820 321002 330758 336923 349420 316735 447988 324047 327032 320820 321002 330758 336923 315710 442867 316735 324047 327032 320820 321002 330758 313427 436087 315710 316735 324047 327032 320820 321002 310527 431328 313427 315710 316735 324047 327032 320820 330962 484015 310527 313427 315710 316735 324047 327032 339015 509673 330962 310527 313427 315710 316735 324047 341332 512927 339015 330962 310527 313427 315710 316735 339092 502831 341332 339015 330962 310527 313427 315710 323308 470984 339092 341332 339015 330962 310527 313427 325849 471067 323308 339092 341332 339015 330962 310527 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] = + 46071.1845929129 + 0.626558835163595X[t] + 0.336631429022401`yt-1`[t] -0.0518462980042751`yt-2`[t] + 0.0698520891405935`yt-3`[t] -0.0690390803881129`yt-4`[t] -0.0228142693555400`yt-5`[t] -0.243418679323981`yt-6`[t] -16257.540984572M1[t] -29962.1038916211M2[t] -30104.4363888772M3[t] -26010.3620742694M4[t] -21521.0979193019M5[t] -14857.4290050065M6[t] -9613.76497647236M7[t] -6977.30610908971M8[t] -5770.32808988139M9[t] -3321.17746051666M10[t] -1454.50789014262M11[t] -481.174713519072t + 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) 46071.1845929129 13809.716823 3.3361 0.001688 0.000844 X 0.626558835163595 0.067925 9.2243 0 0 `yt-1` 0.336631429022401 0.113768 2.9589 0.004864 0.002432 `yt-2` -0.0518462980042751 0.124676 -0.4158 0.679455 0.339728 `yt-3` 0.0698520891405935 0.124737 0.56 0.578199 0.2891 `yt-4` -0.0690390803881129 0.123304 -0.5599 0.578259 0.289129 `yt-5` -0.0228142693555400 0.122964 -0.1855 0.853625 0.426812 `yt-6` -0.243418679323981 0.087382 -2.7857 0.007733 0.003866 M1 -16257.540984572 3917.093507 -4.1504 0.000142 7.1e-05 M2 -29962.1038916211 4202.85066 -7.129 0 0 M3 -30104.4363888772 4208.413418 -7.1534 0 0 M4 -26010.3620742694 3692.10032 -7.0449 0 0 M5 -21521.0979193019 2663.7328 -8.0793 0 0 M6 -14857.4290050065 2857.27529 -5.1999 4e-06 2e-06 M7 -9613.76497647236 2292.691171 -4.1932 0.000124 6.2e-05 M8 -6977.30610908971 2142.876229 -3.256 0.002124 0.001062 M9 -5770.32808988139 2018.424177 -2.8588 0.00637 0.003185 M10 -3321.17746051666 2022.570357 -1.6421 0.107398 0.053699 M11 -1454.50789014262 1657.246921 -0.8777 0.384687 0.192343 t -481.174713519072 84.364183 -5.7035 1e-06 0 Multiple Linear Regression - Regression Statistics Multiple R 0.996201542618571 R-squared 0.992417513515621 Adjusted R-squared 0.989285616924248 F-TEST (value) 316.874291523239 F-TEST (DF numerator) 19 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2531.77697481877 Sum Squared Residuals 294855153.910234 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 337302 334182.393685091 3119.60631490889 2 349420 348213.448417159 1206.55158284101 3 336923 341900.468061574 -4977.46806157396 4 330758 332279.700038611 -1521.70003861141 5 321002 323166.254835277 -2164.25483527702 6 320820 321929.166038643 -1109.16603864280 7 327032 326836.598873915 195.401126084623 8 324047 323806.749403543 240.250596456604 9 316735 319479.040238083 -2744.04023808320 10 315710 318101.459009824 -2391.45900982406 11 313427 317014.503459581 -3587.50345958097 12 310527 313888.55918022 -3361.55918022006 13 330962 328292.681907067 2669.31809293342 14 339015 338017.323828611 997.676171389256 15 341332 342842.360036983 -1510.36003698263 16 339092 342421.208319436 -3329.20831943553 17 323308 325374.687756089 -2066.68775608868 18 325849 326257.511723956 -408.511723956002 19 330675 330340.823332562 334.176667438436 20 332225 330123.189240064 2101.81075993640 21 331735 329264.626805992 2470.37319400763 22 328047 326297.505130347 1749.49486965282 23 326165 325936.595958109 228.404041890979 24 327081 326162.887375317 918.112624682797 25 346764 346754.773227326 9.22677267383943 26 344190 344133.128830415 56.8711695849656 27 343333 340630.890941356 2702.10905864447 28 345777 343590.47990966 2186.52009034034 29 344094 341508.430554620 2585.56944537954 30 348609 348322.530233225 286.469766775032 31 354846 354529.1133241 316.886675900142 32 356427 357559.466306435 -1132.46630643475 33 353467 354645.014911777 -1178.01491177684 34 355996 354597.023574505 1398.97642549531 35 352487 351792.189410360 694.810589640337 36 355178 353726.703428013 1451.29657198703 37 374556 375969.728003829 -1413.72800382922 38 375021 373941.263173298 1079.73682670212 39 375787 372483.729808698 3303.27019130241 40 372720 369113.110518116 3606.88948188375 41 364431 360183.195877919 4247.80412208104 42 370490 367322.737678734 3167.26232126571 43 376974 374282.30863082 2691.69136918042 44 377632 376886.579680783 745.420319217177 45 378205 374916.868583927 3288.13141607309 46 370861 370662.490357632 198.509642368109 47 369167 366677.396483769 2489.60351623071 48 371551 369629.977804921 1921.02219507947 49 382842 383469.928717746 -627.928717746472 50 381903 383974.434894233 -2071.43489423252 51 384502 383920.116739475 581.883260525253 52 392058 390099.395683978 1958.60431602236 53 384359 385279.304395970 -920.304395970423 54 388884 388693.171047198 190.828952802351 55 386586 390124.155838604 -3538.15583860362 56 387495 389450.015369175 -1955.01536917543 57 385705 387541.449460221 -1836.44946022068 58 378670 379625.521927692 -955.521927692172 59 377367 377192.314688181 174.685311818939 60 376911 377839.872211529 -928.872211529229 61 389827 393583.494458940 -3756.49445894046 62 387820 389089.400856285 -1269.40085628483 63 387267 387366.434411916 -99.4344119155428 64 380575 383476.105530200 -2901.10553019951 65 372402 374084.126580124 -1682.12658012445 66 376740 378866.883278244 -2126.88327824429 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 23 0.00586519273735834 0.0117303854747167 0.994134807262642 24 0.00143593005455180 0.00287186010910360 0.998564069945448 25 0.807930899391102 0.384138201217797 0.192069100608898 26 0.942260542818733 0.115478914362535 0.0577394571812674 27 0.927329161350543 0.145341677298913 0.0726708386494567 28 0.879096468235683 0.241807063528635 0.120903531764317 29 0.81852812519786 0.36294374960428 0.18147187480214 30 0.845724893947049 0.308550212105903 0.154275106052951 31 0.817405884135103 0.365188231729794 0.182594115864897 32 0.736671337052723 0.526657325894553 0.263328662947277 33 0.765192860230504 0.469614279538992 0.234807139769496 34 0.705966627892352 0.588066744215297 0.294033372107648 35 0.704689363909763 0.590621272180475 0.295310636090237 36 0.732451760497433 0.535096479005135 0.267548239502567 37 0.856411275337693 0.287177449324615 0.143588724662307 38 0.874780763343867 0.250438473312267 0.125219236656133 39 0.792074806312982 0.415850387374035 0.207925193687018 40 0.691701365999353 0.616597268001294 0.308298634000647 41 0.599700549249419 0.800598901501162 0.400299450750581 42 0.513131437687633 0.973737124624734 0.486868562312367 43 0.363351924088236 0.726703848176472 0.636648075911764 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0476190476190476 NOK 5% type I error level 2 0.0952380952380952 NOK 10% type I error level 2 0.0952380952380952 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/104r0l1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/104r0l1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/10wdn1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/10wdn1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/2pzur1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/2pzur1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/3lfvm1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/3lfvm1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/4lxpp1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/4lxpp1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/5mx531258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/5mx531258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/6nm331258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/6nm331258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/71mgr1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/71mgr1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/86l3l1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/86l3l1258562482.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/9jlrt1258562482.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t12585626458e5kr8lnewskxhl/9jlrt1258562482.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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