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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 05:56:34 -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/t1258721846dvy5aolghkcmxpv.htm/, Retrieved Fri, 20 Nov 2009 13:57:42 +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/t1258721846dvy5aolghkcmxpv.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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89.1 72.7 82.6 79.7 102.7 115.8 91.8 87.8 94.1 99.2 103.1 111.4 93.2 102.3 91 94.4 94.3 118.5 99.4 112.1 115.7 136.5 116.8 139.8 99.8 104.5 96 123.3 115.9 156.6 109.1 136.2 117.3 147.5 109.8 143.8 112.8 135.8 110.7 121.6 100 128 113.3 129.7 122.4 136.2 112.5 130.5 104.2 99.2 92.5 110.4 117.2 151.6 109.3 129.6 106.1 123.6 118.8 142.7 105.3 119 106 118.1 102 120 112.9 124.3 116.5 123.3 114.8 122.4 100.5 90.5 85.4 91 114.6 137 109.9 127.7 100.7 105.1 115.5 135.6 100.7 112.4 99 102.5 102.3 112.6 108.8 110.8 105.9 103.4 113.2 117.6 95.7 87.5 80.9 87 113.9 130 98.1 102.9 102.8 111.1 104.7 128.9 95.9 106.3 94.6 99 101.6 109.9 103.9 104 110.3 112.9 114.1 113.6

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 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation TotaleIndustrieleProductie[t] = + 65.3855849994479 + 0.391844967146594Investeringsgoederen[t] -3.13645561373052M1[t] -16.4161083706153M2[t] -6.67855945910728M3[t] -7.52875096085604M4[t] -7.14899964574348M5[t] -6.91720624702877M6[t] -8.93045141604976M7[t] -7.10001788019115M8[t] -11.5049221293168M9[t] -3.25013328253928M10[t] + 0.789080323780102M11[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) 65.3855849994479 4.329953 15.1008 0 0 Investeringsgoederen 0.391844967146594 0.032659 11.9981 0 0 M1 -3.13645561373052 2.346995 -1.3364 0.187862 0.093931 M2 -16.4161083706153 2.243134 -7.3184 0 0 M3 -6.67855945910728 2.115353 -3.1572 0.002781 0.001391 M4 -7.52875096085604 2.085633 -3.6098 0.000742 0.000371 M5 -7.14899964574348 2.083819 -3.4307 0.001264 0.000632 M6 -6.91720624702877 2.084673 -3.3181 0.001755 0.000878 M7 -8.93045141604976 2.093163 -4.2665 9.5e-05 4.8e-05 M8 -7.10001788019115 2.148319 -3.3049 0.001824 0.000912 M9 -11.5049221293168 2.081968 -5.526 1e-06 1e-06 M10 -3.25013328253928 2.088423 -1.5563 0.126355 0.063177 M11 0.789080323780102 2.070837 0.381 0.704887 0.352444 Multiple Linear Regression - Regression Statistics Multiple R 0.95286188776125 R-squared 0.907945777147933 Adjusted R-squared 0.884442571313363 F-TEST (value) 38.6307205722743 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.27208786320357 Sum Squared Residuals 503.208272272634 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 89.1 90.7362584972752 -1.63625849727518 2 82.6 80.1995205104163 2.40047948958369 3 102.7 104.082672735916 -1.3826727359163 4 91.8 92.260822154063 -0.460822154062903 5 94.1 97.1076060946467 -3.00760609464666 6 103.1 102.119908092550 0.980091907450189 7 93.2 96.5408737224948 -3.3408737224948 8 91 95.2757320178953 -4.27573201789533 9 94.3 100.314291477003 -6.01429147700263 10 99.4 106.061272534042 -6.66127253404189 11 115.7 119.661503338738 -3.96150333873819 12 116.8 120.165511406542 -3.36551140654184 13 99.8 103.196928452537 -3.39692845253655 14 96 97.2839610780078 -1.28396107800777 15 115.9 120.069947395497 -4.16994739549732 16 109.1 111.226118563958 -2.12611856395806 17 117.3 116.033718007827 1.26628199217286 18 109.8 114.815685028099 -5.01568502809945 19 112.8 109.667680121906 3.13231987809430 20 110.7 105.933915124283 4.76608487571733 21 100 104.036818664895 -4.03681866489526 22 113.3 112.957743955822 0.342256044178048 23 122.4 119.543949848594 2.85605015140581 24 112.5 116.521353212079 -4.02135321207852 25 104.2 101.120150126660 3.0798498733404 26 92.5 92.2291610018167 0.270838998183285 27 117.2 118.110722559764 -0.910722559764356 28 109.3 108.639941780791 0.66005821920946 29 106.1 106.668623293024 -0.568623293023542 30 118.8 114.384655564238 4.41534443576181 31 105.3 103.084684673843 2.21531532615708 32 106 104.562457739270 1.4375422607304 33 102 100.902058927723 1.09794107227750 34 112.9 110.841781133230 2.05821886676966 35 116.5 114.489149772403 2.01085022759686 36 114.8 113.347408978191 1.45259102180889 37 100.5 97.7110989124842 2.78890108751577 38 85.4 84.6273686391728 0.772631360827213 39 114.6 112.389786039424 2.21021396057590 40 109.9 107.895436343212 2.00456365678799 41 100.7 99.4194914008116 1.28050859918845 42 115.5 111.602556297497 3.89744370250263 43 100.7 100.498507890675 0.201492109324601 44 99 98.4496762517827 0.550323748217264 45 102.3 98.0024061708377 4.29759382916229 46 108.8 105.551874076751 3.24812592324867 47 105.9 106.691434926186 -0.791434926185919 48 113.2 111.466553135887 1.73344686411255 49 95.7 96.5355640110444 -0.835564011044446 50 80.9 83.0599887705864 -2.15998877058641 51 113.9 109.646871269398 4.25312873060207 52 98.1 98.1776811579765 -0.0776811579764897 53 102.8 101.770561203691 1.02943879630888 54 104.7 108.977195017615 -4.27719501761519 55 95.9 98.1082535910812 -2.20825359108117 56 94.6 97.0782188667697 -2.47821886676966 57 101.6 96.9444247595419 4.65557524045809 58 103.9 102.887328300154 1.01267169984552 59 110.3 110.413962114079 -0.113962114078570 60 114.1 109.899173267301 4.20082673269892 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0186078023987442 0.0372156047974883 0.981392197601256 17 0.34284318455928 0.68568636911856 0.65715681544072 18 0.489813788911416 0.979627577822832 0.510186211088584 19 0.716187185212172 0.567625629575656 0.283812814787828 20 0.909225348749785 0.181549302500430 0.0907746512502151 21 0.938676766612407 0.122646466775186 0.0613232333875929 22 0.956260400156272 0.0874791996874552 0.0437395998437276 23 0.96494027650024 0.0701194469995183 0.0350597234997592 24 0.982963505011813 0.0340729899763741 0.0170364949881870 25 0.98380667275438 0.0323866544912384 0.0161933272456192 26 0.970580928227385 0.0588381435452304 0.0294190717726152 27 0.979122822825159 0.0417543543496824 0.0208771771748412 28 0.967166935276492 0.0656661294470158 0.0328330647235079 29 0.961703695023962 0.076592609952075 0.0382963049760375 30 0.975314781868863 0.0493704362622732 0.0246852181311366 31 0.967011497237435 0.0659770055251303 0.0329885027625652 32 0.945425629711653 0.109148740576694 0.0545743702883472 33 0.971441768497614 0.0571164630047727 0.0285582315023863 34 0.96840176955596 0.0631964608880787 0.0315982304440394 35 0.948471357142022 0.103057285715955 0.0515286428579775 36 0.946357099502944 0.107285800994111 0.0536429004970555 37 0.938585914212868 0.122828171574264 0.061414085787132 38 0.910466733135984 0.179066533728032 0.0895332668640159 39 0.899559894602705 0.200880210794590 0.100440105397295 40 0.877881692803824 0.244236614392352 0.122118307196176 41 0.82597476540993 0.348050469180139 0.174025234590070 42 0.964625359082896 0.0707492818342086 0.0353746409171043 43 0.926958169700755 0.146083660598490 0.0730418302992452 44 0.90927189681722 0.181456206365561 0.0907281031827803 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 5 0.172413793103448 NOK 10% type I error level 14 0.482758620689655 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/10zsry1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/10zsry1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/1eq2y1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/1eq2y1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/2mt881258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/2mt881258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/3lb2b1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/3lb2b1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/45uau1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/45uau1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/5rlt91258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/5rlt91258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/6gd3h1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/6gd3h1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/742er1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/742er1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/84gvo1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/84gvo1258721790.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/984yd1258721790.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258721846dvy5aolghkcmxpv/984yd1258721790.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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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