*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: Sat, 28 Nov 2009 05:57:52 -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/28/t125941346572dkoupasctdaww.htm/, Retrieved Sat, 28 Nov 2009 14:04:37 +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/28/t125941346572dkoupasctdaww.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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4.3 29 3.9 31 4 31 4.3 33 4.8 37 4.4 30 4.3 20 4.7 19 4.7 17 4.9 22 5 12 4.2 25 4.3 25 4.8 29 4.8 32 4.8 31 4.2 28 4.6 28 4.8 28 4.5 32 4.4 35 4.3 30 3.9 32 3.7 38 4 37 4.1 28 3.7 34 3.8 35 3.8 32 3.8 39 3.3 37 3.3 38 3.3 35 3.2 25 3.4 25 4.2 26 4.9 13 5.1 19 5.5 17 5.6 21 6.4 23 6.1 18 7.1 12 7.8 7 7.9 4 7.4 14 7.5 16 6.8 13 5.2 13 4.7 10 4.1 19 3.9 13 2.6 14 2.7 25 1.8 28 1 30 0.3 31 1.3 42 1 41 1.1 38

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Consumentenprijsindex[t] = + 7.15369281765094 -0.110557289171447Consumentenvertrouwen[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) 7.15369281765094 0.468741 15.2615 0 0 Consumentenvertrouwen -0.110557289171447 0.017119 -6.4581 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.646760740970052 R-squared 0.418299456060131 Adjusted R-squared 0.40827013633703 F-TEST (value) 41.7076599020604 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 2.36776167561459e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.22837132348528 Sum Squared Residuals 87.5159742849374 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.3 3.947531431679 0.352468568320999 2 3.9 3.7264168533361 0.173583146663902 3 4 3.72641685333610 0.273583146663905 4 4.3 3.5053022749932 0.794697725006798 5 4.8 3.06307311830742 1.73692688169258 6 4.4 3.83697414250754 0.563025857492458 7 4.3 4.94254703422201 -0.642547034222009 8 4.7 5.05310432339346 -0.353104323393456 9 4.7 5.27421890173635 -0.574218901736349 10 4.9 4.72143245587912 0.178567544120885 11 5 5.82700534759358 -0.827005347593583 12 4.2 4.38976058836478 -0.189760588364775 13 4.3 4.38976058836478 -0.0897605883647757 14 4.8 3.94753143167899 0.852468568321011 15 4.8 3.61585956416465 1.18414043583535 16 4.8 3.72641685333610 1.07358314666390 17 4.2 4.05808872085044 0.141911279149565 18 4.6 4.05808872085044 0.541911279149564 19 4.8 4.05808872085044 0.741911279149564 20 4.5 3.61585956416465 0.884140435835351 21 4.4 3.28418769665031 1.11581230334969 22 4.3 3.83697414250754 0.463025857492458 23 3.9 3.61585956416465 0.284140435835351 24 3.7 2.95251582913597 0.747484170864032 25 4 3.06307311830742 0.936926881692585 26 4.1 4.05808872085044 0.0419112791495642 27 3.7 3.39474498582176 0.305255014178245 28 3.8 3.28418769665031 0.515812303349691 29 3.8 3.61585956416465 0.184140435835351 30 3.8 2.84195853996452 0.958041460035478 31 3.3 3.06307311830742 0.236926881692585 32 3.3 2.95251582913597 0.347484170864031 33 3.3 3.28418769665031 0.0158123033496914 34 3.2 4.38976058836478 -1.18976058836478 35 3.4 4.38976058836478 -0.989760588364776 36 4.2 4.27920329919333 -0.0792032991933287 37 4.9 5.71644805842214 -0.816448058422136 38 5.1 5.05310432339346 0.0468956766065438 39 5.5 5.27421890173635 0.225781098263651 40 5.6 4.83198974505056 0.768010254949437 41 6.4 4.61087516670767 1.78912483329233 42 6.1 5.1636616125649 0.936338387435097 43 7.1 5.82700534759358 1.27299465240642 44 7.8 6.37979179345082 1.42020820654918 45 7.9 6.71146366096516 1.18853633903484 46 7.4 5.60589076925069 1.79410923074931 47 7.5 5.3847761909078 2.11522380909220 48 6.8 5.71644805842214 1.08355194157786 49 5.2 5.71644805842214 -0.516448058422136 50 4.7 6.04811992593648 -1.34811992593648 51 4.1 5.05310432339346 -0.953104323393456 52 3.9 5.71644805842214 -1.81644805842214 53 2.6 5.60589076925069 -3.00589076925069 54 2.7 4.38976058836478 -1.68976058836478 55 1.8 4.05808872085044 -2.25808872085044 56 1 3.83697414250754 -2.83697414250754 57 0.3 3.72641685333610 -3.42641685333610 58 1.3 2.51028667245018 -1.21028667245018 59 1 2.62084396162163 -1.62084396162163 60 1.1 2.95251582913597 -1.85251582913597 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.012355946525155 0.02471189305031 0.987644053474845 6 0.00404392319084856 0.00808784638169712 0.995956076809151 7 0.00220267106562512 0.00440534213125025 0.997797328934375 8 0.00123905694541382 0.00247811389082764 0.998760943054586 9 0.000375574002073033 0.000751148004146065 0.999624425997927 10 0.000187464621216161 0.000374929242432321 0.999812535378784 11 5.7987134691621e-05 0.000115974269383242 0.999942012865308 12 1.90307879001952e-05 3.80615758003904e-05 0.9999809692121 13 4.784081711071e-06 9.568163422142e-06 0.99999521591829 14 2.59341949289918e-06 5.18683898579836e-06 0.999997406580507 15 1.58077462859153e-06 3.16154925718307e-06 0.999998419225371 16 7.5764997338007e-07 1.51529994676014e-06 0.999999242350027 17 2.46277887172287e-07 4.92555774344573e-07 0.999999753722113 18 6.23790301955485e-08 1.24758060391097e-07 0.99999993762097 19 2.34192292366934e-08 4.68384584733869e-08 0.99999997658077 20 5.99387925831233e-09 1.19877585166247e-08 0.99999999400612 21 1.65700297615791e-09 3.31400595231582e-09 0.999999998342997 22 4.26680162373953e-10 8.53360324747906e-10 0.99999999957332 23 3.53447806231827e-10 7.06895612463653e-10 0.999999999646552 24 3.78543135625803e-10 7.57086271251605e-10 0.999999999621457 25 1.51266659374648e-10 3.02533318749297e-10 0.999999999848733 26 5.52077970477331e-11 1.10415594095466e-10 0.999999999944792 27 5.13152041524691e-11 1.02630408304938e-10 0.999999999948685 28 2.85609978756855e-11 5.7121995751371e-11 0.99999999997144 29 1.71277082551723e-11 3.42554165103446e-11 0.999999999982872 30 1.18995339863546e-11 2.37990679727093e-11 0.9999999999881 31 3.39730082247771e-11 6.79460164495542e-11 0.999999999966027 32 7.67781316157901e-11 1.53556263231580e-10 0.999999999923222 33 1.79436421108441e-10 3.58872842216882e-10 0.999999999820564 34 2.16590352842888e-09 4.33180705685777e-09 0.999999997834097 35 4.39135553989142e-09 8.78271107978283e-09 0.999999995608644 36 1.63702280958486e-09 3.27404561916973e-09 0.999999998362977 37 6.381989122618e-10 1.2763978245236e-09 0.9999999993618 38 3.00412958829242e-10 6.00825917658484e-10 0.999999999699587 39 2.53709448230088e-10 5.07418896460176e-10 0.99999999974629 40 6.43458302288906e-10 1.28691660457781e-09 0.999999999356542 41 1.57948518663659e-07 3.15897037327318e-07 0.999999842051481 42 4.43041489463411e-07 8.86082978926823e-07 0.99999955695851 43 2.98906321985221e-06 5.97812643970441e-06 0.99999701093678 44 1.39288551812556e-05 2.78577103625112e-05 0.999986071144819 45 1.99545618860670e-05 3.99091237721339e-05 0.999980045438114 46 0.000269432008872195 0.000538864017744391 0.999730567991128 47 0.0232357781521765 0.046471556304353 0.976764221847824 48 0.206881359579218 0.413762719158437 0.793118640420782 49 0.317548173691276 0.635096347382552 0.682451826308724 50 0.363046413606 0.726092827212 0.636953586394 51 0.562918361479791 0.874163277040418 0.437081638520209 52 0.703330502393012 0.593338995213976 0.296669497606988 53 0.695340055304839 0.609319889390322 0.304659944695161 54 0.865950368954884 0.268099262090232 0.134049631045116 55 0.958546226361257 0.0829075472774853 0.0414537736387427 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 41 0.80392156862745 NOK 5% type I error level 43 0.843137254901961 NOK 10% type I error level 44 0.862745098039216 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/10apec1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/10apec1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/1qf8d1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/1qf8d1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/2nu0a1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/2nu0a1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/3es7p1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/3es7p1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/4ty7e1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/4ty7e1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/5fu4n1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/5fu4n1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/6freo1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/6freo1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/799131259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/799131259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/8y7tw1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/8y7tw1259413067.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/9bfrn1259413067.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/28/t125941346572dkoupasctdaww/9bfrn1259413067.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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