*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 09:55:46 -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/t125873623590w2svhyeolwm53.htm/, Retrieved Fri, 20 Nov 2009 17:57:27 +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/t125873623590w2svhyeolwm53.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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Dataseries X:

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110.3 0 114.1 96.8 87.4 111.4 103.9 0 110.3 114.1 96.8 87.4 101.6 0 103.9 110.3 114.1 96.8 94.6 0 101.6 103.9 110.3 114.1 95.9 0 94.6 101.6 103.9 110.3 104.7 0 95.9 94.6 101.6 103.9 102.8 0 104.7 95.9 94.6 101.6 98.1 0 102.8 104.7 95.9 94.6 113.9 0 98.1 102.8 104.7 95.9 80.9 0 113.9 98.1 102.8 104.7 95.7 0 80.9 113.9 98.1 102.8 113.2 0 95.7 80.9 113.9 98.1 105.9 0 113.2 95.7 80.9 113.9 108.8 0 105.9 113.2 95.7 80.9 102.3 0 108.8 105.9 113.2 95.7 99 0 102.3 108.8 105.9 113.2 100.7 0 99 102.3 108.8 105.9 115.5 0 100.7 99 102.3 108.8 100.7 0 115.5 100.7 99 102.3 109.9 0 100.7 115.5 100.7 99 114.6 0 109.9 100.7 115.5 100.7 85.4 0 114.6 109.9 100.7 115.5 100.5 0 85.4 114.6 109.9 100.7 114.8 0 100.5 85.4 114.6 109.9 116.5 0 114.8 100.5 85.4 114.6 112.9 0 116.5 114.8 100.5 85.4 102 0 112.9 116.5 114.8 100.5 106 0 102 112.9 116.5 114.8 105.3 0 106 102 112.9 116.5 118.8 0 105.3 106 102 112.9 106.1 0 118.8 105.3 106 102 109.3 0 106.1 118.8 105.3 106 117.2 0 109.3 106.1 118.8 105.3 92.5 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 50.2362928119604 -10.5265285686030X[t] -0.202061509463685Y1[t] + 0.236569774841816Y2[t] + 0.554834223280099Y3[t] -0.0160517215222272Y4[t] + 15.3454731041248M1[t] -1.3210319312054M2[t] -18.4272530266396M3[t] -18.0250317516137M4[t] -11.374623572876M5[t] + 1.64234957827378M6[t] -3.43736217073495M7[t] -8.17796939620031M8[t] -2.59167880781004M9[t] -24.2441038493184M10[t] -20.5106923504080M11[t] + 0.121203282376966t + 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) 50.2362928119604 17.613864 2.8521 0.006912 0.003456 X -10.5265285686030 2.790755 -3.7719 0.000538 0.000269 Y1 -0.202061509463685 0.156023 -1.2951 0.202908 0.101454 Y2 0.236569774841816 0.120681 1.9603 0.057134 0.028567 Y3 0.554834223280099 0.121799 4.5553 5e-05 2.5e-05 Y4 -0.0160517215222272 0.149372 -0.1075 0.914974 0.457487 M1 15.3454731041248 5.05474 3.0359 0.004259 0.00213 M2 -1.3210319312054 7.150494 -0.1847 0.854385 0.427192 M3 -18.4272530266396 4.747283 -3.8816 0.000389 0.000195 M4 -18.0250317516137 3.206325 -5.6217 2e-06 1e-06 M5 -11.374623572876 2.91686 -3.8996 0.000369 0.000185 M6 1.64234957827378 3.432236 0.4785 0.634961 0.31748 M7 -3.43736217073495 4.611566 -0.7454 0.460512 0.230256 M8 -8.17796939620031 4.622097 -1.7693 0.08466 0.04233 M9 -2.59167880781004 3.509868 -0.7384 0.464693 0.232347 M10 -24.2441038493184 4.060683 -5.9704 1e-06 0 M11 -20.5106923504080 4.351302 -4.7137 3.1e-05 1.5e-05 t 0.121203282376966 0.058058 2.0876 0.043413 0.021707 Multiple Linear Regression - Regression Statistics Multiple R 0.947256615280796 R-squared 0.89729509519323 Adjusted R-squared 0.852526290533869 F-TEST (value) 20.0428647139588 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 3.530509218308e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.49639337270593 Sum Squared Residuals 476.765898051377 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.3 112.252054510448 -1.95205451044806 2 103.9 106.167926613587 -2.26792661358659 3 101.6 99.0248831971348 2.57511680286515 4 94.6 96.1129378365177 -1.51293783651767 5 95.9 100.264926894534 -4.36492689453374 6 104.7 110.311047246063 -5.61104724606301 7 102.8 100.035017599986 2.7649824000144 8 98.1 98.714991084406 -0.614991084405923 9 113.9 109.784365404339 4.11563459566102 10 80.9 82.753253680297 -1.853253680297 11 95.7 94.4364781378625 1.26352186213746 12 113.2 113.112884679785 0.0871153202149736 13 105.9 109.981570750037 -4.08157075003667 14 108.8 107.792542390679 1.00745760932087 15 102.3 97.9666202727048 4.33337972729524 16 99 96.1583020320791 2.84169796792085 17 100.7 103.785209752577 -3.08520975257667 18 115.5 112.146228919302 3.35377108069795 19 100.7 102.872761982909 -2.17276198290899 20 109.9 105.741289908142 4.15871009185848 21 114.6 114.272843802142 0.327156197858334 22 85.4 85.5192628940012 -0.119262894001209 23 100.5 101.727992026091 -1.22799202609066 24 114.8 114.860966452005 -0.0609664520048876 25 116.5 114.733764442354 1.76623555764602 26 112.9 110.074612943529 2.82538705647099 27 102 101.910933579692 0.0890664203080536 28 106 104.498855962627 1.50114403737323 29 105.3 105.858919709715 -0.558919709714725 30 118.8 114.094911462960 4.70508853703975 31 106.1 108.637274433892 -2.53727443389214 32 109.3 109.325152778972 -0.0251527789720770 33 117.2 118.883111898311 -1.68311189831134 34 92.5 89.2495346177033 3.25046538229669 35 104.2 101.943296281823 2.25670371817728 36 112.5 118.699623670331 -6.19962367033133 37 122.4 121.425841978890 0.97415802111033 38 113.3 111.731698347409 1.56830165259076 39 100 103.344799952820 -3.34479995282032 40 110.7 109.762487156868 0.937512843131833 41 112.8 106.017758986406 6.78224101359374 42 109.8 114.029678337094 -4.22967833709365 43 117.3 116.324365011363 0.975634988636562 44 109.1 110.473188871372 -1.37318887137233 45 115.9 117.913649146018 -2.01364914601842 46 96 97.2779488079985 -1.27794880799848 47 99.8 102.092233554224 -2.29223355422409 48 116.8 110.626525197879 6.17347480212124 49 115.7 112.406768318272 3.29323168172839 50 99.4 102.533219704796 -3.13321970479604 51 94.3 97.9527629976481 -3.65276299764814 52 91 94.7674170119082 -3.76741701190823 53 93.2 91.9731846567686 1.22681534323140 54 103.1 101.318134034581 1.78186596541895 55 94.1 93.1305809718498 0.969419028150166 56 91.8 93.9453773571081 -2.14537735710815 57 102.7 103.446029749190 -0.746029749189583 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.729110849558781 0.541778300882437 0.270889150441219 22 0.610023103158455 0.77995379368309 0.389976896841545 23 0.506269408139698 0.987461183720605 0.493730591860302 24 0.360919085599711 0.721838171199421 0.63908091440029 25 0.251155399477751 0.502310798955501 0.74884460052225 26 0.189462830120848 0.378925660241697 0.810537169879152 27 0.210222141834129 0.420444283668259 0.78977785816587 28 0.145181702966681 0.290363405933361 0.85481829703332 29 0.149184836200164 0.298369672400327 0.850815163799836 30 0.160905471659361 0.321810943318722 0.839094528340639 31 0.156597369257074 0.313194738514148 0.843402630742926 32 0.110233445838266 0.220466891676532 0.889766554161734 33 0.113041874342426 0.226083748684852 0.886958125657574 34 0.105586526540899 0.211173053081798 0.894413473459101 35 0.0557112159048024 0.111422431809605 0.944288784095198 36 0.59968594742987 0.80062810514026 0.40031405257013 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/t125873623590w2svhyeolwm53/10jbut1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/10jbut1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/1npqs1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/1npqs1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/2t6yn1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/2t6yn1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/3ufig1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/3ufig1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/4jmi91258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/4jmi91258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/5rknp1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/5rknp1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/6ayzs1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/6ayzs1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/7yec91258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/7yec91258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/87gkz1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/87gkz1258736139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/99xjd1258736139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t125873623590w2svhyeolwm53/99xjd1258736139.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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