Workshop6/module1

*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: Tue, 24 Nov 2009 11:17:39 -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/24/t12590869326ey6enmylhu51jc.htm/, Retrieved Tue, 24 Nov 2009 19:22:24 +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/24/t12590869326ey6enmylhu51jc.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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114.08 136.49 112.95 142.62 135.31 141.71 134.31 149.51 133.03 147.39 140.11 131.96 124.69 136.38 131.68 127.34 150.95 133.85 137.26 125.14 130.51 141.25 143.15 149.32 118.01 120.92 122.56 134.85 147.97 131.93 135.74 134.22 151.62 143.07 154.82 145.37 145.59 134.32 147.12 126.31 175.86 162.21 140.66 124.09 152.69 153.91 154.38 154.34 132.45 138.70 136.44 150.98 153.24 146.39 154.11 178.30 155.93 168.23 142.53 162.52 148.73 158.86 147.73 152.17 166.79 171.01 144.30 171.49 156.07 189.62 161.70 177.46 152.10 179.98 140.45 156.96 155.56 167.89 174.53 194.78 167.16 192.78 159.48 165.06 173.22 196.60 176.13 151.64 180.31 187.02 185.84 210.99 169.43 219.08 195.25 235.68 174.99 241.44 156.42 187.46 182.08 229.57 182.00 208.44 153.28 215.09 136.72 217.00 130.19 171.08 132.04 178.41 143.89 196.34 133.38 172.11 127.98 154.93 150.45 182.26 133.55 181.74

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 InvoerEU[t] = + 90.7172722565875 + 0.390789979219476InvoerAM[t] -18.2713893456953M1[t] -17.3592425044589M2[t] + 0.221347720986538M3[t] -2.20547816051791M4[t] -6.24186513507341M5[t] -8.22411062064349M6[t] -8.5439528631746M7[t] -1.29139665823474M8[t] + 6.37482333788861M9[t] -5.25423247582743M10[t] -10.5025775073663M11[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) 90.7172722565875 12.252527 7.404 0 0 InvoerAM 0.390789979219476 0.0595 6.5679 0 0 M1 -18.2713893456953 8.123873 -2.2491 0.029131 0.014566 M2 -17.3592425044589 8.577429 -2.0238 0.048574 0.024287 M3 0.221347720986538 8.50056 0.026 0.979334 0.489667 M4 -2.20547816051791 8.454535 -0.2609 0.795314 0.397657 M5 -6.24186513507341 8.453807 -0.7383 0.463897 0.231948 M6 -8.22411062064349 8.494714 -0.9681 0.337825 0.168913 M7 -8.5439528631746 8.531436 -1.0015 0.321623 0.160812 M8 -1.29139665823474 8.665364 -0.149 0.882155 0.441077 M9 6.37482333788861 8.464761 0.7531 0.455067 0.227533 M10 -5.25423247582743 8.520668 -0.6166 0.540382 0.270191 M11 -10.5025775073663 8.458542 -1.2417 0.220398 0.110199 Multiple Linear Regression - Regression Statistics Multiple R 0.76477950540453 R-squared 0.584887691886798 Adjusted R-squared 0.481109614858498 F-TEST (value) 5.63594651813888 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 6.45611813276936e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.3526474338707 Sum Squared Residuals 8558.07328767618 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 114.08 125.784807174559 -11.7048071745590 2 112.95 129.092496588410 -16.1424965884104 3 135.31 146.317467932766 -11.0074679327661 4 134.31 146.938803889174 -12.6288038891735 5 133.03 142.073942158673 -9.04394215867274 6 140.11 134.061807293746 6.04819270625387 7 124.69 135.469256759365 -10.7792567593651 8 131.68 139.189071552161 -7.5090715521609 9 150.95 149.399334313003 1.55066568699694 10 137.26 134.366497780285 2.89350221971461 11 130.51 135.413779313972 -4.90377931397221 12 143.15 149.070031953640 -5.92003195363972 13 118.01 119.700207198111 -1.69020719811132 14 122.56 126.056058449875 -3.49605844987504 15 147.97 142.495541936000 5.47445806400042 16 135.74 140.963625106908 -5.22362510690773 17 151.62 140.385729448445 11.2342705515554 18 154.82 139.302300915079 15.5176990849207 19 145.59 134.664229402173 10.9257705978270 20 147.12 138.786557873565 8.33344212643516 21 175.86 160.482138123667 15.3778618763326 22 140.66 133.956168302105 6.70383169789507 23 152.69 140.361180450891 12.3288195491092 24 154.38 151.031797649322 3.34820235067849 25 132.45 126.648453028634 5.80154697136638 26 136.44 132.359500814685 4.08049918531481 27 153.24 148.146365035513 5.09363496448681 28 154.11 158.189647390902 -4.07964739090224 29 155.93 150.218005325607 5.71199467439339 30 142.53 146.004349058693 -3.47434905869334 31 148.73 144.254215492219 4.47578450778104 32 147.73 148.892386736180 -1.16238673618050 33 166.79 163.921089940799 2.86891005920120 34 144.3 152.479613317108 -8.17961331710809 35 156.07 154.316290608818 1.75370939118171 36 161.7 160.066861968876 1.63313803112419 37 152.1 142.780263370814 9.3197366291864 38 140.45 134.696424890418 5.75357510958232 39 155.56 156.548349588732 -0.988349588731937 40 174.53 164.629866248439 9.90013375156078 41 167.16 159.811899315445 7.34810068455523 42 159.48 146.996955605911 12.4830443940892 43 173.22 159.002629307962 14.2173706920380 44 176.13 148.685268047194 27.4447319528058 45 180.31 170.177637508103 10.1323624918974 46 185.84 167.915817496277 17.9241825037226 47 169.43 165.828963396624 3.60103660337595 48 195.25 182.818654559034 12.4313454409663 49 174.99 166.798215493643 8.19178450635741 50 156.42 146.615519256612 9.8044807433883 51 182.08 180.652275506989 1.42772449301077 52 182 169.968057364577 12.0319426354227 53 153.28 168.530423751831 -15.2504237518313 54 136.72 167.294587126570 -30.5745871265704 55 130.19 149.029669038281 -18.8396690382810 56 132.04 159.146715790900 -27.1067157908995 57 143.89 173.819800114428 -29.9298001144281 58 133.38 152.721903104224 -19.3419031042242 59 127.98 140.759786229695 -12.7797862296947 60 150.45 161.942653869129 -11.4926538691293 61 133.55 143.46805373424 -9.91805373423986 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0486338734819288 0.0972677469638577 0.951366126518071 17 0.0855024121052266 0.171004824210453 0.914497587894773 18 0.136381760951064 0.272763521902128 0.863618239048936 19 0.168284263652392 0.336568527304783 0.831715736347608 20 0.141997551445717 0.283995102891434 0.858002448554283 21 0.194063397447900 0.388126794895799 0.8059366025521 22 0.125686060427097 0.251372120854195 0.874313939572903 23 0.121559508866286 0.243119017732571 0.878440491133714 24 0.0818332958846213 0.163666591769243 0.918166704115379 25 0.0610848267875819 0.122169653575164 0.938915173212418 26 0.0450513158694378 0.0901026317388757 0.954948684130562 27 0.0268859024935885 0.0537718049871769 0.973114097506412 28 0.0164608245889039 0.0329216491778077 0.983539175411096 29 0.00889120805113204 0.0177824161022641 0.991108791948868 30 0.00922489919333084 0.0184497983866617 0.990775100806669 31 0.0047633577917952 0.0095267155835904 0.995236642208205 32 0.00232191386933862 0.00464382773867724 0.997678086130661 33 0.00136306750855467 0.00272613501710933 0.998636932491445 34 0.000957822407401818 0.00191564481480364 0.999042177592598 35 0.000415166629240552 0.000830333258481104 0.99958483337076 36 0.000171471968665296 0.000342943937330592 0.999828528031335 37 0.000114567488303261 0.000229134976606521 0.999885432511697 38 5.84924889815724e-05 0.000116984977963145 0.999941507511018 39 2.02215353975267e-05 4.04430707950534e-05 0.999979778464603 40 1.30628728061160e-05 2.61257456122319e-05 0.999986937127194 41 6.76569317837849e-06 1.35313863567570e-05 0.999993234306822 42 3.98026062621464e-05 7.96052125242928e-05 0.999960197393738 43 3.30720316337286e-05 6.61440632674571e-05 0.999966927968366 44 0.0159132858608517 0.0318265717217033 0.984086714139148 45 0.331643211450914 0.663286422901828 0.668356788549086 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 13 0.433333333333333 NOK 5% type I error level 17 0.566666666666667 NOK 10% type I error level 20 0.666666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/10knxx1259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/10knxx1259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/1w54d1259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/1w54d1259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/2ytj21259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/2ytj21259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/39at61259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/39at61259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/4849v1259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/4849v1259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/51ohs1259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/51ohs1259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/6xzmo1259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/6xzmo1259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/7s6q91259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/7s6q91259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/800pl1259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/800pl1259086654.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/9i0i01259086654.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/24/t12590869326ey6enmylhu51jc/9i0i01259086654.ps (open in new window)

Parameters (Session):

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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