*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: Mon, 21 Dec 2009 02:25:37 -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/Dec/21/t1261397441k3asgmgtfhipo4t.htm/, Retrieved Mon, 21 Dec 2009 13:10:53 +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/Dec/21/t1261397441k3asgmgtfhipo4t.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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2.6 30.5 2.4 28.6 2.5 30 2.7 28.2 3.2 27.6 2.8 24.9 2.8 23.8 3 24.3 3.1 23.6 3.1 24.2 3 28.1 2.4 30.1 2.7 31.1 3 32 2.7 32.4 2.7 34 2 35.1 2.4 37.1 2.6 37.3 2.4 38.1 2.3 39.5 2.4 38.3 2.5 37.3 2.6 38.7 2.6 37.5 2.6 38.7 2.7 37.9 2.8 36.6 2.6 35.5 2.6 37.6 2 38.6 2 40.3 2.1 39 1.9 36.8 2 36.5 2.5 34.1 2.9 34.2 3.3 31.9 3.5 33.7 3.8 33.5 4.6 33.8 4.4 29.9 5.3 32.3 5.8 30.5 5.9 28.5 5.6 29 5.8 23.8 5.5 17.9 4.6 9.9 4.2 3 4 4.2 3.5 0.4 2.3 0 2.2 2.4 1.4 4.2 0.6 8.2 0 9 0.5 13.6 0.1 14 0.1 17.6

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 1.72689738135436 + 0.0180325925060919X[t] + 0.563065517828795M1[t] + 0.604580838282066M2[t] + 0.559211418219497M3[t] + 0.588103923918503M4[t] + 0.419685140811662M5[t] + 0.349102446604088M6[t] + 0.262651070991155M7[t] + 0.172953828727124M8[t] + 0.0885022159716228M9[t] + 0.0892638773611261M10[t] + 0.0662548720061118M11[t] + 0.0109433460576948t + 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) 1.72689738135436 1.140728 1.5139 0.136904 0.068452 X 0.0180325925060919 0.020237 0.8911 0.377519 0.18876 M1 0.563065517828795 0.945477 0.5955 0.554404 0.277202 M2 0.604580838282066 0.946487 0.6388 0.526145 0.263073 M3 0.559211418219497 0.943146 0.5929 0.556138 0.278069 M4 0.588103923918503 0.9432 0.6235 0.536023 0.268011 M5 0.419685140811662 0.941771 0.4456 0.657952 0.328976 M6 0.349102446604088 0.940309 0.3713 0.712146 0.356073 M7 0.262651070991155 0.937969 0.28 0.780717 0.390358 M8 0.172953828727124 0.936351 0.1847 0.854268 0.427134 M9 0.0885022159716228 0.935871 0.0946 0.92507 0.462535 M10 0.0892638773611261 0.935356 0.0954 0.924385 0.462193 M11 0.0662548720061118 0.935156 0.0708 0.943825 0.471913 t 0.0109433460576948 0.013621 0.8034 0.425863 0.212931 Multiple Linear Regression - Regression Statistics Multiple R 0.193509298948104 R-squared 0.0374458487793868 Adjusted R-squared -0.234580324391656 F-TEST (value) 0.137655315820812 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0.999798347790672 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.47849940054841 Sum Squared Residuals 100.554181961413 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.6 2.85090031667666 -0.250900316676658 2 2.4 2.86909705742605 -0.469097057426048 3 2.5 2.8599166129297 -0.359916612929702 4 2.7 2.86729379817544 -0.167293798175439 5 3.2 2.69899880562264 0.501001194377364 6 2.8 2.59067145770631 0.209328542293690 7 2.8 2.49532757639437 0.30467242360563 8 3 2.42558997644108 0.57441002355892 9 3.1 2.33945889498901 0.76054110501099 10 3.1 2.36198345793986 0.738016542060138 11 3 2.4202449094163 0.5797550905837 12 2.4 2.40099856848007 -0.000998568480067793 13 2.7 2.99304002487265 -0.293040024872649 14 3 3.06172802463910 -0.0617280246390973 15 2.7 3.03451498763666 -0.334514987636661 16 2.7 3.10320298740311 -0.403202987403108 17 2 2.96556340211066 -0.965563402110662 18 2.4 2.94198923897297 -0.541989238972968 19 2.6 2.87008772791895 -0.270087727918947 20 2.4 2.80575990571749 -0.405759905717485 21 2.3 2.75749726852821 -0.457497268528208 22 2.4 2.74756316496809 -0.347563164968095 23 2.5 2.71746491316468 -0.217464913164683 24 2.6 2.68739901672480 -0.0873990167247952 25 2.6 3.23976876960397 -0.639768769603974 26 2.6 3.31386654712225 -0.71386654712225 27 2.7 3.2650143991125 -0.565014399112503 28 2.8 3.28140788061128 -0.481407880611284 29 2.6 3.10409659180544 -0.504096591805436 30 2.6 3.08232568791835 -0.482325687918351 31 2 3.02485025086920 -1.02485025086920 32 2 2.97675176192322 -0.976751761923225 33 2.1 2.8798011249675 -0.779801124967498 34 1.9 2.85183442890129 -0.951834428901294 35 2 2.83435899185215 -0.834358991852147 36 2.5 2.73576924388911 -0.235769243889109 37 2.9 3.31158136702621 -0.411581367026208 38 3.3 3.32256507077316 -0.022565070773162 39 3.5 3.32059766327925 0.179402336720746 40 3.8 3.35682699653474 0.443173003465263 41 4.6 3.20476133723742 1.39523866276258 42 4.4 3.07479487831378 1.32520512168622 43 5.3 3.04256507077316 2.25743492922684 44 5.8 2.93135250805586 2.86864749194414 45 5.9 2.82177905634587 3.07822094365413 46 5.6 2.84250036004611 2.75749963995389 47 5.8 2.73666521971712 3.06333478028288 48 5.5 2.57496139798276 2.92503860201724 49 4.6 3.00470952182051 1.59529047817949 50 4.2 2.93274330003944 1.26725669996056 51 4 2.91995633704188 1.08004366295812 52 3.5 2.89126833727543 0.608731662724568 53 2.3 2.72657986322385 -0.426579863223848 54 2.2 2.71021873708859 -0.51021873708859 55 1.4 2.66716937404432 -1.26716937404432 56 0.6 2.66054584786235 -2.06054584786235 57 0 2.60146365516942 -2.60146365516942 58 0.5 2.69611858814464 -2.19611858814464 59 0.1 2.69126596584975 -2.59126596584975 60 0.1 2.70087177292327 -2.60087177292327 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0138075381305970 0.0276150762611940 0.986192461869403 18 0.00443858268568820 0.00887716537137641 0.995561417314312 19 0.00114220879465170 0.00228441758930339 0.998857791205348 20 0.000199888195492114 0.000399776390984229 0.999800111804508 21 3.25100474291404e-05 6.50200948582808e-05 0.99996748995257 22 4.86497989531882e-06 9.72995979063765e-06 0.999995135020105 23 7.27302783944554e-07 1.45460556788911e-06 0.999999272697216 24 1.83868799642384e-07 3.67737599284769e-07 0.9999998161312 25 2.51690393533294e-08 5.03380787066588e-08 0.99999997483096 26 3.35947602774986e-09 6.71895205549972e-09 0.999999996640524 27 4.40800755874597e-10 8.81601511749195e-10 0.9999999995592 28 5.28526746225048e-11 1.05705349245010e-10 0.999999999947147 29 7.15652035792799e-12 1.43130407158560e-11 0.999999999992844 30 8.21563564372013e-13 1.64312712874403e-12 0.999999999999178 31 7.26440141194268e-13 1.45288028238854e-12 0.999999999999274 32 3.55035417224154e-13 7.10070834448308e-13 0.999999999999645 33 1.52964399504690e-13 3.05928799009381e-13 0.999999999999847 34 5.12498726838839e-13 1.02499745367768e-12 0.999999999999488 35 2.91443539269332e-12 5.82887078538663e-12 0.999999999997086 36 3.54022438716447e-10 7.08044877432895e-10 0.999999999645978 37 8.40932300284752e-09 1.68186460056950e-08 0.999999991590677 38 1.12082152461754e-07 2.24164304923509e-07 0.999999887917848 39 1.35584549101814e-05 2.71169098203629e-05 0.99998644154509 40 0.00115705419455419 0.00231410838910839 0.998842945805446 41 0.0128869350971765 0.0257738701943531 0.987113064902823 42 0.346597511435971 0.693195022871942 0.653402488564029 43 0.836388118591422 0.327223762817157 0.163611881408578 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 23 0.851851851851852 NOK 5% type I error level 25 0.925925925925926 NOK 10% type I error level 25 0.925925925925926 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/10pli01261387532.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/10pli01261387532.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/1i1dy1261387531.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/1i1dy1261387531.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/29vpd1261387531.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/29vpd1261387531.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/3ufqq1261387531.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/3ufqq1261387531.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/43o3d1261387531.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/43o3d1261387531.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/5k8jc1261387531.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/5k8jc1261387531.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/66ncp1261387531.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/66ncp1261387531.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/7uxdv1261387532.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/7uxdv1261387532.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/8v6cx1261387532.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/8v6cx1261387532.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/9pebu1261387532.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261397441k3asgmgtfhipo4t/9pebu1261387532.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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