BDM 2

*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: Wed, 18 Nov 2009 10:56:14 -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/18/t1258567007qxxj14coqsqv9ox.htm/, Retrieved Wed, 18 Nov 2009 18:56:59 +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/18/t1258567007qxxj14coqsqv9ox.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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101,3 0 106,3 0 94 0 102,8 0 102 0 105,1 1 92,4 0 81,4 0 105,8 0 120,3 1 100,7 0 88,8 0 94,3 0 99,9 0 103,4 0 103,3 0 98,8 0 104,2 0 91,2 0 74,7 0 108,5 0 114,5 0 96,9 0 89,6 0 97,1 0 100,3 0 122,6 0 115,4 1 109 0 129,1 1 102,8 1 96,2 0 127,7 1 128,9 1 126,5 1 119,8 1 113,2 1 114,1 1 134,1 1 130 1 121,8 1 132,1 1 105,3 1 103 1 117,1 1 126,3 1 138,1 1 119,5 1 138 1 135,5 1 178,6 1 162,2 1 176,9 1 204,9 1 132,2 1 142,5 1 164,3 1 174,9 1 175,4 1 143 1

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 Omzet[t] = + 99.2592592592592 + 35.8498316498317Uitvoer[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) 99.2592592592592 3.797098 26.1408 0 0 Uitvoer 35.8498316498317 5.120006 7.0019 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.67681574505074 R-squared 0.458079552748587 Adjusted R-squared 0.448736096761494 F-TEST (value) 49.0267791041517 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 2.90954016435307e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 19.7302989726515 Sum Squared Residuals 22578.5124579125 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.3 99.2592592592595 2.04074074074051 2 106.3 99.2592592592592 7.04074074074076 3 94 99.2592592592593 -5.25925925925925 4 102.8 99.2592592592593 3.54074074074075 5 102 99.2592592592593 2.74074074074075 6 105.1 135.109090909091 -30.0090909090909 7 92.4 99.2592592592593 -6.85925925925924 8 81.4 99.2592592592593 -17.8592592592592 9 105.8 99.2592592592593 6.54074074074075 10 120.3 135.109090909091 -14.8090909090909 11 100.7 99.2592592592593 1.44074074074075 12 88.8 99.2592592592593 -10.4592592592593 13 94.3 99.2592592592593 -4.95925925925925 14 99.9 99.2592592592593 0.640740740740756 15 103.4 99.2592592592593 4.14074074074076 16 103.3 99.2592592592593 4.04074074074075 17 98.8 99.2592592592593 -0.459259259259252 18 104.2 99.2592592592593 4.94074074074075 19 91.2 99.2592592592593 -8.05925925925925 20 74.7 99.2592592592593 -24.5592592592592 21 108.5 99.2592592592593 9.24074074074075 22 114.5 99.2592592592593 15.2407407407408 23 96.9 99.2592592592593 -2.35925925925924 24 89.6 99.2592592592593 -9.65925925925925 25 97.1 99.2592592592593 -2.15925925925925 26 100.3 99.2592592592593 1.04074074074075 27 122.6 99.2592592592593 23.3407407407407 28 115.4 135.109090909091 -19.7090909090909 29 109 99.2592592592593 9.74074074074075 30 129.1 135.109090909091 -6.00909090909091 31 102.8 135.109090909091 -32.3090909090909 32 96.2 99.2592592592593 -3.05925925925925 33 127.7 135.109090909091 -7.4090909090909 34 128.9 135.109090909091 -6.2090909090909 35 126.5 135.109090909091 -8.6090909090909 36 119.8 135.109090909091 -15.3090909090909 37 113.2 135.109090909091 -21.9090909090909 38 114.1 135.109090909091 -21.0090909090909 39 134.1 135.109090909091 -1.00909090909091 40 130 135.109090909091 -5.10909090909091 41 121.8 135.109090909091 -13.3090909090909 42 132.1 135.109090909091 -3.00909090909091 43 105.3 135.109090909091 -29.8090909090909 44 103 135.109090909091 -32.1090909090909 45 117.1 135.109090909091 -18.0090909090909 46 126.3 135.109090909091 -8.80909090909091 47 138.1 135.109090909091 2.99090909090909 48 119.5 135.109090909091 -15.6090909090909 49 138 135.109090909091 2.89090909090909 50 135.5 135.109090909091 0.390909090909093 51 178.6 135.109090909091 43.4909090909091 52 162.2 135.109090909091 27.0909090909091 53 176.9 135.109090909091 41.7909090909091 54 204.9 135.109090909091 69.7909090909091 55 132.2 135.109090909091 -2.90909090909092 56 142.5 135.109090909091 7.3909090909091 57 164.3 135.109090909091 29.1909090909091 58 174.9 135.109090909091 39.7909090909091 59 175.4 135.109090909091 40.2909090909091 60 143 135.109090909091 7.8909090909091 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0212157183383253 0.0424314366766505 0.978784281661675 6 0.00463043035592989 0.00926086071185977 0.99536956964407 7 0.00356756895852439 0.00713513791704878 0.996432431041476 8 0.0168327360021052 0.0336654720042103 0.983167263997895 9 0.00926044591534966 0.0185208918306993 0.99073955408465 10 0.00704536352638705 0.0140907270527741 0.992954636473613 11 0.00272564432588103 0.00545128865176205 0.997274355674119 12 0.00171719403151068 0.00343438806302136 0.99828280596849 13 0.000661630560868409 0.00132326112173682 0.999338369439132 14 0.000237830173924156 0.000475660347848312 0.999762169826076 15 0.000100319731364251 0.000200639462728503 0.999899680268636 16 3.98737406644319e-05 7.97474813288638e-05 0.999960126259336 17 1.24637312911734e-05 2.49274625823468e-05 0.99998753626871 18 4.97631036943766e-06 9.9526207388753e-06 0.99999502368963 19 2.4149608240899e-06 4.8299216481798e-06 0.999997585039176 20 3.56999936634196e-05 7.13999873268391e-05 0.999964300006337 21 2.54029713049642e-05 5.08059426099285e-05 0.999974597028695 22 3.66880772847476e-05 7.33761545694951e-05 0.999963311922715 23 1.41757582407630e-05 2.83515164815260e-05 0.99998582424176 24 8.04762691530492e-06 1.60952538306098e-05 0.999991952373085 25 3.02589412741813e-06 6.05178825483627e-06 0.999996974105873 26 1.12016248814008e-06 2.24032497628016e-06 0.999998879837512 27 6.3056226042543e-06 1.26112452085086e-05 0.999993694377396 28 3.08533233028859e-06 6.17066466057718e-06 0.99999691466767 29 1.76826979363290e-06 3.53653958726579e-06 0.999998231730206 30 1.38509361697334e-06 2.77018723394668e-06 0.999998614906383 31 2.10936094175233e-06 4.21872188350465e-06 0.999997890639058 32 8.1536125595133e-07 1.63072251190266e-06 0.999999184638744 33 5.60805313767193e-07 1.12161062753439e-06 0.999999439194686 34 3.51102797801601e-07 7.02205595603203e-07 0.999999648897202 35 1.74734925876009e-07 3.49469851752017e-07 0.999999825265074 36 8.36707415132093e-08 1.67341483026419e-07 0.999999916329259 37 6.2571755246511e-08 1.25143510493022e-07 0.999999937428245 38 4.67437794878132e-08 9.34875589756264e-08 0.99999995325622 39 4.082416482672e-08 8.164832965344e-08 0.999999959175835 40 2.35942518815548e-08 4.71885037631096e-08 0.999999976405748 41 1.31436937804007e-08 2.62873875608015e-08 0.999999986856306 42 8.1863592872025e-09 1.6372718574405e-08 0.99999999181364 43 4.65693190991108e-08 9.31386381982216e-08 0.999999953430681 44 6.62774913037122e-07 1.32554982607424e-06 0.999999337225087 45 1.51332725201806e-06 3.02665450403611e-06 0.999998486672748 46 2.34997249280195e-06 4.69994498560389e-06 0.999997650027507 47 3.73520731697557e-06 7.47041463395114e-06 0.999996264792683 48 2.41645089835221e-05 4.83290179670442e-05 0.999975835491016 49 5.56369235716713e-05 0.000111273847143343 0.999944363076428 50 0.000184781916243367 0.000369563832486733 0.999815218083757 51 0.00477142644194001 0.00954285288388002 0.99522857355806 52 0.0060149908977144 0.0120299817954288 0.993985009102286 53 0.0133991383590779 0.0267982767181557 0.986600861640922 54 0.300971517264970 0.601943034529941 0.69902848273503 55 0.373103134975782 0.746206269951564 0.626896865024218 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 43 0.843137254901961 NOK 5% type I error level 49 0.96078431372549 NOK 10% type I error level 49 0.96078431372549 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/10rwkl1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/10rwkl1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/1i21y1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/1i21y1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/28aum1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/28aum1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/3w3ma1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/3w3ma1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/480kh1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/480kh1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/5fwkb1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/5fwkb1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/6ypmn1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/6ypmn1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/70xuz1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/70xuz1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/8ho0x1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/8ho0x1258566970.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/9oicp1258566970.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258567007qxxj14coqsqv9ox/9oicp1258566970.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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