Regressiemodel - include monthly dummies - linear trend

*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: Thu, 19 Nov 2009 10:13:05 -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/19/t1258650905n94of1j3k21ghql.htm/, Retrieved Thu, 19 Nov 2009 18:15:18 +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/19/t1258650905n94of1j3k21ghql.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:

Uitleg in Word document

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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96.96 89.1 93.11 83.3 95.62 97.7 98.30 100.9 96.38 108.3 100.82 113.2 99.06 105 94.03 104 102.07 109.8 99.31 98.6 98.64 93.5 101.82 98.2 99.14 88 97.63 85.3 100.06 96.8 101.32 98.8 101.49 110.3 105.43 111.6 105.09 111.2 99.48 106.9 108.53 117.6 104.34 97 106.10 97.3 107.35 98.4 103.00 87.6 104.50 87.4 105.17 94.7 104.84 101.5 106.18 110.4 108.86 108.4 107.77 109.7 102.74 105.2 112.63 111.1 106.26 96.2 108.86 97.3 111.38 98.9 106.85 91.7 107.86 90.9 107.94 98.8 111.38 111.5 111.29 119 113.72 115.3 111.88 116.3 109.87 113.6 113.72 115.1 111.71 109.7 114.81 97.6 112.05 100.8 111.54 94 110.87 87.2 110.87 102.9 115.48 111.3 111.63 106.6 116.24 108.9 113.56 108.3 106.01 100.5 110.45 104 107.77 89.9 108.61 86.8 108.19 91.2

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 BESTC[t] = + 56.7055652840370 + 0.427784443400585INDUSTR[t] + 1.49132630137853M1[t] + 1.91125215674213M2[t] -2.08103055041091M3[t] -2.85161499584517M4[t] -6.61030721957914M5[t] -3.50051793800585M6[t] -4.72282683623544M7[t] -8.30267342615144M8[t] -3.86358360610904M9[t] -2.07236900560768M10[t] + 0.800004760324151M11[t] + 0.270651430122387t + 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) 56.7055652840370 5.026098 11.2822 0 0 INDUSTR 0.427784443400585 0.051363 8.3287 0 0 M1 1.49132630137853 1.087123 1.3718 0.176777 0.088388 M2 1.91125215674213 1.153488 1.6569 0.104339 0.05217 M3 -2.08103055041091 1.022239 -2.0358 0.047558 0.023779 M4 -2.85161499584517 1.090707 -2.6145 0.012041 0.006021 M5 -6.61030721957914 1.235823 -5.3489 3e-06 1e-06 M6 -3.50051793800585 1.250703 -2.7988 0.007469 0.003735 M7 -4.72282683623544 1.209414 -3.9051 0.000306 0.000153 M8 -8.30267342615144 1.109301 -7.4846 0 0 M9 -3.86358360610904 1.24773 -3.0965 0.003331 0.001666 M10 -2.07236900560768 1.016378 -2.039 0.047223 0.023611 M11 0.800004760324151 1.026656 0.7792 0.439833 0.219916 t 0.270651430122387 0.012298 22.0072 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.969917333827114 R-squared 0.940739634458297 Adjusted R-squared 0.923992139848686 F-TEST (value) 56.1719622180619 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.60513216920770 Sum Squared Residuals 118.516666908769 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.96 96.5831369225304 0.376863077469554 2 93.11 94.7925644362927 -1.68256443629265 3 95.62 97.2310291442304 -1.61102914423043 4 98.3 98.1000063478004 0.199993652199554 5 96.38 97.7775704353532 -1.39757043535318 6 100.82 103.254154919712 -2.43415491971174 7 99.06 98.7946650157197 0.265334984280270 8 94.03 95.0576854125255 -1.02768541252553 9 102.07 102.248576434414 -0.178576434413718 10 99.31 99.519256698951 -0.20925669895091 11 98.64 100.480581233662 -1.84058123366214 12 101.82 101.961814787443 -0.141814787443135 13 99.14 99.360391196258 -0.220391196258066 14 97.63 98.8959504845625 -1.26595048456249 15 100.06 100.093840306639 -0.0338403066385519 16 101.32 100.449476178128 0.870523821872151 17 101.49 101.880956483623 -0.390956483623005 18 105.43 105.817516971739 -0.387516971739418 19 105.09 104.694745726272 0.395254273727996 20 99.48 99.5460774598559 -0.0660774598558651 21 108.53 108.833112254407 -0.303112254406920 22 104.34 102.082618750979 2.25738124902139 23 106.1 105.353979280053 0.746020719946994 24 107.35 105.295188837592 2.05481116240811 25 103 102.437094580366 0.562905419633528 26 104.5 103.042114977172 1.45788502282766 27 105.17 102.443310136966 2.72668986303404 28 104.84 104.852311336778 -0.0123113367780628 29 106.18 105.171552089432 1.00844791056831 30 108.86 107.696423914326 1.16357608567380 31 107.77 107.300886222640 0.469113777360228 32 102.74 102.066661067544 0.673338932456485 33 112.63 109.300330533772 3.32966946622824 34 106.26 104.988208357727 1.27179164227322 35 108.86 108.601796441522 0.258203558478359 36 111.38 108.756898220761 2.62310177923918 37 106.85 107.438827959778 -0.588827959777521 38 107.86 107.787177690543 0.07282230945697 39 107.94 107.445043516377 0.494956483622992 40 111.38 112.377972932253 -0.997972932252565 41 111.29 112.098315464145 -0.808315464145366 42 113.72 113.895953735259 -0.175953735258875 43 111.88 113.372080710552 -1.49208071055227 44 109.87 108.907867553577 0.962132446422937 45 113.72 114.259285468843 -0.539285468842736 46 111.71 114.011115505103 -2.30111550510333 47 114.81 111.977948936010 2.83205106398955 48 112.05 112.817505824691 -0.767505824690569 49 111.54 111.670549341067 -0.130549341067496 50 110.87 109.452192411429 1.41780758857051 51 110.87 112.446776895788 -1.57677689578804 52 115.48 115.540233205041 -0.0602332050410774 53 111.63 110.041605527447 1.58839447255325 54 116.24 114.405950458964 1.83404954103623 55 113.56 113.197622324816 0.362377675183779 56 106.01 106.551708506498 -0.541708506498028 57 110.45 112.758695308565 -2.30869530856487 58 107.77 108.788800687240 -1.01880068724038 59 108.61 110.605694108753 -1.99569410875277 60 108.19 111.958592329514 -3.76859232951359 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.086903040618802 0.173806081237604 0.913096959381198 18 0.0783426657105238 0.156685331421048 0.921657334289476 19 0.0310303708243889 0.0620607416487778 0.968969629175611 20 0.0136483702171386 0.0272967404342771 0.986351629782861 21 0.00553947671003839 0.0110789534200768 0.994460523289962 22 0.00574633152535474 0.0114926630507095 0.994253668474645 23 0.0126075413582426 0.0252150827164852 0.987392458641757 24 0.00782769197854296 0.0156553839570859 0.992172308021457 25 0.00736178703763536 0.0147235740752707 0.992638212962365 26 0.00760122678988074 0.0152024535797615 0.99239877321012 27 0.00785827633650294 0.0157165526730059 0.992141723663497 28 0.0167105456013864 0.0334210912027728 0.983289454398614 29 0.0089307938498318 0.0178615876996636 0.991069206150168 30 0.00504986686967954 0.0100997337393591 0.99495013313032 31 0.00427320558636513 0.00854641117273026 0.995726794413635 32 0.00267230016723802 0.00534460033447604 0.997327699832762 33 0.00400907605311883 0.00801815210623766 0.995990923946881 34 0.00402263439509221 0.00804526879018443 0.995977365604908 35 0.00243966553671552 0.00487933107343105 0.997560334463284 36 0.00590304348819993 0.0118060869763999 0.9940969565118 37 0.00866794236242795 0.0173358847248559 0.991332057637572 38 0.00564658399792617 0.0112931679958523 0.994353416002074 39 0.00457550488940345 0.0091510097788069 0.995424495110597 40 0.0024101380794434 0.0048202761588868 0.997589861920557 41 0.00299213052534913 0.00598426105069827 0.99700786947465 42 0.00773885583675592 0.0154777116735118 0.992261144163244 43 0.437290348364574 0.874580696729149 0.562709651635426 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.296296296296296 NOK 5% type I error level 23 0.851851851851852 NOK 10% type I error level 24 0.888888888888889 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/107d061258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/107d061258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/1e4571258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/1e4571258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/2dziu1258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/2dziu1258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/3fjwe1258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/3fjwe1258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/405711258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/405711258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/5bda61258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/5bda61258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/6k8bf1258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/6k8bf1258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/7aja21258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/7aja21258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/8cwv71258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/8cwv71258650780.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/9byhj1258650780.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258650905n94of1j3k21ghql/9byhj1258650780.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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