seizoenaliteit

*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 08:58:14 -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/19/t1258646521g0yyan4r4kekei8.htm/, Retrieved Thu, 19 Nov 2009 17:02:13 +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/t1258646521g0yyan4r4kekei8.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:

sdws7

Dataseries X:

» Textbox « » Textfile « » CSV «

593530.00 0 610943.00 0 612613.00 0 611324.00 0 594167.00 0 595454.00 0 590865.00 0 589379.00 0 584428.00 0 573100.00 0 567456.00 0 569028.00 0 620735.00 0 628884.00 0 628232.00 0 612117.00 0 595404.00 0 597141.00 0 593408.00 0 590072.00 0 579799.00 0 574205.00 0 572775.00 0 572942.00 0 619567.00 0 625809.00 0 619916.00 0 587625.00 0 565742.00 0 557274.00 0 560576.00 0 548854.00 0 531673.00 0 525919.00 0 511038.00 0 498662.00 0 555362.00 0 564591.00 0 541657.00 0 527070.00 0 509846.00 0 514258.00 0 516922.00 0 507561.00 0 492622.00 0 490243.00 0 469357.00 0 477580.00 0 528379.00 1 533590.00 1 517945.00 1 506174.00 1 501866.00 1 516141.00 1 528222.00 1 532638.00 1 536322.00 1 536535.00 1 523597.00 1 536214.00 1 586570.00 1 596594.00 1 580523.00 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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation werklozen[t] = + 537942.741666667 -35287.7083333333`crisis `[t] + 57843.661111111M1[t] + 67221.661111111M2[t] + 57300.8277777778M3[t] + 37976.8M4[t] + 22519.8M5[t] + 25168.4000000000M6[t] + 27113.4M7[t] + 22815.6M8[t] + 14083.6M9[t] + 9115.19999999999M10[t] -2040.60000000001M11[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) 537942.741666667 16835.720074 31.9525 0 0 `crisis ` -35287.7083333333 11149.727996 -3.1649 0.002641 0.00132 M1 57843.661111111 22643.67963 2.5545 0.013726 0.006863 M2 67221.661111111 22643.67963 2.9687 0.004582 0.002291 M3 57300.8277777778 22643.67963 2.5305 0.014583 0.007291 M4 37976.8 23599.525971 1.6092 0.113865 0.056933 M5 22519.8 23599.525971 0.9542 0.344549 0.172274 M6 25168.4000000000 23599.525971 1.0665 0.29133 0.145665 M7 27113.4 23599.525971 1.1489 0.256065 0.128033 M8 22815.6 23599.525971 0.9668 0.338306 0.169153 M9 14083.6 23599.525971 0.5968 0.553351 0.276675 M10 9115.19999999999 23599.525971 0.3862 0.700954 0.350477 M11 -2040.60000000001 23599.525971 -0.0865 0.93144 0.46572 Multiple Linear Regression - Regression Statistics Multiple R 0.606516795618305 R-squared 0.367862623367097 Adjusted R-squared 0.216149652975200 F-TEST (value) 2.42472757877493 F-TEST (DF numerator) 12 F-TEST (DF denominator) 50 p-value 0.0144044869082498 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 37314.1268849696 Sum Squared Residuals 69617203259.3806 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 593530 595786.402777778 -2256.40277777821 2 610943 605164.402777778 5778.59722222212 3 612613 595243.569444444 17369.4305555556 4 611324 575919.541666667 35404.4583333334 5 594167 560462.541666667 33704.4583333332 6 595454 563111.141666667 32342.8583333334 7 590865 565056.141666667 25808.8583333333 8 589379 560758.341666667 28620.6583333334 9 584428 552026.341666667 32401.6583333333 10 573100 547057.941666667 26042.0583333333 11 567456 535902.141666667 31553.8583333333 12 569028 537942.741666667 31085.2583333333 13 620735 595786.402777778 24948.5972222223 14 628884 605164.402777778 23719.5972222222 15 628232 595243.569444444 32988.4305555555 16 612117 575919.541666667 36197.4583333333 17 595404 560462.541666667 34941.4583333333 18 597141 563111.141666667 34029.8583333333 19 593408 565056.141666667 28351.8583333334 20 590072 560758.341666667 29313.6583333333 21 579799 552026.341666667 27772.6583333333 22 574205 547057.941666667 27147.0583333333 23 572775 535902.141666667 36872.8583333333 24 572942 537942.741666667 34999.2583333333 25 619567 595786.402777778 23780.5972222223 26 625809 605164.402777778 20644.5972222222 27 619916 595243.569444444 24672.4305555556 28 587625 575919.541666667 11705.4583333333 29 565742 560462.541666667 5279.45833333335 30 557274 563111.141666667 -5837.14166666667 31 560576 565056.141666667 -4480.14166666665 32 548854 560758.341666667 -11904.3416666667 33 531673 552026.341666667 -20353.3416666667 34 525919 547057.941666667 -21138.9416666666 35 511038 535902.141666667 -24864.1416666666 36 498662 537942.741666667 -39280.7416666667 37 555362 595786.402777778 -40424.4027777777 38 564591 605164.402777778 -40573.4027777778 39 541657 595243.569444444 -53586.5694444445 40 527070 575919.541666667 -48849.5416666667 41 509846 560462.541666667 -50616.5416666667 42 514258 563111.141666667 -48853.1416666667 43 516922 565056.141666667 -48134.1416666667 44 507561 560758.341666667 -53197.3416666667 45 492622 552026.341666667 -59404.3416666667 46 490243 547057.941666667 -56814.9416666667 47 469357 535902.141666667 -66545.1416666666 48 477580 537942.741666667 -60362.7416666667 49 528379 560498.694444444 -32119.6944444444 50 533590 569876.694444444 -36286.6944444444 51 517945 559955.861111111 -42010.8611111111 52 506174 540631.833333333 -34457.8333333334 53 501866 525174.833333333 -23308.8333333333 54 516141 527823.433333333 -11682.4333333333 55 528222 529768.433333333 -1546.43333333333 56 532638 525470.633333333 7167.36666666665 57 536322 516738.633333333 19583.3666666667 58 536535 511770.233333333 24764.7666666667 59 523597 500614.433333333 22982.5666666667 60 536214 502655.033333333 33558.9666666666 61 586570 560498.694444444 26071.3055555556 62 596594 569876.694444444 26717.3055555556 63 580523 559955.861111111 20567.1388888889 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0685681118892025 0.137136223778405 0.931431888110797 17 0.0229824887208870 0.0459649774417741 0.977017511279113 18 0.00736078994254593 0.0147215798850919 0.992639210057454 19 0.00220389526553755 0.0044077905310751 0.997796104734462 20 0.000643588301938161 0.00128717660387632 0.999356411698062 21 0.000196421537445566 0.000392843074891131 0.999803578462554 22 5.56798621172208e-05 0.000111359724234442 0.999944320137883 23 2.10920746184059e-05 4.21841492368118e-05 0.999978907925382 24 8.16703759788837e-06 1.63340751957767e-05 0.999991832962402 25 6.25467735505072e-06 1.25093547101014e-05 0.999993745322645 26 3.04923855261589e-06 6.09847710523178e-06 0.999996950761447 27 2.21445703592181e-06 4.42891407184362e-06 0.999997785542964 28 2.46412173155447e-05 4.92824346310894e-05 0.999975358782685 29 0.000283189465194537 0.000566378930389074 0.999716810534806 30 0.00335168829482757 0.00670337658965515 0.996648311705172 31 0.00936905885782719 0.0187381177156544 0.990630941142173 32 0.0298439700232937 0.0596879400465874 0.970156029976706 33 0.0825729698478494 0.165145939695699 0.91742703015215 34 0.133487671305284 0.266975342610568 0.866512328694716 35 0.236096885825764 0.472193771651528 0.763903114174236 36 0.367324275410829 0.734648550821658 0.632675724589171 37 0.413043201295349 0.826086402590699 0.586956798704651 38 0.450387027158599 0.900774054317199 0.549612972841401 39 0.535029596521353 0.929940806957294 0.464970403478647 40 0.652038662749737 0.695922674500525 0.347961337250263 41 0.707457698867417 0.585084602265167 0.292542301132583 42 0.718002308440914 0.563995383118172 0.281997691559086 43 0.69832813137547 0.60334373724906 0.30167186862453 44 0.648950630573027 0.702098738853947 0.351049369426973 45 0.563583781845038 0.872832436309925 0.436416218154962 46 0.450894062263305 0.90178812452661 0.549105937736695 47 0.332562280699572 0.665124561399144 0.667437719300428 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.375 NOK 5% type I error level 15 0.46875 NOK 10% type I error level 16 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/10xm0z1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/10xm0z1258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/1te141258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/1te141258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/2q5vj1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/2q5vj1258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/3nvpm1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/3nvpm1258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/40k1c1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/40k1c1258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/5ifsm1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/5ifsm1258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/6ydbo1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/6ydbo1258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/7ak711258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/7ak711258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/8ah571258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/8ah571258646290.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/90vff1258646290.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646521g0yyan4r4kekei8/90vff1258646290.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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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