Q3 Reeks 1, trend season

*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, 24 Nov 2008 14:23:20 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8.htm/, Retrieved Mon, 24 Nov 2008 21:24:14 +0000

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/2008/Nov/24/t1227561854lq2i4c2cx21qwh8.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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User-defined keywords:

Dataseries X:

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577992 0 565464 0 547344 0 554788 0 562325 0 560854 0 555332 1 543599 1 536662 1 542722 1 593530 1 610763 1 612613 1 611324 1 594167 1 595454 1 590865 1 589379 1 584428 1 573100 1 567456 1 569028 1 620735 1 628884 1 628232 1 612117 1 595404 1 597141 1 593408 1 590072 1 579799 1 574205 1 572775 1 572942 1 619567 1 625809 1 619916 1 587625 1 565742 1 557274 1 560576 1 548854 1 531673 1 525919 1 511038 1 498662 1 555362 1 564591 1 541657 1 527070 1 509846 1 514258 1 516922 1 507561 1 492622 1 490243 1 469357 1 477580 1 528379 1 533590 1 517945 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Werkloosheid[t] = + 592485.965217391 + 66679.4826086956Aanslag[t] -7782.49287439609M1[t] -17126.516763285M2[t] -33500.4154347826M3[t] -30372.5141062802M4[t] -27490.8127777778M5[t] -31120.5114492753M6[t] -53184.1066425121M7[t] -58696.2053140097M8[t] -66806.3039855072M9[t] -64231.6026570048M10[t] -11058.3013285024M11[t] -1845.50132850242t + 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) 592485.965217391 14835.368146 39.9374 0 0 Aanslag 66679.4826086956 12217.279112 5.4578 2e-06 1e-06 M1 -7782.49287439609 14161.604543 -0.5495 0.585231 0.292615 M2 -17126.516763285 14846.798608 -1.1535 0.254518 0.127259 M3 -33500.4154347826 14838.080482 -2.2577 0.028648 0.014324 M4 -30372.5141062802 14832.006718 -2.0478 0.046194 0.023097 M5 -27490.8127777778 14828.580566 -1.8539 0.070034 0.035017 M6 -31120.5114492753 14827.803862 -2.0988 0.041234 0.020617 M7 -53184.1066425121 14710.12455 -3.6155 0.000729 0.000364 M8 -58696.2053140097 14698.099226 -3.9935 0.000227 0.000114 M9 -66806.3039855072 14688.73939 -4.5481 3.8e-05 1.9e-05 M10 -64231.6026570048 14682.05014 -4.3748 6.7e-05 3.4e-05 M11 -11058.3013285024 14678.035127 -0.7534 0.454973 0.227486 t -1845.50132850242 198.226417 -9.3101 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.858188825447399 R-squared 0.736488060122786 Adjusted R-squared 0.663601778880152 F-TEST (value) 10.1046184215527 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.2047184361208e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 23205.8948040985 Sum Squared Residuals 25310137021.9675 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 577992 582857.971014493 -4865.97101449256 2 565464 571668.445797101 -6204.44579710151 3 547344 553449.045797101 -6105.04579710146 4 554788 554731.445797101 56.5542028984994 5 562325 555767.645797102 6557.35420289851 6 560854 550292.445797101 10561.5542028985 7 555332 593062.831884058 -37730.831884058 8 543599 585705.231884058 -42106.231884058 9 536662 575749.631884058 -39087.631884058 10 542722 576478.831884058 -33756.831884058 11 593530 627806.631884058 -34276.631884058 12 610763 637019.431884058 -26256.431884058 13 612613 627391.43768116 -14778.4376811595 14 611324 616201.912463768 -4877.91246376814 15 594167 597982.512463768 -3815.5124637681 16 595454 599264.912463768 -3810.91246376811 17 590865 600301.112463768 -9436.1124637681 18 589379 594825.912463768 -5446.91246376812 19 584428 570916.815942029 13511.1840579710 20 573100 563559.215942029 9540.784057971 21 567456 553603.615942029 13852.384057971 22 569028 554332.815942029 14695.1840579710 23 620735 605660.615942029 15074.3840579710 24 628884 614873.415942029 14010.584057971 25 628232 605245.42173913 22986.5782608695 26 612117 594055.896521739 18061.1034782609 27 595404 575836.496521739 19567.5034782609 28 597141 577118.896521739 20022.1034782609 29 593408 578155.096521739 15252.9034782609 30 590072 572679.896521739 17392.1034782609 31 579799 548770.8 31028.2 32 574205 541413.2 32791.8 33 572775 531457.6 41317.4 34 572942 532186.8 40755.2 35 619567 583514.6 36052.4 36 625809 592727.4 33081.6 37 619916 583099.405797101 36816.5942028985 38 587625 571909.88057971 15715.1194202899 39 565742 553690.48057971 12051.5194202899 40 557274 554972.88057971 2301.11942028987 41 560576 556009.08057971 4566.91942028986 42 548854 550533.88057971 -1679.88057971014 43 531673 526624.784057971 5048.21594202899 44 525919 519267.184057971 6651.81594202899 45 511038 509311.584057971 1726.41594202900 46 498662 510040.784057971 -11378.784057971 47 555362 561368.584057971 -6006.58405797102 48 564591 570581.384057971 -5990.384057971 49 541657 560953.389855072 -19296.3898550725 50 527070 549763.864637681 -22693.8646376811 51 509846 531544.464637681 -21698.4646376812 52 514258 532826.864637681 -18568.8646376811 53 516922 533863.064637681 -16941.0646376811 54 507561 528387.864637681 -20826.8646376811 55 492622 504478.768115942 -11856.7681159420 56 490243 497121.168115942 -6878.16811594205 57 469357 487165.568115942 -17808.5681159420 58 477580 487894.768115942 -10314.7681159420 59 528379 539222.568115942 -10843.5681159420 60 533590 548435.368115942 -14845.3681159420 61 517945 538807.373913044 -20862.3739130436 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0884289181600745 0.176857836320149 0.911571081839926 18 0.0638910729838196 0.127782145967639 0.93610892701618 19 0.0269341683905266 0.0538683367810531 0.973065831609473 20 0.0134388511827505 0.026877702365501 0.98656114881725 21 0.00664524959419077 0.0132904991883815 0.99335475040581 22 0.00381502090983684 0.00763004181967367 0.996184979090163 23 0.00262244011025924 0.00524488022051849 0.99737755988974 24 0.00655099946271808 0.0131019989254362 0.993449000537282 25 0.0163973311310685 0.0327946622621371 0.983602668868931 26 0.0647989551401772 0.129597910280354 0.935201044859823 27 0.0901324383068445 0.180264876613689 0.909867561693155 28 0.111376613959147 0.222753227918294 0.888623386040853 29 0.186731069051991 0.373462138103982 0.81326893094801 30 0.232785570016836 0.465571140033672 0.767214429983164 31 0.204427326393037 0.408854652786073 0.795572673606963 32 0.148873571039324 0.297747142078648 0.851126428960676 33 0.117750525130888 0.235501050261776 0.882249474869112 34 0.110074099191653 0.220148198383307 0.889925900808347 35 0.0904051808668525 0.180810361733705 0.909594819133147 36 0.0912029675961165 0.182405935192233 0.908797032403884 37 0.371689550058793 0.743379100117586 0.628310449941207 38 0.816869590287019 0.366260819425962 0.183130409712981 39 0.96358315739484 0.0728336852103212 0.0364168426051606 40 0.979415988762026 0.0411680224759482 0.0205840112379741 41 0.982258116996804 0.0354837660063926 0.0177418830031963 42 0.981562737879002 0.0368745242419963 0.0184372621209981 43 0.97186962298208 0.0562607540358408 0.0281303770179204 44 0.935924514812435 0.128150970375129 0.0640754851875646 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0714285714285714 NOK 5% type I error level 9 0.321428571428571 NOK 10% type I error level 12 0.428571428571429 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/10ykvh1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/10ykvh1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/18dld1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/18dld1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/2scqq1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/2scqq1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/38r6b1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/38r6b1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/4z39z1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/4z39z1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/5hjy61227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/5hjy61227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/6xopr1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/6xopr1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/7tbax1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/7tbax1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/80ezb1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/80ezb1227561789.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/9ke1s1227561789.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227561854lq2i4c2cx21qwh8/9ke1s1227561789.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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