Eerste meervoudige regressiemodel

*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: Tue, 23 Nov 2010 19:52:07 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh.htm/, Retrieved Tue, 23 Nov 2010 20:50:33 +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/2010/Nov/23/t1290541833dr5wgdvtxec53jh.htm/}, year = {2010}, } @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 = {2010}, 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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16198,9 16896,2 0 16554,2 16698 0 19554,2 19691,6 0 15903,8 15930,7 0 18003,8 17444,6 0 18329,6 17699,4 0 16260,7 15189,8 0 14851,9 15672,7 0 18174,1 17180,8 0 18406,6 17664,9 0 18466,5 17862,9 0 16016,5 16162,3 0 17428,5 17463,6 0 17167,2 16772,1 0 19630 19106,9 0 17183,6 16721,3 0 18344,7 18161,3 0 19301,4 18509,9 0 18147,5 17802,7 0 16192,9 16409,9 0 18374,4 17967,7 0 20515,2 20286,6 0 18957,2 19537,3 0 16471,5 18021,9 0 18746,8 20194,3 0 19009,5 19049,6 0 19211,2 20244,7 0 20547,7 21473,3 0 19325,8 19673,6 0 20605,5 21053,2 0 20056,9 20159,5 0 16141,4 18203,6 0 20359,8 21289,5 0 19711,6 20432,3 1 15638,6 17180,4 1 14384,5 15816,8 1 13855,6 15071,8 1 14308,3 14521,1 1 15290,6 15668,8 1 14423,8 14346,9 1 13779,7 13881 1 15686,3 15465,9 1 14733,8 14238,2 1 12522,5 13557,7 1 16189,4 16127,6 1 16059,1 16793,9 1 16007,1 16014 1 15806,8 16867,9 1 15160 16014,6 0 15692,1 15878,6 0 18908,9 18664,9 0 16969,9 17962,5 0 16997,5 17332,7 0 19858,9 19542,1 0 17681,2 etc...

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 12 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation uitvoer[t] = + 2113.94008826516 + 0.87631681502034invoer[t] -673.894099652128crisis[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) 2113.94008826516 1023.768921 2.0649 0.043939 0.021969 invoer 0.87631681502034 0.056187 15.5964 0 0 crisis -673.894099652128 245.819041 -2.7414 0.008367 0.004184 Multiple Linear Regression - Regression Statistics Multiple R 0.943748599824329 R-squared 0.890661419670381 Adjusted R-squared 0.886456089657704 F-TEST (value) 211.793466145425 F-TEST (DF numerator) 2 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 680.349569326576 Sum Squared Residuals 24069527.8971086 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 16198.9 16920.3642582118 -721.464258211822 2 16554.2 16746.6782654748 -192.478265474802 3 19554.2 19370.0202829197 184.179717080307 4 15903.8 16074.2803733097 -170.480373309698 5 18003.8 17400.936399569 602.863600431011 6 18329.6 17624.2219240362 705.378075963825 7 16260.7 15425.0172450611 835.682754938875 8 14851.9 15848.1906350344 -996.29063503445 9 18174.1 17169.7640237666 1004.33597623338 10 18406.6 17593.9889939180 812.611006082027 11 18466.5 17767.499723292 699.000276708 12 16016.5 16277.2353476684 -260.735347668407 13 17428.5 17417.5864190544 10.9135809456248 14 17167.2 16811.6133414678 355.586658532191 15 19630 18857.6378411773 772.362158822697 16 17183.6 16767.0964472648 416.503552735221 17 18344.7 18028.9926608941 315.707339105934 18 19301.4 18334.4767026102 966.923297389841 19 18147.5 17714.7454510278 432.754548972226 20 16192.9 16494.2113910674 -301.311391067445 21 18374.4 17859.3377255061 515.062274493871 22 20515.2 19891.4287878568 623.771212143204 23 18957.2 19234.8045983621 -277.604598362055 24 16471.5 17906.8340968802 -1435.33409688023 25 18746.8 19810.5447458304 -1063.74474583042 26 19009.5 18807.4248876766 202.075112323365 27 19211.2 19854.7111133074 -643.511113307445 28 20547.7 20931.3539522414 -383.653952241434 29 19325.8 19354.2465802493 -28.4465802493284 30 20605.5 20563.2132582514 42.2867417486085 31 20056.9 19780.0489206677 276.851079332289 32 16141.4 18066.0608621694 -1924.66086216943 33 20359.8 20770.2869216407 -410.486921640698 34 19711.6 19345.2140481531 366.385951846866 35 15638.6 16495.5193973885 -856.919397388489 36 14384.5 15300.5737884268 -916.073788426751 37 13855.6 14647.7177612366 -792.117761236598 38 14308.3 14165.1300912049 143.169908795102 39 15290.6 15170.8788998037 119.721100196260 40 14423.8 14012.4757020284 411.324297971646 41 13779.7 13604.1996979104 175.500302089624 42 15686.3 14993.0742180361 693.225781963885 43 14733.8 13917.2200642356 816.579935764356 44 12522.5 13320.8864716143 -798.386471614302 45 16189.4 15572.9330545351 616.466945464926 46 16059.1 16156.8229483831 -97.7229483831274 47 16007.1 15473.3834643488 533.716535651237 48 15806.8 16221.6703926946 -414.870392694634 49 15160 16147.8033540899 -987.803354089903 50 15692.1 16028.6242672471 -336.524267247137 51 18908.9 18470.3058089383 438.594191061689 52 16969.9 17854.7808780680 -884.880878068023 53 16997.5 17302.8765479682 -305.376547968215 54 19858.9 19239.0109190742 619.889080925849 55 17681.2 17189.7440471491 491.455952850914 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.512235793360664 0.975528413278672 0.487764206639336 7 0.587549005183318 0.824901989633363 0.412450994816682 8 0.708054266700753 0.583891466598493 0.291945733299247 9 0.755040102646188 0.489919794707624 0.244959897353812 10 0.723650761982486 0.552698476035028 0.276349238017514 11 0.663712673533466 0.672574652933069 0.336287326466534 12 0.580187316471073 0.839625367057853 0.419812683528927 13 0.488532343090435 0.97706468618087 0.511467656909565 14 0.404507739516898 0.809015479033797 0.595492260483102 15 0.349800106672512 0.699600213345023 0.650199893327488 16 0.289247961199047 0.578495922398093 0.710752038800953 17 0.225353447621683 0.450706895243367 0.774646552378317 18 0.239049383386556 0.478098766773113 0.760950616613444 19 0.195000378615763 0.390000757231525 0.804999621384237 20 0.156687017787507 0.313374035575014 0.843312982212493 21 0.133121321551605 0.266242643103211 0.866878678448395 22 0.122875368183478 0.245750736366957 0.877124631816522 23 0.152785478219056 0.305570956438112 0.847214521780944 24 0.48686811235452 0.97373622470904 0.51313188764548 25 0.657412408805518 0.685175182388963 0.342587591194482 26 0.597524969274574 0.804950061450852 0.402475030725426 27 0.588011049380148 0.823977901239703 0.411988950619852 28 0.527374212395278 0.945251575209443 0.472625787604722 29 0.448380437201945 0.896760874403889 0.551619562798055 30 0.371784222772703 0.743568445545406 0.628215777227297 31 0.323504324539428 0.647008649078855 0.676495675460572 32 0.775653398198049 0.448693203603903 0.224346601801951 33 0.731389231547832 0.537221536904336 0.268610768452168 34 0.657329170890061 0.685341658219879 0.342670829109939 35 0.741262459705623 0.517475080588755 0.258737540294378 36 0.813785533175404 0.372428933649192 0.186214466824596 37 0.851029221124117 0.297941557751765 0.148970778875883 38 0.802725120957756 0.394549758084487 0.197274879042244 39 0.741669845897481 0.516660308205038 0.258330154102519 40 0.694055203915248 0.611889592169505 0.305944796084752 41 0.616734818239378 0.766530363521244 0.383265181760622 42 0.599307962892157 0.801384074215685 0.400692037107843 43 0.757251169298324 0.485497661403351 0.242748830701676 44 0.689131377523516 0.621737244952968 0.310868622476484 45 0.678873770592512 0.642252458814976 0.321126229407488 46 0.567058966539006 0.865882066921988 0.432941033460994 47 0.63756199985283 0.72487600029434 0.36243800014717 48 0.490098675387734 0.980197350775468 0.509901324612266 49 0.417077809199559 0.834155618399119 0.58292219080044 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/10mm2d1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/10mm2d1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/1xl4k1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/1xl4k1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/2xl4k1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/2xl4k1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/38umn1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/38umn1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/48umn1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/48umn1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/58umn1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/58umn1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/61llq1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/61llq1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/71llq1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/71llq1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/8tdks1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/8tdks1290541912.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/9tdks1290541912.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t1290541833dr5wgdvtxec53jh/9tdks1290541912.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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