Regressievergelijking

*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 08:48:03 -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/t1258648181mz1serb8pxajvrq.htm/, Retrieved Thu, 19 Nov 2009 17:29:53 +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/t1258648181mz1serb8pxajvrq.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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96.8 9.3 114.1 9.3 110.3 8.7 103.9 8.2 101.6 8.3 94.6 8.5 95.9 8.6 104.7 8.5 102.8 8.2 98.1 8.1 113.9 7.9 80.9 8.6 95.7 8.7 113.2 8.7 105.9 8.5 108.8 8.4 102.3 8.5 99 8.7 100.7 8.7 115.5 8.6 100.7 8.5 109.9 8.3 114.6 8 85.4 8.2 100.5 8.1 114.8 8.1 116.5 8 112.9 7.9 102 7.9 106 8 105.3 8 118.8 7.9 106.1 8 109.3 7.7 117.2 7.2 92.5 7.5 104.2 7.3 112.5 7 122.4 7 113.3 7 100 7.2 110.7 7.3 112.8 7.1 109.8 6.8 117.3 6.4 109.1 6.1 115.9 6.5 96 7.7 99.8 7.9 116.8 7.5 115.7 6.9 99.4 6.6 94.3 6.9 91 7.7 93.2 8 103.1 8 94.1 7.7 91.8 7.3 102.7 7.4 82.6 8.1 89.1 8.3

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation tip[t] = + 134.323639607493 -3.80196253345228wrk[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) 134.323639607493 13.454379 9.9836 0 0 wrk -3.80196253345228 1.703128 -2.2323 0.029402 0.014701 Multiple Linear Regression - Regression Statistics Multiple R 0.279078880578303 R-squared 0.0778850215848387 Adjusted R-squared 0.0622559541540733 F-TEST (value) 4.98334413936457 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 0.0294016525122771 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.29271866130875 Sum Squared Residuals 5094.92258697592 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 96.8 98.9653880463874 -2.16538804638741 2 114.1 98.9653880463871 15.1346119536129 3 110.3 101.246565566459 9.05343443354149 4 103.9 103.147546833185 0.752453166815355 5 101.6 102.767350579839 -1.16735057983942 6 94.6 102.006958073149 -7.40695807314897 7 95.9 101.626761819804 -5.72676181980373 8 104.7 102.006958073149 2.69304192685104 9 102.8 103.147546833185 -0.347546833184654 10 98.1 103.527743086530 -5.42774308652988 11 113.9 104.288135593220 9.61186440677968 12 80.9 101.626761819804 -20.7267618198037 13 95.7 101.246565566459 -5.54656556645851 14 113.2 101.246565566459 11.9534344335415 15 105.9 102.006958073149 3.89304192685104 16 108.8 102.387154326494 6.4128456735058 17 102.3 102.006958073149 0.293041926851034 18 99 101.246565566459 -2.24656556645851 19 100.7 101.246565566459 -0.546565566458506 20 115.5 101.626761819804 13.8732381801963 21 100.7 102.006958073149 -1.30695807314896 22 109.9 102.767350579839 7.13264942016059 23 114.6 103.907939339875 10.6920606601249 24 85.4 103.147546833185 -17.7475468331846 25 100.5 103.527743086530 -3.02774308652988 26 114.8 103.527743086530 11.2722569134701 27 116.5 103.907939339875 12.5920606601249 28 112.9 104.288135593220 8.61186440677968 29 102 104.288135593220 -2.28813559322033 30 106 103.907939339875 2.09206066012489 31 105.3 103.907939339875 1.39206066012489 32 118.8 104.288135593220 14.5118644067797 33 106.1 103.907939339875 2.19206066012489 34 109.3 105.048528099911 4.25147190008921 35 117.2 106.949509366637 10.2504906333631 36 92.5 105.808920606601 -13.3089206066012 37 104.2 106.569313113292 -2.3693131132917 38 112.5 107.709901873327 4.79009812667261 39 122.4 107.709901873327 14.6900981266726 40 113.3 107.709901873327 5.59009812667261 41 100 106.949509366637 -6.94950936663693 42 110.7 106.569313113292 4.1306868867083 43 112.8 107.329705619982 5.47029438001783 44 109.8 108.470294380018 1.32970561998215 45 117.3 109.991079393399 7.30892060660124 46 109.1 111.131668153434 -2.03166815343445 47 115.9 109.610883140054 6.28911685994647 48 96 105.048528099911 -9.04852809991079 49 99.8 104.288135593220 -4.48813559322034 50 116.8 105.808920606601 10.9910793933987 51 115.7 108.090098126673 7.60990187332738 52 99.4 109.230686886708 -9.8306868867083 53 94.3 108.090098126673 -13.7900981266726 54 91 105.048528099911 -14.0485280999108 55 93.2 103.907939339875 -10.7079393398751 56 103.1 103.907939339875 -0.807939339875111 57 94.1 105.048528099911 -10.9485280999108 58 91.8 106.569313113292 -14.7693131132917 59 102.7 106.189116859946 -3.48911685994647 60 82.6 103.527743086530 -20.9277430865299 61 89.1 102.767350579839 -13.6673505798394 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.44637671884651 0.89275343769302 0.55362328115349 6 0.440930423996786 0.881860847993573 0.559069576003214 7 0.369068946142979 0.738137892285957 0.630931053857021 8 0.258219794598997 0.516439589197994 0.741780205401003 9 0.166438750090919 0.332877500181838 0.83356124990908 10 0.105638841894411 0.211277683788823 0.894361158105589 11 0.183548439689168 0.367096879378335 0.816451560310832 12 0.598873587462616 0.802252825074768 0.401126412537384 13 0.532349629031744 0.935300741936512 0.467650370968256 14 0.581518178181024 0.836963643637952 0.418481821818976 15 0.505532091045061 0.988935817909879 0.494467908954939 16 0.457995125387597 0.915990250775193 0.542004874612403 17 0.372136238747087 0.744272477494173 0.627863761252913 18 0.300518961569059 0.601037923138119 0.69948103843094 19 0.231558695748635 0.463117391497271 0.768441304251365 20 0.31695815548221 0.63391631096442 0.68304184451779 21 0.250436418085414 0.500872836170827 0.749563581914586 22 0.230876879150587 0.461753758301173 0.769123120849413 23 0.257795359064529 0.515590718129058 0.74220464093547 24 0.440828020010704 0.881656040021408 0.559171979989296 25 0.36982175574837 0.73964351149674 0.63017824425163 26 0.421903653927499 0.843807307854999 0.578096346072501 27 0.503825451041737 0.992349097916525 0.496174548958263 28 0.507662845404137 0.984674309191727 0.492337154595863 29 0.443554901741454 0.887109803482908 0.556445098258546 30 0.38882419602763 0.77764839205526 0.61117580397237 31 0.33588247808268 0.67176495616536 0.66411752191732 32 0.515529769997916 0.968940460004168 0.484470230002084 33 0.491090085153163 0.982180170306326 0.508909914846837 34 0.473875700508855 0.94775140101771 0.526124299491145 35 0.501637609220238 0.996724781559524 0.498362390779762 36 0.591481896572551 0.817036206854899 0.408518103427449 37 0.524378183565774 0.951243632868452 0.475621816434226 38 0.467688239023924 0.935376478047847 0.532311760976076 39 0.60989163567514 0.78021672864972 0.39010836432486 40 0.574710950662962 0.850578098674076 0.425289049337038 41 0.542677105098204 0.914645789803592 0.457322894901796 42 0.518318245671977 0.963363508656046 0.481681754328023 43 0.502133782121457 0.995732435757085 0.497866217878543 44 0.427851725365687 0.855703450731375 0.572148274634313 45 0.391320379440037 0.782640758880073 0.608679620559963 46 0.331224963947428 0.662449927894856 0.668775036052572 47 0.301571905702305 0.603143811404609 0.698428094297695 48 0.255914722599593 0.511829445199185 0.744085277400407 49 0.208089369186579 0.416178738373158 0.79191063081342 50 0.513648856031364 0.972702287937273 0.486351143968636 51 0.804418446973242 0.391163106053516 0.195581553026758 52 0.737276554405909 0.525446891188182 0.262723445594091 53 0.691687224458461 0.616625551083078 0.308312775541539 54 0.618868466948487 0.762263066103026 0.381131533051513 55 0.488244339520186 0.976488679040371 0.511755660479814 56 0.672357220797984 0.655285558404031 0.327642779202016 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/2009/Nov/19/t1258648181mz1serb8pxajvrq/10759r1258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/10759r1258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/1ji001258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/1ji001258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/2pgr11258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/2pgr11258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/320mj1258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/320mj1258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/4o96z1258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/4o96z1258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/59nmw1258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/59nmw1258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/64l081258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/64l081258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/7z69q1258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/7z69q1258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/8zxkq1258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/8zxkq1258645678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/9w8791258645678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258648181mz1serb8pxajvrq/9w8791258645678.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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