ws 7 3

*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: Fri, 20 Nov 2009 10:02:43 -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/20/t1258736793bvm1gs0ma10e9yt.htm/, Retrieved Fri, 20 Nov 2009 18:06:45 +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/20/t1258736793bvm1gs0ma10e9yt.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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29.837 0 29.571 0 30.167 0 30.524 0 30.996 0 31.033 0 31.198 0 30.937 0 31.649 0 33.115 0 34.106 0 33.926 0 33.382 0 32.851 0 32.948 0 36.112 0 36.113 0 35.210 0 35.193 0 34.383 0 35.349 0 37.058 0 38.076 0 36.630 0 36.045 0 35.638 0 35.114 0 35.465 0 35.254 0 35.299 0 35.916 0 36.683 0 37.288 0 38.536 0 38.977 0 36.407 0 34.955 0 34.951 0 32.680 0 34.791 0 34.178 0 35.213 0 34.871 0 35.299 0 35.443 0 37.108 0 36.419 0 34.471 0 33.868 0 34.385 0 33.643 1 34.627 1 32.919 1 35.500 1 36.110 1 37.086 1 37.711 1 40.427 1 39.884 1 38.512 1 38.767 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 Multiple Linear Regression - Estimated Regression Equation saldo_zichtrek[t] = + 35.5499652830189 + 2.19617358490566crisis[t] -1.44032754716980M1[t] -2.07076528301887M2[t] -3.07880000000001M3[t] -1.6854M4[t] -2.09720000000000M5[t] -1.53820000000000M6[t] -1.33160000000000M7[t] -1.11160000000000M8[t] -0.501200000000004M9[t] + 1.25960000000000M10[t] + 1.50320000000000M11[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) 35.5499652830189 0.955589 37.2021 0 0 crisis 2.19617358490566 0.710939 3.0891 0.003334 0.001667 M1 -1.44032754716980 1.27969 -1.1255 0.265962 0.132981 M2 -2.07076528301887 1.343906 -1.5409 0.129919 0.06496 M3 -3.07880000000001 1.336363 -2.3039 0.025603 0.012802 M4 -1.6854 1.336363 -1.2612 0.213338 0.106669 M5 -2.09720000000000 1.336363 -1.5693 0.12314 0.06157 M6 -1.53820000000000 1.336363 -1.151 0.25542 0.12771 M7 -1.33160000000000 1.336363 -0.9964 0.324035 0.162018 M8 -1.11160000000000 1.336363 -0.8318 0.409636 0.204818 M9 -0.501200000000004 1.336363 -0.375 0.709277 0.354639 M10 1.25960000000000 1.336363 0.9426 0.350627 0.175314 M11 1.50320000000000 1.336363 1.1248 0.266249 0.133124 Multiple Linear Regression - Regression Statistics Multiple R 0.642327038651281 R-squared 0.412584024582524 Adjusted R-squared 0.265730030728155 F-TEST (value) 2.80948453463018 F-TEST (DF numerator) 12 F-TEST (DF denominator) 48 p-value 0.00549319063690001 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.11297592601036 Sum Squared Residuals 214.304028667169 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 29.837 34.109637735849 -4.272637735849 2 29.571 33.4792 -3.90819999999999 3 30.167 32.4711652830189 -2.30416528301887 4 30.524 33.8645652830189 -3.34056528301887 5 30.996 33.4527652830189 -2.45676528301886 6 31.033 34.0117652830189 -2.97876528301887 7 31.198 34.2183652830189 -3.02036528301887 8 30.937 34.4383652830189 -3.50136528301887 9 31.649 35.0487652830189 -3.39976528301887 10 33.115 36.8095652830189 -3.69456528301887 11 34.106 37.0531652830189 -2.94716528301887 12 33.926 35.5499652830189 -1.62396528301887 13 33.382 34.1096377358491 -0.72763773584907 14 32.851 33.4792 -0.6282 15 32.948 32.4711652830189 0.476834716981132 16 36.112 33.8645652830189 2.24743471698113 17 36.113 33.4527652830189 2.66023471698113 18 35.21 34.0117652830189 1.19823471698113 19 35.193 34.2183652830189 0.97463471698113 20 34.383 34.4383652830189 -0.0553652830188658 21 35.349 35.0487652830189 0.30023471698113 22 37.058 36.8095652830189 0.248434716981131 23 38.076 37.0531652830189 1.02283471698113 24 36.63 35.5499652830189 1.08003471698113 25 36.045 34.1096377358491 1.93536226415093 26 35.638 33.4792 2.1588 27 35.114 32.4711652830189 2.64283471698113 28 35.465 33.8645652830189 1.60043471698114 29 35.254 33.4527652830189 1.80123471698113 30 35.299 34.0117652830189 1.28723471698113 31 35.916 34.2183652830189 1.69763471698113 32 36.683 34.4383652830189 2.24463471698113 33 37.288 35.0487652830189 2.23923471698113 34 38.536 36.8095652830189 1.72643471698113 35 38.977 37.0531652830189 1.92383471698113 36 36.407 35.5499652830189 0.857034716981126 37 34.955 34.1096377358491 0.845362264150931 38 34.951 33.4792 1.4718 39 32.68 32.4711652830189 0.208834716981132 40 34.791 33.8645652830189 0.926434716981128 41 34.178 33.4527652830189 0.72523471698113 42 35.213 34.0117652830189 1.20123471698113 43 34.871 34.2183652830189 0.652634716981134 44 35.299 34.4383652830189 0.860634716981131 45 35.443 35.0487652830189 0.394234716981131 46 37.108 36.8095652830189 0.298434716981128 47 36.419 37.0531652830189 -0.63416528301887 48 34.471 35.5499652830189 -1.07896528301887 49 33.868 34.1096377358491 -0.241637735849065 50 34.385 33.4792 0.905799999999998 51 33.643 34.6673388679245 -1.02433886792453 52 34.627 36.0607388679245 -1.43373886792453 53 32.919 35.6489388679245 -2.72993886792453 54 35.5 36.2079388679245 -0.707938867924528 55 36.11 36.4145388679245 -0.304538867924528 56 37.086 36.6345388679245 0.451461132075471 57 37.711 37.2449388679245 0.466061132075473 58 40.427 39.0057388679245 1.42126113207547 59 39.884 39.2493388679245 0.634661132075473 60 38.512 37.7461388679245 0.765861132075471 61 38.767 36.3058113207547 2.46118867924528 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.999385094477527 0.00122981104494515 0.000614905522472573 17 0.99991235614124 0.000175287717520889 8.76438587604445e-05 18 0.999928879061604 0.000142241876791867 7.11209383959335e-05 19 0.999926692992786 0.000146614014428093 7.33070072140467e-05 20 0.999928177172828 0.000143645654344558 7.18228271722788e-05 21 0.999920526424178 0.000158947151643243 7.94735758216217e-05 22 0.999921987988702 0.000156024022596165 7.80120112980823e-05 23 0.999896402957053 0.000207194085893125 0.000103597042946562 24 0.999807222083554 0.000385555832891331 0.000192777916445665 25 0.999823700886871 0.000352598226258124 0.000176299113129062 26 0.999830804408722 0.000338391182556915 0.000169195591278458 27 0.99990115340675 0.000197693186501828 9.8846593250914e-05 28 0.999840610579957 0.000318778840086776 0.000159389420043388 29 0.999861520558434 0.00027695888313143 0.000138479441565715 30 0.999714799744106 0.000570400511787555 0.000285200255893778 31 0.99954889841111 0.000902203177779186 0.000451101588889593 32 0.9994566873265 0.00108662534699925 0.000543312673499626 33 0.999368763688649 0.00126247262270241 0.000631236311351203 34 0.998857975922751 0.00228404815449717 0.00114202407724858 35 0.99856058365203 0.00287883269594168 0.00143941634797084 36 0.996940902203682 0.00611819559263625 0.00305909779631813 37 0.993403265130633 0.0131934697387346 0.00659673486936732 38 0.986799173513455 0.0264016529730908 0.0132008264865454 39 0.974178350188698 0.0516432996226037 0.0258216498113018 40 0.967006418029166 0.065987163941668 0.032993581970834 41 0.986993588024052 0.0260128239518959 0.0130064119759480 42 0.990940382303429 0.0181192353931423 0.00905961769657117 43 0.990200356567998 0.0195992868640037 0.00979964343200183 44 0.988346409431996 0.0233071811360083 0.0116535905680041 45 0.98740235194817 0.0251952961036599 0.0125976480518300 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.7 NOK 5% type I error level 28 0.933333333333333 NOK 10% type I error level 30 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/10qoic1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/10qoic1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/1fc3y1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/1fc3y1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/2xip11258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/2xip11258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/3oa7s1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/3oa7s1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/444hy1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/444hy1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/5w29c1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/5w29c1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/67gm11258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/67gm11258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/7vo8z1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/7vo8z1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/87okv1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/87okv1258736559.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/9ixql1258736559.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736793bvm1gs0ma10e9yt/9ixql1258736559.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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