Model 4

*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 07:38:51 -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/t1258727994fdvidnjrq336989.htm/, Retrieved Fri, 20 Nov 2009 15:40:06 +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/t1258727994fdvidnjrq336989.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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21,3 2533 21,8 19,2 20 20,3 21,5 2058 21,3 21,8 19,2 20 19,5 2160 21,5 21,3 21,8 19,2 19,5 2260 19,5 21,5 21,3 21,8 19,7 2498 19,5 19,5 21,5 21,3 18,7 2695 19,7 19,5 19,5 21,5 19,7 2799 18,7 19,7 19,5 19,5 20 2946 19,7 18,7 19,7 19,5 19,7 2930 20 19,7 18,7 19,7 19,2 2318 19,7 20 19,7 18,7 19,7 2540 19,2 19,7 20 19,7 22 2570 19,7 19,2 19,7 20 21,8 2669 22 19,7 19,2 19,7 22,8 2450 21,8 22 19,7 19,2 21 2842 22,8 21,8 22 19,7 25 3440 21 22,8 21,8 22 23,3 2678 25 21 22,8 21,8 25 2981 23,3 25 21 22,8 26,8 2260 25 23,3 25 21 25,3 2844 26,8 25 23,3 25 26,5 2546 25,3 26,8 25 23,3 27,8 2456 26,5 25,3 26,8 25 22 2295 27,8 26,5 25,3 26,8 22,3 2379 22 27,8 26,5 25,3 28 2479 22,3 22 27,8 26,5 25 2057 28 22,3 22 27,8 27,3 2280 25 28 22,3 22 25,8 2351 27,3 25 28 22,3 27,3 2276 25,8 27,3 25 28 23,5 2548 27,3 25,8 27,3 25 24,5 2311 23,5 27,3 25,8 27,3 18 2201 24,5 23,5 27,3 25,8 21,3 2725 18 24,5 23,5 27,3 21,8 2408 21,3 18 24,5 23,5 20,5 2139 21,8 21,3 18 24,5 22,3 1898 20,5 21,8 21,3 18 18 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 1.85422779475554 + 0.000604826557932779X[t] + 0.574485001754852Y1[t] + 0.391886275032236Y2[t] -0.0635822312445514Y3[t] -0.00547461318830385Y4[t] -0.301760548071200M1[t] -0.417100597659018M2[t] -1.99878180986883M3[t] + 0.351604644101707M4[t] + 0.316273377056073M5[t] -0.606367151252546M6[t] -1.53548312063179M7[t] -0.388501259503871M8[t] + 0.560018451568021M9[t] + 1.32513484904000M10[t] -0.155911104604277M11[t] -0.0496906557819444t + 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) 1.85422779475554 5.074053 0.3654 0.716763 0.358381 X 0.000604826557932779 0.001756 0.3444 0.732365 0.366183 Y1 0.574485001754852 0.164087 3.5011 0.001177 0.000588 Y2 0.391886275032236 0.18973 2.0655 0.045567 0.022784 Y3 -0.0635822312445514 0.19297 -0.3295 0.743546 0.371773 Y4 -0.00547461318830385 0.167352 -0.0327 0.97407 0.487035 M1 -0.301760548071200 2.062045 -0.1463 0.884406 0.442203 M2 -0.417100597659018 2.100274 -0.1986 0.843612 0.421806 M3 -1.99878180986883 2.043168 -0.9783 0.333968 0.166984 M4 0.351604644101707 2.110233 0.1666 0.868531 0.434265 M5 0.316273377056073 2.112733 0.1497 0.881774 0.440887 M6 -0.606367151252546 2.127673 -0.285 0.777159 0.38858 M7 -1.53548312063179 2.048291 -0.7496 0.457969 0.228985 M8 -0.388501259503871 2.079945 -0.1868 0.852798 0.426399 M9 0.560018451568021 2.211955 0.2532 0.80146 0.40073 M10 1.32513484904000 2.213938 0.5985 0.552939 0.27647 M11 -0.155911104604277 2.17245 -0.0718 0.943154 0.471577 t -0.0496906557819444 0.034665 -1.4335 0.159695 0.079848 Multiple Linear Regression - Regression Statistics Multiple R 0.933057841963762 R-squared 0.870596936450073 Adjusted R-squared 0.814190472851387 F-TEST (value) 15.4343470749043 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 2.58504329053721e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.00902986975080 Sum Squared Residuals 353.116169525049 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 21.3 21.7000125084072 -0.40001250840724 2 21.5 22.0318591711779 -0.531859171177916 3 19.5 20.2201993642450 -0.720199364244964 4 19.5 21.5283421910563 -2.02834219105626 5 19.7 20.7935172992975 -1.09351729929745 6 18.7 20.1813034873221 -1.48130348732206 7 19.7 18.7802403038141 0.919759696185918 8 20 20.1363232936499 -0.136323293649880 9 19.7 21.6521942081785 -1.95219420817849 10 19.2 21.8845788603406 -2.68457886034063 11 19.7 20.0587560808267 -0.358756080826719 12 22 20.2918529751652 1.70814702483478 13 21.8 21.5509717416785 0.249028258321539 14 22.8 22.0108716433165 0.789128356683531 15 21 20.9637230943261 0.0362769056738507 16 25 22.9840432819479 2.01595671805215 17 23.3 23.96820092533 -0.66820092532999 18 25 23.8790261884906 1.12097381150936 19 26.8 22.5300828292491 4.26991717075089 20 25.3 25.7670637555039 -0.467063755503882 21 26.5 26.23053933826 0.269460661740011 22 27.8 26.8693284206332 0.930671579366772 23 22 26.5438278108277 -4.54382781082771 24 22.3 23.8102060801691 -1.51020608016915 25 28 21.3294162010049 6.67058379899511 26 25 24.6629390247734 0.337060975226573 27 27.3 23.6894183287387 3.61058167126133 28 25.8 25.8146523894295 -0.0146523894294990 29 27.3 25.8834208032592 1.41657919674085 30 23.5 25.2196852407127 -1.71968524071267 31 24.5 22.5851028637351 1.91489713626494 32 18 22.6140188772564 -4.61401887725641 33 21.3 20.7209113714757 0.579088628524328 34 21.8 20.5503681112535 1.24963188874650 35 20.5 20.8452102561284 -0.345210256128447 36 22.3 20.0805437623407 2.21945623765926 37 18.7 20.5903402354798 -1.89034023547980 38 22.3 18.8588147572039 3.44118524279612 39 17.7 17.7744431535285 -0.0744431535284873 40 19.7 19.3387459994775 0.36125400052248 41 20.5 18.3388147060266 2.16118529397335 42 18.5 18.7350360495159 -0.235036049515885 43 10 17.1841024400393 -7.18410244003927 44 14.2 12.1147891410890 2.08521085891104 45 15.5 12.5478371119871 2.95216288801290 46 16.5 15.9957246077727 0.504275392227346 47 20.5 15.2522058522171 5.24779414778287 48 15.7 18.1173971823249 -2.41739718232489 49 11.7 16.3292593134296 -4.62925931342961 50 7.5 11.5355154035283 -4.03551540352831 51 3.5 6.35221605916173 -2.85221605916173 52 4.5 4.83421613808886 -0.334216138088863 53 2.2 4.01604626608676 -1.81604626608676 54 5 2.68494903395874 2.31505096604126 55 2.3 2.22047156316247 0.0795284368375297 56 6.1 2.96780493250087 3.13219506749913 57 3.3 5.14851797009875 -1.84851797009875 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.0105736376674834 0.0211472753349668 0.989426362332517 22 0.00301165343483557 0.00602330686967114 0.996988346565164 23 0.0116265171047791 0.0232530342095582 0.98837348289522 24 0.0359694330151446 0.0719388660302892 0.964030566984855 25 0.0645538612947193 0.129107722589439 0.93544613870528 26 0.0315829065062475 0.063165813012495 0.968417093493752 27 0.0610109095244062 0.122021819048812 0.938989090475594 28 0.0540065834548045 0.108013166909609 0.945993416545196 29 0.0270567800696462 0.0541135601392925 0.972943219930354 30 0.0287471065754386 0.0574942131508773 0.971252893424561 31 0.0373702178206093 0.0747404356412185 0.96262978217939 32 0.152253197687707 0.304506395375414 0.847746802312293 33 0.0868788412302134 0.173757682460427 0.913121158769787 34 0.0628568305536573 0.125713661107315 0.937143169446343 35 0.128070571889005 0.256141143778011 0.871929428110995 36 0.0685925491387563 0.137185098277513 0.931407450861244 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0625 NOK 5% type I error level 3 0.1875 NOK 10% type I error level 8 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/10hmo41258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/10hmo41258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/1tkij1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/1tkij1258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/2b1xy1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/2b1xy1258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/33sf51258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/33sf51258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/4w80y1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/4w80y1258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/5sf931258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/5sf931258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/6gbfn1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/6gbfn1258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/76ila1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/76ila1258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/8s7hn1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/8s7hn1258727926.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/9vg9a1258727926.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258727994fdvidnjrq336989/9vg9a1258727926.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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