*Unverified author*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 21 Dec 2009 05:18:08 -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/Dec/21/t1261398160n7jh4eenbwvtgzc.htm/, Retrieved Mon, 21 Dec 2009 13:22:52 +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/Dec/21/t1261398160n7jh4eenbwvtgzc.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:

» Textbox « » Textfile « » CSV «

2350.44 0 2440.25 0 2408.64 0 2472.81 0 2407.6 0 2454.62 0 2448.05 0 2497.84 0 2645.64 0 2756.76 0 2849.27 0 2921.44 0 2981.85 0 3080.58 0 3106.22 0 3119.31 0 3061.26 0 3097.31 0 3161.69 0 3257.16 0 3277.01 0 3295.32 0 3363.99 0 3494.17 0 3667.03 0 3813.06 0 3917.96 0 3895.51 0 3801.06 0 3570.12 0 3701.61 0 3862.27 0 3970.1 0 4138.52 0 4199.75 0 4290.89 0 4443.91 0 4502.64 1 4356.98 1 4591.27 1 4696.96 1 4621.4 1 4562.84 1 4202.52 1 4296.49 1 4435.23 1 4105.18 1 4116.68 1 3844.49 1 3720.98 1 3674.4 1 3857.62 1 3801.06 1 3504.37 1 3032.6 1 3047.03 1 2962.34 1 2197.82 1 2014.45 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 3249.21675675676 + 575.571879606879Dummy[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) 3249.21675675676 110.505107 29.4033 0 0 Dummy 575.571879606879 180.966011 3.1806 0.002378 0.001189 Multiple Linear Regression - Regression Statistics Multiple R 0.388230633971951 R-squared 0.150723025154263 Adjusted R-squared 0.135823429104337 F-TEST (value) 10.1159135220326 F-TEST (DF numerator) 1 F-TEST (DF denominator) 57 p-value 0.00237838263792423 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 672.176322477571 Sum Squared Residuals 25753797.4844699 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2350.44 3249.21675675675 -898.776756756754 2 2440.25 3249.21675675676 -808.966756756756 3 2408.64 3249.21675675676 -840.576756756757 4 2472.81 3249.21675675676 -776.406756756757 5 2407.6 3249.21675675676 -841.616756756757 6 2454.62 3249.21675675676 -794.596756756757 7 2448.05 3249.21675675676 -801.166756756757 8 2497.84 3249.21675675676 -751.376756756757 9 2645.64 3249.21675675676 -603.576756756757 10 2756.76 3249.21675675676 -492.456756756757 11 2849.27 3249.21675675676 -399.946756756757 12 2921.44 3249.21675675676 -327.776756756757 13 2981.85 3249.21675675676 -267.366756756757 14 3080.58 3249.21675675676 -168.636756756757 15 3106.22 3249.21675675676 -142.996756756757 16 3119.31 3249.21675675676 -129.906756756757 17 3061.26 3249.21675675676 -187.956756756757 18 3097.31 3249.21675675676 -151.906756756757 19 3161.69 3249.21675675676 -87.5267567567569 20 3257.16 3249.21675675676 7.94324324324292 21 3277.01 3249.21675675676 27.7932432432433 22 3295.32 3249.21675675676 46.1032432432432 23 3363.99 3249.21675675676 114.773243243243 24 3494.17 3249.21675675676 244.953243243243 25 3667.03 3249.21675675676 417.813243243243 26 3813.06 3249.21675675676 563.843243243243 27 3917.96 3249.21675675676 668.743243243243 28 3895.51 3249.21675675676 646.293243243243 29 3801.06 3249.21675675676 551.843243243243 30 3570.12 3249.21675675676 320.903243243243 31 3701.61 3249.21675675676 452.393243243243 32 3862.27 3249.21675675676 613.053243243243 33 3970.1 3249.21675675676 720.883243243243 34 4138.52 3249.21675675676 889.303243243244 35 4199.75 3249.21675675676 950.533243243243 36 4290.89 3249.21675675676 1041.67324324324 37 4443.91 3249.21675675676 1194.69324324324 38 4502.64 3824.78863636364 677.851363636364 39 4356.98 3824.78863636364 532.191363636363 40 4591.27 3824.78863636364 766.481363636364 41 4696.96 3824.78863636364 872.171363636364 42 4621.4 3824.78863636364 796.611363636363 43 4562.84 3824.78863636364 738.051363636364 44 4202.52 3824.78863636364 377.731363636364 45 4296.49 3824.78863636364 471.701363636363 46 4435.23 3824.78863636364 610.441363636363 47 4105.18 3824.78863636364 280.391363636364 48 4116.68 3824.78863636364 291.891363636364 49 3844.49 3824.78863636364 19.7013636363634 50 3720.98 3824.78863636364 -103.808636363636 51 3674.4 3824.78863636364 -150.388636363636 52 3857.62 3824.78863636364 32.8313636363635 53 3801.06 3824.78863636364 -23.7286363636364 54 3504.37 3824.78863636364 -320.418636363636 55 3032.6 3824.78863636364 -792.188636363637 56 3047.03 3824.78863636364 -777.758636363636 57 2962.34 3824.78863636364 -862.448636363636 58 2197.82 3824.78863636364 -1626.96863636364 59 2014.45 3824.78863636364 -1810.33863636364 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.000742019934828489 0.00148403986965698 0.999257980065172 6 6.93450392656692e-05 0.000138690078531338 0.999930654960734 7 5.74360322489835e-06 1.14872064497967e-05 0.999994256396775 8 1.22675566242444e-06 2.45351132484888e-06 0.999998773244338 9 9.1462552397515e-06 1.82925104795030e-05 0.99999085374476 10 4.53917504165671e-05 9.07835008331342e-05 0.999954608249583 11 0.000144028530072870 0.000288057060145739 0.999855971469927 12 0.000326366791549812 0.000652733583099625 0.99967363320845 13 0.000598812930278682 0.00119762586055736 0.999401187069721 14 0.00121659960532063 0.00243319921064126 0.99878340039468 15 0.00183865974409928 0.00367731948819856 0.9981613402559 16 0.00228604412441638 0.00457208824883277 0.997713955875584 17 0.00210587551245053 0.00421175102490107 0.99789412448755 18 0.00201743207724334 0.00403486415448668 0.997982567922757 19 0.00214537731703232 0.00429075463406465 0.997854622682968 20 0.00269303240177148 0.00538606480354297 0.997306967598228 21 0.00319648133456157 0.00639296266912313 0.996803518665438 22 0.00365370519843556 0.00730741039687112 0.996346294801564 23 0.00448561513735152 0.00897123027470305 0.995514384862648 24 0.00656721891722591 0.0131344378344518 0.993432781082774 25 0.0119238684170654 0.0238477368341307 0.988076131582935 26 0.0230204292611409 0.0460408585222818 0.97697957073886 27 0.0411661564494015 0.0823323128988031 0.958833843550599 28 0.0567575731917531 0.113515146383506 0.943242426808247 29 0.0623740029235225 0.124748005847045 0.937625997076478 30 0.0567389877821805 0.113477975564361 0.94326101221782 31 0.0548465764384881 0.109693152876976 0.945153423561512 32 0.0576468553840801 0.115293710768160 0.94235314461592 33 0.0630555294064361 0.126111058812872 0.936944470593564 34 0.0745630930124366 0.149126186024873 0.925436906987563 35 0.0856171842951435 0.171234368590287 0.914382815704857 36 0.0979220414639525 0.195844082927905 0.902077958536047 37 0.116035382993207 0.232070765986415 0.883964617006793 38 0.0984036042937825 0.196807208587565 0.901596395706218 39 0.078491436134426 0.156982872268852 0.921508563865574 40 0.073710244862185 0.14742048972437 0.926289755137815 41 0.0810112838177953 0.162022567635591 0.918988716182205 42 0.089436753784622 0.178873507569244 0.910563246215378 43 0.102119124958717 0.204238249917434 0.897880875041283 44 0.0929491707232783 0.185898341446557 0.907050829276722 45 0.094874011993273 0.189748023986546 0.905125988006727 46 0.126070164864661 0.252140329729322 0.873929835135339 47 0.131483114789954 0.262966229579907 0.868516885210046 48 0.152300103754324 0.304600207508649 0.847699896245676 49 0.148975092002613 0.297950184005227 0.851024907997387 50 0.138199875839891 0.276399751679781 0.86180012416011 51 0.128033207416616 0.256066414833231 0.871966792583384 52 0.16686546137792 0.33373092275584 0.83313453862208 53 0.266944756794042 0.533889513588083 0.733055243205958 54 0.355045354295936 0.710090708591872 0.644954645704064 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.38 NOK 5% type I error level 22 0.44 NOK 10% type I error level 23 0.46 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/10sgts1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/10sgts1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/1wrm51261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/1wrm51261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/2d5go1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/2d5go1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/3lxsv1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/3lxsv1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/4oudy1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/4oudy1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/5t3jx1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/5t3jx1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/64n4t1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/64n4t1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/75ngl1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/75ngl1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/8yoqf1261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/8yoqf1261397883.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/9c4q91261397883.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/21/t1261398160n7jh4eenbwvtgzc/9c4q91261397883.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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