WorkShop7 (SHW)

*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, 27 Nov 2009 06:36:52 -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/27/t125932913116lsgjrct1c1e0i.htm/, Retrieved Fri, 27 Nov 2009 14:39:13 +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/27/t125932913116lsgjrct1c1e0i.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 «

282965 1 276610 1 277838 1 277051 1 277026 1 274960 1 270073 1 267063 1 264916 1 287182 1 291109 1 292223 1 288109 1 281400 1 282579 1 280113 1 280331 1 276759 1 275139 1 274275 1 271234 1 289725 1 290649 1 292223 1 278429 0 269749 0 265784 0 268957 0 264099 0 255121 0 253276 0 245980 0 235295 0 258479 0 260916 0 254586 0 250566 0 243345 0 247028 0 248464 0 244962 0 237003 0 237008 0 225477 0 226762 0 247857 0 248256 0 246892 0 245021 0 246186 0 255688 0 264242 0 268270 0 272969 0 273886 0 267353 0 271916 0 292633 0 295804 0 293222 0

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 Y[t] = + 257263.361111111 + 22801.3055555555X[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) 257263.361111111 2387.12003 107.7714 0 0 X 22801.3055555555 3774.368171 6.0411 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.621457614902277 R-squared 0.386209567120026 Adjusted R-squared 0.375626973449682 F-TEST (value) 36.4947931623135 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 1.16594568666528e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14322.7201794422 Sum Squared Residuals 11898138173.6389 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 282965 280064.666666667 2900.33333333323 2 276610 280064.666666667 -3454.66666666673 3 277838 280064.666666667 -2226.66666666666 4 277051 280064.666666667 -3013.66666666666 5 277026 280064.666666667 -3038.66666666666 6 274960 280064.666666667 -5104.66666666666 7 270073 280064.666666667 -9991.66666666666 8 267063 280064.666666667 -13001.6666666667 9 264916 280064.666666667 -15148.6666666667 10 287182 280064.666666667 7117.33333333334 11 291109 280064.666666667 11044.3333333333 12 292223 280064.666666667 12158.3333333333 13 288109 280064.666666667 8044.33333333334 14 281400 280064.666666667 1335.33333333334 15 282579 280064.666666667 2514.33333333334 16 280113 280064.666666667 48.3333333333404 17 280331 280064.666666667 266.333333333340 18 276759 280064.666666667 -3305.66666666666 19 275139 280064.666666667 -4925.66666666666 20 274275 280064.666666667 -5789.66666666666 21 271234 280064.666666667 -8830.66666666666 22 289725 280064.666666667 9660.33333333334 23 290649 280064.666666667 10584.3333333333 24 292223 280064.666666667 12158.3333333333 25 278429 257263.361111111 21165.6388888889 26 269749 257263.361111111 12485.6388888889 27 265784 257263.361111111 8520.63888888889 28 268957 257263.361111111 11693.6388888889 29 264099 257263.361111111 6835.63888888889 30 255121 257263.361111111 -2142.36111111111 31 253276 257263.361111111 -3987.36111111111 32 245980 257263.361111111 -11283.3611111111 33 235295 257263.361111111 -21968.3611111111 34 258479 257263.361111111 1215.63888888889 35 260916 257263.361111111 3652.63888888889 36 254586 257263.361111111 -2677.36111111111 37 250566 257263.361111111 -6697.36111111111 38 243345 257263.361111111 -13918.3611111111 39 247028 257263.361111111 -10235.3611111111 40 248464 257263.361111111 -8799.36111111111 41 244962 257263.361111111 -12301.3611111111 42 237003 257263.361111111 -20260.3611111111 43 237008 257263.361111111 -20255.3611111111 44 225477 257263.361111111 -31786.3611111111 45 226762 257263.361111111 -30501.3611111111 46 247857 257263.361111111 -9406.36111111111 47 248256 257263.361111111 -9007.36111111111 48 246892 257263.361111111 -10371.3611111111 49 245021 257263.361111111 -12242.3611111111 50 246186 257263.361111111 -11077.3611111111 51 255688 257263.361111111 -1575.36111111111 52 264242 257263.361111111 6978.63888888889 53 268270 257263.361111111 11006.6388888889 54 272969 257263.361111111 15705.6388888889 55 273886 257263.361111111 16622.6388888889 56 267353 257263.361111111 10089.6388888889 57 271916 257263.361111111 14652.6388888889 58 292633 257263.361111111 35369.6388888889 59 295804 257263.361111111 38540.6388888889 60 293222 257263.361111111 35958.6388888889 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0114978120366261 0.0229956240732523 0.988502187963374 6 0.00329760672068065 0.0065952134413613 0.99670239327932 7 0.00445635097748595 0.0089127019549719 0.995543649022514 8 0.00679104428626274 0.0135820885725255 0.993208955713737 9 0.0094531153017295 0.018906230603459 0.99054688469827 10 0.0152803717089901 0.0305607434179802 0.98471962829101 11 0.0288973852807135 0.057794770561427 0.971102614719286 12 0.0408841619186840 0.0817683238373681 0.959115838081316 13 0.0321364567026652 0.0642729134053304 0.967863543297335 14 0.0174332156562222 0.0348664313124443 0.982566784343778 15 0.00933225031655431 0.0186645006331086 0.990667749683446 16 0.00457137612651217 0.00914275225302434 0.995428623873488 17 0.00215027216503313 0.00430054433006627 0.997849727834967 18 0.00102475974442154 0.00204951948884309 0.998975240255578 19 0.000515285761169788 0.00103057152233958 0.99948471423883 20 0.000272423160758447 0.000544846321516893 0.999727576839242 21 0.000203375436133375 0.00040675087226675 0.999796624563867 22 0.000189683495027052 0.000379366990054104 0.999810316504973 23 0.000179735351172825 0.000359470702345649 0.999820264648827 24 0.000187638651768604 0.000375277303537209 0.999812361348231 25 0.000118431964719039 0.000236863929438077 0.99988156803528 26 7.37716409892976e-05 0.000147543281978595 0.99992622835901 27 4.45923219717819e-05 8.91846439435637e-05 0.999955407678028 28 2.25283337351463e-05 4.50566674702926e-05 0.999977471666265 29 1.23084475149109e-05 2.46168950298218e-05 0.999987691552485 30 1.37210438488024e-05 2.74420876976048e-05 0.999986278956151 31 1.37883473780227e-05 2.75766947560454e-05 0.999986211652622 32 3.02640713734695e-05 6.05281427469391e-05 0.999969735928627 33 0.000266361816108508 0.000532723632217016 0.999733638183891 34 0.000127735174985079 0.000255470349970158 0.999872264825015 35 6.04418150677123e-05 0.000120883630135425 0.999939558184932 36 2.92674399350837e-05 5.85348798701674e-05 0.999970732560065 37 1.69072763282739e-05 3.38145526565477e-05 0.999983092723672 38 1.92691045102028e-05 3.85382090204057e-05 0.99998073089549 39 1.32454946908398e-05 2.64909893816796e-05 0.99998675450531 40 7.70092978126426e-06 1.54018595625285e-05 0.999992299070219 41 6.0280567671771e-06 1.20561135343542e-05 0.999993971943233 42 1.40502716066721e-05 2.81005432133443e-05 0.999985949728393 43 3.05634610654992e-05 6.11269221309985e-05 0.999969436538934 44 0.000598714024376538 0.00119742804875308 0.999401285975623 45 0.00764526135013597 0.0152905227002719 0.992354738649864 46 0.00757829140579366 0.0151565828115873 0.992421708594206 47 0.00824579490979927 0.0164915898195985 0.9917542050902 48 0.0121183576086795 0.0242367152173589 0.98788164239132 49 0.0296869669190608 0.0593739338381216 0.97031303308094 50 0.0980973988508455 0.196194797701691 0.901902601149154 51 0.172709279741175 0.345418559482351 0.827290720258825 52 0.201320633721532 0.402641267443064 0.798679366278468 53 0.208824177546121 0.417648355092242 0.791175822453879 54 0.183866559059425 0.367733118118849 0.816133440940575 55 0.152843897008622 0.305687794017244 0.847156102991378 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 31 0.607843137254902 NOK 5% type I error level 41 0.80392156862745 NOK 10% type I error level 45 0.88235294117647 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/10r7l51259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/10r7l51259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/1s6qa1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/1s6qa1259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/2dhg41259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/2dhg41259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/32my31259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/32my31259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/41kme1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/41kme1259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/555ak1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/555ak1259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/6uosa1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/6uosa1259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/7oe4v1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/7oe4v1259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/86htz1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/86htz1259329007.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/924om1259329007.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/27/t125932913116lsgjrct1c1e0i/924om1259329007.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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