blog 5

*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 10:45:44 -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/t1258739324iumhzxd785mnk1f.htm/, Retrieved Fri, 20 Nov 2009 18:48:56 +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/t1258739324iumhzxd785mnk1f.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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3884.3 12476.8 3983.4 3956.2 3892.2 12384.6 4152.9 3142.7 3613 12266.7 4286.1 3884.3 3730.5 12919.9 4348.1 3892.2 3481.3 11497.3 3949.3 3613 3649.5 12142 4166.7 3730.5 4215.2 13919.4 4217.9 3481.3 4066.6 12656.8 4528.2 3649.5 4196.8 12034.1 4232.2 4215.2 4536.6 13199.7 4470.9 4066.6 4441.6 10881.3 5121.2 4196.8 3548.3 11301.2 4170.8 4536.6 4735.9 13643.9 4398.6 4441.6 4130.6 12517 4491.4 3548.3 4356.2 13981.1 4251.8 4735.9 4159.6 14275.7 4901.9 4130.6 3988 13435 4745.2 4356.2 4167.8 13565.7 4666.9 4159.6 4902.2 16216.3 4210.4 3988 3909.4 12970 5273.6 4167.8 4697.6 14079.9 4095.3 4902.2 4308.9 14235 4610.1 3909.4 4420.4 12213.4 4718.1 4697.6 3544.2 12581 4185.5 4308.9 4433 14130.4 4314.7 4420.4 4479.7 14210.8 4422.6 3544.2 4533.2 14378.5 5059.2 4433 4237.5 13142.8 5043.6 4479.7 4207.4 13714.7 4436.6 4533.2 4394 13621.9 4922.6 4237.5 5148.4 15379.8 4454.8 4207.4 4202.2 13306.3 5058.7 4394 4682.5 14391.2 4768.9 5148.4 4884.3 14909.9 5171.8 4202.2 5288.9 14025.4 4989.3 4682.5 45 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] = -169.548966574492 + 0.255796082918873X[t] + 0.0207257906429053Y1[t] + 0.175892072127307Y2[t] + 320.150624530104M1[t] + 352.419180806125M2[t] -53.9281294197686M3[t] + 23.9260159457721M4[t] -86.3569691050612M5[t] + 97.5515370487508M6[t] + 273.292809663731M7[t] + 214.749768916847M8[t] + 411.976430473136M9[t] + 313.631950712519M10[t] + 891.257869708524M11[t] -3.16006754830636t + 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) -169.548966574492 331.386342 -0.5116 0.611524 0.305762 X 0.255796082918873 0.03084 8.2942 0 0 Y1 0.0207257906429053 0.067025 0.3092 0.758646 0.379323 Y2 0.175892072127307 0.098885 1.7787 0.082351 0.041176 M1 320.150624530104 156.234546 2.0492 0.046579 0.023289 M2 352.419180806125 195.810666 1.7998 0.078912 0.039456 M3 -53.9281294197686 153.440499 -0.3515 0.72696 0.36348 M4 23.9260159457721 151.855534 0.1576 0.875543 0.437771 M5 -86.3569691050612 160.753804 -0.5372 0.593899 0.296949 M6 97.5515370487508 166.599475 0.5855 0.561242 0.280621 M7 273.292809663731 204.91515 1.3337 0.189328 0.094664 M8 214.749768916847 168.44607 1.2749 0.209195 0.104598 M9 411.976430473136 152.580136 2.7001 0.009874 0.004937 M10 313.631950712519 178.803776 1.7541 0.086546 0.043273 M11 891.257869708524 150.593946 5.9183 0 0 t -3.16006754830636 2.2056 -1.4327 0.159161 0.07958 Multiple Linear Regression - Regression Statistics Multiple R 0.942613397058808 R-squared 0.888520016314745 Adjusted R-squared 0.849631649912912 F-TEST (value) 22.8479645334977 F-TEST (DF numerator) 15 F-TEST (DF denominator) 43 p-value 1.11022302462516e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 218.394652553447 Sum Squared Residuals 2050937.64334946 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3884.3 4117.38148796651 -233.081487966507 2 3892.2 3983.3303986875 -91.1303986875024 3 3613 3676.86689874041 -63.8668987404131 4 3730.5 3921.32152430992 -190.821524309920 5 3481.3 3386.60845230406 94.6915476959427 6 3649.5 3757.44173092809 -107.941730928087 7 4215.2 4341.90374988156 -126.703749881557 8 4066.6 3993.2487666613 73.351233338696 9 4196.8 4121.39845100782 75.401548992177 10 4536.6 4296.85950225748 239.740497742519 11 4441.6 4314.66684451212 126.933155487878 12 3548.3 3567.72801715477 -19.4280171547682 13 4735.9 4471.98364584697 263.916354153032 14 4130.6 4057.63449407374 72.9655059262573 15 4356.2 4226.56168672141 129.638313278585 16 4159.6 4283.61965580484 -124.019655804843 17 3988 3991.56235637398 -3.56235637398397 18 4167.8 4169.54013222942 -1.74013222941856 19 4902.2 4980.49003167532 -78.2900316753234 20 3909.4 4142.05715458062 -232.657154580623 21 4697.6 4724.78575967602 -27.1857596760217 22 4308.9 4498.99917264279 -190.099172642794 23 4420.4 4697.22417950188 -276.824179501877 24 3544.2 3817.42907779373 -273.229077793728 25 4433 4553.03982384328 -120.039823843285 26 4479.7 4450.8339968501 28.8660031499005 27 4533.2 4253.75053421142 279.449465788581 28 4237.5 4020.24822980012 217.251770199882 29 4207.4 4049.92462796085 157.475372039150 30 4394 4164.99663859589 229.003361404109 31 5148.4 4772.25190159187 376.148098408133 32 4202.2 4225.4933809926 -23.2933809926006 33 4682.5 4823.75979044380 -141.259790443795 34 4884.3 4696.85801374806 187.441986251941 35 5288.9 5125.77073530443 163.129264695569 36 4505.2 3996.48211418025 508.717885819751 37 4611.5 4733.45015660221 -121.950156602211 38 5104 5072.91460966065 31.0853903393505 39 4586.6 4528.91944683760 57.6805531624048 40 4529.3 4505.89593847394 23.4040615260632 41 4504.1 4606.10195442988 -102.001954429879 42 4604.9 4874.84912309456 -269.949123094561 43 4795.4 5108.92948566271 -313.52948566271 44 5391.1 5214.52006218029 176.579937819706 45 5213.9 5072.56920895445 141.330791045545 46 5415 5673.68346228286 -258.683462282857 47 5990.3 5749.30084749589 240.999152504114 48 4241.8 4457.86079087126 -216.060790871256 49 5677.6 5466.44488574103 211.155114258971 50 5164.2 5205.98650072801 -41.7865007280059 51 3962.3 4365.20143348916 -402.901433489158 52 4011 3936.81465161118 74.1853483888178 53 3310.3 3456.90260893123 -146.602608931230 54 3837.3 3686.67237515204 150.627624847958 55 4145.3 4002.92483118854 142.375168811457 56 3796.7 3790.68063558518 6.01936441482239 57 3849.6 3897.88678991791 -48.2867899179053 58 4285 4263.39984906881 21.6001509311903 59 4189.6 4443.83739318568 -254.237393185684 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.225225537374528 0.450451074749056 0.774774462625472 20 0.284092593344317 0.568185186688634 0.715907406655683 21 0.244329648484189 0.488659296968377 0.755670351515811 22 0.300232922010881 0.600465844021762 0.699767077989119 23 0.334515100023982 0.669030200047965 0.665484899976017 24 0.313868906733387 0.627737813466774 0.686131093266613 25 0.293927603235711 0.587855206471422 0.706072396764289 26 0.267639200126874 0.535278400253747 0.732360799873126 27 0.245657632933917 0.491315265867834 0.754342367066083 28 0.214272385725621 0.428544771451243 0.785727614274379 29 0.153480683708834 0.306961367417667 0.846519316291166 30 0.114639399228507 0.229278798457013 0.885360600771493 31 0.139782130726763 0.279564261453525 0.860217869273237 32 0.102310665905703 0.204621331811406 0.897689334094297 33 0.098695789670041 0.197391579340082 0.90130421032996 34 0.0595824742545773 0.119164948509155 0.940417525745423 35 0.0516022613640218 0.103204522728044 0.948397738635978 36 0.159481633865599 0.318963267731197 0.840518366134401 37 0.221626280614618 0.443252561229237 0.778373719385382 38 0.146284139778011 0.292568279556023 0.853715860221989 39 0.1422077594679 0.2844155189358 0.8577922405321 40 0.0818589607725494 0.163717921545099 0.91814103922745 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/20/t1258739324iumhzxd785mnk1f/10pavu1258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/10pavu1258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/1xyt71258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/1xyt71258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/2la461258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/2la461258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/30dxe1258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/30dxe1258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/4c0pv1258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/4c0pv1258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/5446g1258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/5446g1258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/6a8a91258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/6a8a91258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/77jzx1258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/77jzx1258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/87fi01258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/87fi01258739139.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/9sfb91258739139.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258739324iumhzxd785mnk1f/9sfb91258739139.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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