blog 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 10:39:33 -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/t1258738920yqif8uavwem0eor.htm/, Retrieved Fri, 20 Nov 2009 18:42:12 +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/t1258738920yqif8uavwem0eor.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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3613 12266.7 4286.1 3884.3 3142.7 3956.2 3730.5 12919.9 4348.1 3892.2 3884.3 3142.7 3481.3 11497.3 3949.3 3613 3892.2 3884.3 3649.5 12142 4166.7 3730.5 3613 3892.2 4215.2 13919.4 4217.9 3481.3 3730.5 3613 4066.6 12656.8 4528.2 3649.5 3481.3 3730.5 4196.8 12034.1 4232.2 4215.2 3649.5 3481.3 4536.6 13199.7 4470.9 4066.6 4215.2 3649.5 4441.6 10881.3 5121.2 4196.8 4066.6 4215.2 3548.3 11301.2 4170.8 4536.6 4196.8 4066.6 4735.9 13643.9 4398.6 4441.6 4536.6 4196.8 4130.6 12517 4491.4 3548.3 4441.6 4536.6 4356.2 13981.1 4251.8 4735.9 3548.3 4441.6 4159.6 14275.7 4901.9 4130.6 4735.9 3548.3 3988 13435 4745.2 4356.2 4130.6 4735.9 4167.8 13565.7 4666.9 4159.6 4356.2 4130.6 4902.2 16216.3 4210.4 3988 4159.6 4356.2 3909.4 12970 5273.6 4167.8 3988 4159.6 4697.6 14079.9 4095.3 4902.2 4167.8 3988 4308.9 14235 4610.1 3909.4 4902.2 4167.8 4420.4 12213.4 4718.1 4697.6 3909.4 4902.2 3544.2 12581 4185.5 4308.9 4697.6 3909.4 4433 14130.4 4314.7 4420.4 4308.9 4697.6 4479.7 14210.8 4422.6 3544.2 4420.4 4308.9 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Y[t] = + 527.153964987183 + 0.240954871069374X[t] + 0.0818613967871051`Yt-1`[t] + 0.219375924876468`Yt-2`[t] + 0.0732409941807161`Yt-3`[t] -0.203227371439877`Yt-4 `[t] -376.320123526623M1[t] -566.548733539981M2[t] -453.392933547681M3[t] -284.226352917444M4[t] -102.796221338208M5[t] -229.174297826116M6[t] -101.477955789942M7[t] -162.031792665541M8[t] + 492.054561282492M9[t] -489.494924058148M10[t] -27.2853312979341M11[t] -2.8468762610436t + 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) 527.153964987183 428.001027 1.2317 0.225449 0.112725 X 0.240954871069374 0.032074 7.5125 0 0 `Yt-1` 0.0818613967871051 0.084415 0.9697 0.338148 0.169074 `Yt-2` 0.219375924876468 0.112689 1.9467 0.058794 0.029397 `Yt-3` 0.0732409941807161 0.109214 0.6706 0.506414 0.253207 `Yt-4 ` -0.203227371439877 0.121176 -1.6771 0.101516 0.050758 M1 -376.320123526623 223.342297 -1.6849 0.099986 0.049993 M2 -566.548733539981 229.555998 -2.468 0.018078 0.009039 M3 -453.392933547681 163.563729 -2.772 0.008497 0.004249 M4 -284.226352917444 175.825767 -1.6165 0.114043 0.057021 M5 -102.796221338208 172.235645 -0.5968 0.554067 0.277033 M6 -229.174297826116 207.469621 -1.1046 0.276095 0.138047 M7 -101.477955789942 234.645415 -0.4325 0.667779 0.33389 M8 -162.031792665541 175.801986 -0.9217 0.362366 0.181183 M9 492.054561282492 198.937744 2.4734 0.017844 0.008922 M10 -489.494924058148 222.378604 -2.2012 0.033709 0.016854 M11 -27.2853312979341 202.411735 -0.1348 0.893462 0.446731 t -2.8468762610436 2.323603 -1.2252 0.227848 0.113924 Multiple Linear Regression - Regression Statistics Multiple R 0.947855213102673 R-squared 0.898429505005913 Adjusted R-squared 0.854155186675157 F-TEST (value) 20.2923396424559 F-TEST (DF numerator) 17 F-TEST (DF denominator) 39 p-value 2.86437540353290e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 215.815377171798 Sum Squared Residuals 1816474.80392841 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3613 3732.86246543437 -119.862465434367 2 3730.5 3923.62816530057 -193.128165300567 3 3481.3 3447.12379137854 34.1762086214585 4 3649.5 3790.30605815103 -140.806058151032 5 4215.2 4412.03222326646 -196.832223266460 6 4066.6 3998.74740039856 67.8525996014425 7 4196.8 4136.38765139644 60.4123486035631 8 4536.6 4348.03457548654 188.565424513461 9 4441.6 4396.59215607707 45.0078439229288 10 3548.3 3549.85117744227 -1.55117744227235 11 4735.9 4569.93326978164 165.966730218363 12 4130.6 4058.45334927771 72.1466507222936 13 4356.2 4226.86565412097 129.334345879032 14 4159.6 4293.72933518343 -134.129335183434 15 3988 3952.44542648268 35.5545735173243 16 4167.8 4240.25557452122 -72.4555745212196 17 4902.2 4922.25190030101 -20.0519003010127 18 3909.4 4164.68032458006 -255.280324580062 19 4697.6 4669.66054484290 27.9394551571036 20 4308.9 4485.22566579919 -176.325665799188 21 4420.4 4609.14007036491 -188.740070364910 22 3544.2 3843.94060341878 -299.740603418783 23 4433 4523.02311663451 -90.0231166345052 24 4479.7 4470.61085277177 9.08914722822901 25 4533.2 4292.11266107063 241.087338929375 26 4237.5 4053.42147700109 184.078522998910 27 4207.4 4086.37110232052 121.028897679482 28 4394 4199.67304744946 194.326952550539 29 5148.4 4724.40286751344 423.997132486556 30 4202.2 4243.81941451376 -41.6194145137602 31 4682.5 4791.63849824442 -109.138498244420 32 4884.3 4695.96031187812 188.339688121877 33 5288.9 5001.88640020074 287.013599799263 34 4505.2 4047.81787133411 457.382128665892 35 4611.5 4819.01125403667 -207.511254036666 36 5104 5084.71990364185 19.28009635815 37 4586.6 4507.22621708191 79.3737829180911 38 4529.3 4405.77487312435 123.525126875648 39 4504.1 4645.4846927837 -141.384692783701 40 4604.9 4841.97604959047 -237.076049590472 41 4795.4 5080.18846632577 -284.788466325774 42 5391.1 5181.38733059569 209.712669404312 43 5213.9 5065.29701784031 148.602982159690 44 5415 5655.4803519969 -240.480351996901 45 5990.3 5862.58043996156 127.719560038441 46 4241.8 4397.89034780484 -156.090347804836 47 5677.6 5546.03235954719 131.567640452809 48 5164.2 5264.71589430867 -100.515894308673 49 3962.3 4292.23300229213 -329.933002292131 50 4011 3991.34614939056 19.653850609443 51 3310.3 3359.67498703456 -49.3749870345644 52 3837.3 3581.28927028782 256.010729712184 53 4145.3 4067.62454259331 77.6754574066904 54 3796.7 3777.36552991193 19.3344700880675 55 3849.6 3977.41628767594 -127.816287675937 56 4285 4245.09909483925 39.9009051607516 57 4189.6 4460.60093339572 -271.000933395723 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.100915725245131 0.201831450490261 0.89908427475487 22 0.0543106822916586 0.108621364583317 0.945689317708341 23 0.074827835922145 0.14965567184429 0.925172164077855 24 0.0337921911658902 0.0675843823317803 0.96620780883411 25 0.11406806267476 0.22813612534952 0.88593193732524 26 0.145769400869119 0.291538801738237 0.854230599130881 27 0.0884385642329883 0.176877128465977 0.911561435767012 28 0.0890531313810226 0.178106262762045 0.910946868618977 29 0.16090955106892 0.32181910213784 0.83909044893108 30 0.127717230572220 0.255434461144441 0.87228276942778 31 0.118998308490131 0.237996616980262 0.881001691509869 32 0.079788830147394 0.159577660294788 0.920211169852606 33 0.0836273078149583 0.167254615629917 0.916372692185042 34 0.137651371888914 0.275302743777828 0.862348628111086 35 0.296204840467894 0.592409680935789 0.703795159532106 36 0.169951534002777 0.339903068005554 0.830048465997223 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 1 0.0625 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/102m9a1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/102m9a1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/1zmtz1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/1zmtz1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/2h9wl1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/2h9wl1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/3o3t51258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/3o3t51258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/4a7qg1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/4a7qg1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/5e9zf1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/5e9zf1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/6j2lo1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/6j2lo1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/7fyit1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/7fyit1258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/88zc91258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/88zc91258738769.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/9lolp1258738769.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258738920yqif8uavwem0eor/9lolp1258738769.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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