ws7

*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 07:21:10 -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/t1258728032yjst5ejacf95yvf.htm/, Retrieved Fri, 20 Nov 2009 15:40:45 +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/t1258728032yjst5ejacf95yvf.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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10284.5 1.038351422 1.4 12792 0.933031106 1.3 12823.61538 0.932783124 1.3 13845.66667 0.953755367 1.2 15335.63636 1.009865664 1.1 11188.5 0.979532493 1.4 13633.25 0.98651077 1.2 12298.46667 0.964661281 1.5 15353.63636 0.946761816 1.1 12696.15385 0.959068881 1.3 12213.93333 0.985710058 1.5 13683.72727 0.92582159 1.1 11214.14286 1.036865325 1.4 13950.23077 0.944443576 1.3 11179.13333 0.944901812 1.5 11801.875 0.989151445 1.6 11188.82353 1.054361624 1.7 16456.27273 1.033478919 1.1 11110.0625 1.001368875 1.6 16530.69231 1.019812646 1.3 10038.41176 0.993902155 1.7 11681.25 0.961444482 1.6 11148.88235 0.957449711 1.7 8631 0.93308639 1.9 9386.444444 1.045170549 1.8 9764.736842 0.953166261 1.9 12043.75 0.966782226 1.6 12948.06667 0.972992606 1.5 10987.125 1.013607482 1.6 11648.3125 0.984839518 1.6 10633.35294 0.973220775 1.7 10219.3 0.957284573 2 9037.6 0.972067159 2 10296.31579 0.986878944 1.9 11705.41176 0.954654488 1.7 10681.94444 0.978986976 1.8 9362.947368 1.003056035 1.9 113 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Invoer/inflatie[t] = + 609.166987055855 + 19553.7411763901`Uitvoer/inflatie`[t] -4968.06824936545Inflatie[t] -2293.74599688658M1[t] + 748.788773984817M2[t] + 652.42614918149M3[t] + 14.0241082018048M4[t] -1315.63792759216M5[t] -758.668421233577M6[t] -519.441502684909M7[t] + 555.88998307813M8[t] -60.2757250744329M9[t] -187.771360832577M10[t] + 399.126243757439M11[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) 609.166987055855 6142.514804 0.0992 0.921432 0.460716 `Uitvoer/inflatie` 19553.7411763901 7059.010671 2.77 0.008057 0.004029 Inflatie -4968.06824936545 446.032479 -11.1384 0 0 M1 -2293.74599688658 930.868751 -2.4641 0.017534 0.008767 M2 748.788773984817 703.424032 1.0645 0.292662 0.146331 M3 652.42614918149 708.092117 0.9214 0.361656 0.180828 M4 14.0241082018048 778.837815 0.018 0.985712 0.492856 M5 -1315.63792759216 958.077895 -1.3732 0.176345 0.088173 M6 -758.668421233577 872.449772 -0.8696 0.389043 0.194521 M7 -519.441502684909 737.90079 -0.7039 0.485015 0.242508 M8 555.88998307813 748.396542 0.7428 0.461395 0.230697 M9 -60.2757250744329 720.560054 -0.0837 0.933697 0.466848 M10 -187.771360832577 710.956807 -0.2641 0.792874 0.396437 M11 399.126243757439 713.509468 0.5594 0.578613 0.289306 Multiple Linear Regression - Regression Statistics Multiple R 0.918613524596436 R-squared 0.843850807571488 Adjusted R-squared 0.799721687972126 F-TEST (value) 19.1223123242115 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 2.55351295663786e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1107.02513224762 Sum Squared Residuals 56373213.5976813 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10284.5 11663.7803969823 -1379.28039698228 2 12792 13143.7157931106 -351.715793110617 3 12823.61538 13042.5041924629 -218.888812462891 4 13845.66667 13310.9947879301 534.671882069892 5 15335.63636 13575.3058019411 1760.33055805893 6 11188.5 12048.7278586968 -860.227858696835 7 13633.25 13418.0198494337 215.230150566253 8 12298.46667 12575.6916076448 -277.22493764477 9 15353.63636 13596.7516934325 1756.88466656747 10 12696.15385 12716.2915714523 -20.1377214523066 11 12213.93333 12830.5102058616 -616.576875861633 12 13683.72727 13247.5676591278 436.159610872151 13 11214.14286 11634.7216408813 -420.578780881293 14 13950.23077 13366.8722776739 583.358492326066 15 11179.13333 12285.8562311392 -1106.72290113922 16 11801.875 12015.8932360552 -214.018236055246 17 11188.82353 11464.5273375568 -275.703807556811 18 16456.27273 14594.0027849018 1862.26994509825 19 11110.0625 11721.3240892292 -611.261589229198 20 16530.69231 14647.7207742525 1882.97153574752 21 10038.41176 11537.6807315866 -1499.26897158655 22 11681.25 11272.3229837350 408.927016264954 23 11148.88235 11284.3010451956 -135.418695195568 24 8631 9415.16707853373 -784.16707853373 25 9386.444444 9809.89254164306 -423.448097643058 26 9764.736842 10556.5924529078 -791.855610907849 27 12043.75 12216.8933583909 -173.143358390942 28 12948.06667 12196.7343054748 751.332364525167 29 10987.125 11164.4382179595 -177.313217959505 30 11648.3125 11158.8864020904 489.426097909624 31 10633.35294 10674.1166022855 -40.763662285505 32 10219.3 9947.41524399624 271.884756003761 33 9037.6 9620.3043964054 -582.704396405403 34 10296.31579 10279.2413958341 17.0743941658583 35 11705.41176 11229.6439781233 475.767781876726 36 10681.94444 10809.5020819589 -127.557641958910 37 9362.947368 8489.58941018105 873.357957818947 38 11306.35294 11880.9555816368 -574.602641636753 39 10984.45 10710.5732720158 273.876727984227 40 10062.61905 10186.0802839407 -123.461233940748 41 8118.583333 8681.8115464835 -563.2282134835 42 8867.48 8164.59824136598 702.881758634016 43 8346.72 6948.61734149725 1398.10265850275 44 8529.307692 8535.32384399626 -6.0161519962549 45 10697.18182 9668.27929464008 1028.90252535992 46 8591.84 7679.65352331848 912.18647668152 47 8695.607143 7018.31169594111 1677.29544705889 48 8125.571429 6565.53808164451 1560.03334735549 49 7009.758621 5659.80930331232 1349.94931768769 50 7883.466667 6748.65111367085 1134.81555332915 51 7527.645161 6302.76681699117 1224.87834400883 52 6763.758621 7712.28339759907 -948.524776599065 53 6682.333333 7426.41865205911 -744.085319059113 54 7855.681818 10050.0317609451 -2194.34994294505 55 6738.88 7700.1875575543 -961.3075575543 56 7895.434783 9767.04998511025 -1871.61520211025 57 6361.884615 7065.69843893544 -703.81382393544 58 6935.956522 8254.00668766003 -1318.05016566003 59 8344.454545 9745.52220287841 -1401.06765787841 60 9107.944444 10192.412681735 -1084.46823773500 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.064396675942302 0.128793351884604 0.935603324057698 18 0.0422739227308281 0.0845478454616563 0.957726077269172 19 0.0242577631919942 0.0485155263839883 0.975742236808006 20 0.0196468699649209 0.0392937399298417 0.98035313003508 21 0.0447381040910848 0.0894762081821697 0.955261895908915 22 0.125575634236109 0.251151268472218 0.874424365763891 23 0.18225752228522 0.36451504457044 0.81774247771478 24 0.235517152841867 0.471034305683733 0.764482847158133 25 0.28404229989665 0.5680845997933 0.71595770010335 26 0.242254740123107 0.484509480246214 0.757745259876893 27 0.186263786076481 0.372527572152961 0.81373621392352 28 0.169325762459102 0.338651524918203 0.830674237540898 29 0.118122041616723 0.236244083233447 0.881877958383277 30 0.113313988536947 0.226627977073894 0.886686011463053 31 0.0957117762830444 0.191423552566089 0.904288223716956 32 0.079192850570348 0.158385701140696 0.920807149429652 33 0.0515633499046475 0.103126699809295 0.948436650095352 34 0.0384591325919330 0.0769182651838661 0.961540867408067 35 0.0360993109899270 0.0721986219798541 0.963900689010073 36 0.0227086404170830 0.0454172808341661 0.977291359582917 37 0.0378823445606627 0.0757646891213254 0.962117655439337 38 0.0204538177344879 0.0409076354689757 0.979546182265512 39 0.0191379281063076 0.0382758562126152 0.980862071893692 40 0.0243744302351898 0.0487488604703796 0.97562556976481 41 0.0144174421438212 0.0288348842876423 0.985582557856179 42 0.0200514266994248 0.0401028533988495 0.979948573300575 43 0.182747442119952 0.365494884239904 0.817252557880048 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 8 0.296296296296296 NOK 10% type I error level 13 0.481481481481481 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/10tux61258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/10tux61258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/1j5ip1258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/1j5ip1258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/2awyf1258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/2awyf1258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/3hj061258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/3hj061258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/4k9ac1258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/4k9ac1258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/5lbg01258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/5lbg01258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/677ct1258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/677ct1258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/783qo1258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/783qo1258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/86ew71258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/86ew71258726861.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/9cisw1258726861.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258728032yjst5ejacf95yvf/9cisw1258726861.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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