*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: Wed, 22 Dec 2010 15:48:15 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq.htm/, Retrieved Wed, 22 Dec 2010 16:46:46 +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/2010/Dec/22/t1293032803nlo9dyntqrz6goq.htm/}, year = {2010}, } @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 = {2010}, 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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1143,94 9,70 1227,85 9,20 1261,26 9,90 1408,95 10,00 1162,58 9,90 1259,39 9,20 1253,85 9,60 1475,32 9,40 1211,75 9,10 1303,83 8,70 1299,37 9,50 1430,73 9,50 1244,95 9,40 1318,58 9,00 1318,74 9,60 1525,05 9,30 1275,88 9,00 1360,09 8,50 1349,81 8,50 1574,04 7,90 1294,58 7,20 1380,60 6,50 1369,22 7,10 1565,98 6,80 1338,96 6,20 1457,57 6,20 1456,21 6,50 1654,44 7,50 1428,47 7,40 1530,39 6,90 1514,13 7,60 1698,25 8,10 1454,22 8,20 1578,06 7,70 1526,53 8,30 1714,21 8,50 1492,86 8,70 1593,42 7,40 1555,50 9,10 1820,55 8,40 1534,57 8,60 1636,03 8,10 1594,58 8,70 1805,13 8,50 1565,37 8,70 1679,57 8,30 1638,26 8,10 1854,64 7,90 1628,72 8,00 1744,97 7,60 1694,35 7,30 1920,88 7,10 1680,26 7,10 1778,62 6,30 1740,89 7,70 2010,56 6,80

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 5 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk 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 LOONKOSTEN[t] = + 2464.07940269401 -95.405199980261`WERKLOOSHEIDSGRAAD `[t] -275.608014287829Q1[t] -227.980837134257Q2[t] -193.878617145395Q3[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) 2464.07940269401 165.847642 14.8575 0 0 `WERKLOOSHEIDSGRAAD ` -95.405199980261 19.486685 -4.8959 1e-05 5e-06 Q1 -275.608014287829 56.082072 -4.9144 1e-05 5e-06 Q2 -227.980837134257 56.682716 -4.0221 0.000191 9.6e-05 Q3 -193.878617145395 56.099169 -3.456 0.001114 0.000557 Multiple Linear Regression - Regression Statistics Multiple R 0.710256941823706 R-squared 0.504464923408763 Adjusted R-squared 0.465599427205529 F-TEST (value) 12.9797628408198 F-TEST (DF numerator) 4 F-TEST (DF denominator) 51 p-value 2.32424157142752e-07 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 148.276353945835 Sum Squared Residuals 1121279.73411301 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1143.94 1263.04094859765 -119.100948597651 2 1227.85 1358.37072574136 -130.520725741357 3 1261.26 1325.68930574403 -64.4293057440351 4 1408.95 1510.0274028914 -101.077402891404 5 1162.58 1243.9599086016 -81.3799086016012 6 1259.39 1358.37072574136 -98.9807257413562 7 1253.85 1354.31086573811 -100.460865738114 8 1475.32 1567.27052287956 -91.9505228795607 9 1211.75 1320.28406858581 -108.53406858581 10 1303.83 1406.07332573149 -102.243325731487 11 1299.37 1363.85138573614 -64.4813857361397 12 1430.73 1557.73000288153 -127.000002881535 13 1244.95 1291.66250859173 -46.7125085917316 14 1318.58 1377.45176573741 -58.8717657374085 15 1318.74 1354.31086573811 -35.5708657381135 16 1525.05 1576.81104287759 -51.7610428775868 17 1275.88 1329.82458858384 -53.9445885838359 18 1360.09 1425.15436572754 -65.0643657275391 19 1349.81 1459.2565857164 -109.446585716401 20 1574.04 1710.37832284995 -136.338322849952 21 1294.58 1501.55394854831 -206.973948548306 22 1380.6 1615.96476568806 -235.364765688061 23 1369.22 1592.82386568877 -223.603865688766 24 1565.98 1815.32404282824 -249.344042828239 25 1338.96 1596.95914852857 -257.999148528567 26 1457.57 1644.58632568214 -187.016325682139 27 1456.21 1650.06698567692 -193.856985676923 28 1654.44 1748.54040284206 -94.1004028420566 29 1428.47 1482.47290855225 -54.0029085522537 30 1530.39 1577.80268569596 -47.4126856959565 31 1514.13 1545.12126569864 -30.9912656986354 32 1698.25 1691.2972828539 6.95271714609993 33 1454.22 1406.14874856804 48.0712514319551 34 1578.06 1501.47852571175 76.5814742882521 35 1526.53 1478.33762571245 48.1923742875472 36 1714.21 1653.1352028618 61.0747971382044 37 1492.86 1358.44614857791 134.413851422085 38 1593.42 1530.10008570583 63.319914294174 39 1555.5 1402.01346572824 153.486534271756 40 1820.55 1662.67572285982 157.874277140178 41 1534.57 1367.98666857594 166.583331424059 42 1636.03 1463.31644571964 172.713554280357 43 1594.58 1440.17554572035 154.404454279651 44 1805.13 1653.1352028618 151.994797138205 45 1565.37 1358.44614857791 206.923851422085 46 1679.57 1444.23540572359 235.334594276409 47 1638.26 1497.4186657085 140.841334291495 48 1854.64 1710.37832284995 144.261677150048 49 1628.72 1425.2297885641 203.490211435903 50 1744.97 1511.01904570977 233.950954290226 51 1694.35 1573.74282569271 120.607174307286 52 1920.88 1786.70248283416 134.177517165839 53 1680.26 1511.09446854633 169.165531453668 54 1778.62 1635.04580568411 143.574194315887 55 1740.89 1535.58074570061 205.309254299391 56 2010.56 1815.32404282824 195.235957171761 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.00547456262533053 0.0109491252506611 0.99452543737467 9 0.00075675048975666 0.00151350097951332 0.999243249510243 10 0.000129656617662733 0.000259313235325465 0.999870343382337 11 2.2499244923164e-05 4.49984898463279e-05 0.999977500755077 12 6.77048826123468e-06 1.35409765224694e-05 0.999993229511739 13 1.06411940971591e-05 2.12823881943183e-05 0.999989358805903 14 8.48363978337652e-06 1.6967279566753e-05 0.999991516360217 15 4.03398204149718e-06 8.06796408299435e-06 0.999995966017958 16 3.1851823892098e-06 6.37036477841961e-06 0.99999681481761 17 1.1820860512491e-06 2.3641721024982e-06 0.999998817913949 18 5.10132330625344e-07 1.02026466125069e-06 0.99999948986767 19 9.05603553623728e-07 1.81120710724746e-06 0.999999094396446 20 5.64918257965082e-07 1.12983651593016e-06 0.999999435081742 21 1.44309019087459e-06 2.88618038174918e-06 0.99999855690981 22 1.84284240777722e-06 3.68568481555443e-06 0.999998157157592 23 1.85101882010953e-06 3.70203764021905e-06 0.99999814898118 24 2.00360678605318e-06 4.00721357210635e-06 0.999997996393214 25 1.72007109722552e-06 3.44014219445105e-06 0.999998279928903 26 3.68458813555759e-06 7.36917627111518e-06 0.999996315411864 27 8.36858300560304e-06 1.67371660112061e-05 0.999991631416994 28 0.00012426141145395 0.000248522822907899 0.999875738588546 29 0.00316009913014026 0.00632019826028052 0.99683990086986 30 0.0419001019545861 0.0838002039091722 0.958099898045414 31 0.185025528899764 0.370051057799527 0.814974471100236 32 0.459253811871515 0.91850762374303 0.540746188128485 33 0.780045751329938 0.439908497340124 0.219954248670062 34 0.924485312367026 0.151029375265948 0.0755146876329742 35 0.973567245604886 0.052865508790228 0.026432754395114 36 0.992096554692874 0.0158068906142522 0.00790344530712611 37 0.99636080974237 0.00727838051526024 0.00363919025763012 38 0.999894000221082 0.000211999557836328 0.000105999778918164 39 0.999859864726022 0.000280270547955562 0.000140135273977781 40 0.999804314542922 0.000391370914155946 0.000195685457077973 41 0.999747954686291 0.000504090627417597 0.000252045313708798 42 0.999690249500376 0.000619500999248437 0.000309750499624219 43 0.999276552374424 0.00144689525115171 0.000723447625575856 44 0.998561662339838 0.00287667532032358 0.00143833766016179 45 0.996262862701261 0.00747427459747787 0.00373713729873894 46 0.990151016941952 0.019697966116097 0.00984898305804848 47 0.976757910034447 0.0464841799311058 0.0232420899655529 48 0.961257927088334 0.0774841458233314 0.0387420729116657 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.73170731707317 NOK 5% type I error level 34 0.829268292682927 NOK 10% type I error level 37 0.902439024390244 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/10vvno1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/10vvno1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/1ouqc1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/1ouqc1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/2hlqf1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/2hlqf1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/3hlqf1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/3hlqf1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/4hlqf1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/4hlqf1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/5rvp01293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/5rvp01293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/6rvp01293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/6rvp01293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/7km6l1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/7km6l1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/8km6l1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/8km6l1293032887.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/9vvno1293032887.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293032803nlo9dyntqrz6goq/9vvno1293032887.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Quarterly 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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