Multiple Linear Regression met 1xlag 9

*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: Sat, 19 Dec 2009 06:56:41 -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/Dec/19/t1261231661a3n7rhkrezqkqng.htm/, Retrieved Sat, 19 Dec 2009 15:07:53 +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/Dec/19/t1261231661a3n7rhkrezqkqng.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:

kvn paper

Dataseries X:

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9487 1169 8700 2154 9627 2249 8947 2687 9283 4359 8829 5382 9947 4459 9628 6398 9318 4596 9605 3024 8640 1887 9214 2070 9567 1351 8547 2218 9185 2461 9470 3028 9123 4784 9278 4975 10170 4607 9434 6249 9655 4809 9429 3157 8739 1910 9552 2228 9784 1594 9089 2467 9763 2222 9330 3607 9144 4685 9895 4962 10404 5770 10195 5480 9987 5000 9789 3228 9437 1993 10096 2288 9776 1580 9106 2111 10258 2192 9766 3601 9826 4665 9957 4876 10036 5813 10508 5589 10146 5331 10166 3075 9365 2002 9968 2306 10123 1507 9144 1992 10447 2487 9699 3490 10451 4647 10192 5594 10404 5611 10597 5788 10633 6204 10727 3013 9784 1931 9667 2549 10297 1504 9426 2090 10274 2702 9598 2939 10400 4500 9985 6208 10761 6415 11081 5657 10297 5964 10751 3163 9760 1997 10133 2422 10806 1376 9734 2202 10083 2683 10691 3303 10446 5202 10517 5231 11353 4880 10436 7998 10721 4977 10701 3531 9793 2025 10142 2205

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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 9377.3192435779 + 0.194844740786332X[t] + 319.219351869687M1[t] -694.785640883456M2[t] + 97.740582935755M3[t] -364.920329651367M4[t] -481.046525992848M5[t] -605.987816434113M6[t] + 16.6244363891546M7[t] -310.219838948932M8[t] -295.757655712276M9[t] + 171.852236023613M10[t] -400.19652387905M11[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) 9377.3192435779 369.47296 25.3803 0 0 X 0.194844740786332 0.137895 1.413 0.162025 0.081013 M1 319.219351869687 294.195856 1.0851 0.281567 0.140783 M2 -694.785640883456 270.021184 -2.5731 0.01217 0.006085 M3 97.740582935755 270.140174 0.3618 0.718565 0.359283 M4 -364.920329651367 299.130205 -1.2199 0.226525 0.113263 M5 -481.046525992848 426.413834 -1.1281 0.263066 0.131533 M6 -605.987816434113 496.380465 -1.2208 0.226196 0.113098 M7 16.6244363891546 501.802032 0.0331 0.973664 0.486832 M8 -310.219838948932 597.868895 -0.5189 0.605461 0.30273 M9 -295.757655712276 490.654276 -0.6028 0.548574 0.274287 M10 171.852236023613 295.280072 0.582 0.562412 0.281206 M11 -400.19652387905 273.378164 -1.4639 0.147637 0.073818 Multiple Linear Regression - Regression Statistics Multiple R 0.621383931711173 R-squared 0.386117990588836 Adjusted R-squared 0.282363284772864 F-TEST (value) 3.72145039159659 F-TEST (DF numerator) 12 F-TEST (DF denominator) 71 p-value 0.000227788697980236 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 504.227458280158 Sum Squared Residuals 18051418.4075405 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9487 9924.31209742676 -437.312097426764 2 8700 9102.22917434819 -402.22917434819 3 9627 9913.2656485421 -286.265648542104 4 8947 9535.9467324194 -588.946732419396 5 9283 9745.60094267266 -462.600942672662 6 8829 9819.98582205581 -990.985822055815 7 9947 10262.7563791333 -315.756379133297 8 9628 10313.7160561799 -685.716056179909 9 9318 9977.0680165196 -659.068016519594 10 9605 10138.3819757394 -533.381975739369 11 8640 9344.79474556265 -704.794745562646 12 9214 9780.6478570056 -566.647857005595 13 9567 9959.7738402499 -392.773840249909 14 8547 9114.69923775852 -567.699237758516 15 9185 9954.5727335888 -769.572733588806 16 9470 9602.38878902753 -132.388789027535 17 9123 9828.40995750685 -705.409957506853 18 9278 9740.68401255578 -462.684012555777 19 10170 10291.5934007697 -121.593400769674 20 9434 10284.6841898027 -850.684189802746 21 9655 10018.5699463071 -363.569946307083 22 9429 10164.2963262640 -735.296326263951 23 8739 9349.27617460073 -610.276174600732 24 9552 9811.43332604984 -259.433326049836 25 9784 10007.1211122610 -223.121112260987 26 9089 9163.21557821431 -74.2155782143124 27 9763 9908.00484054087 -145.004840540873 28 9330 9715.20389394282 -385.203893942821 29 9144 9809.120328169 -665.120328169006 30 9895 9738.15103092556 156.848969074445 31 10404 10518.1978343042 -114.197834304179 32 10195 10134.8485841381 60.1514158619441 33 9987 10055.7852917973 -68.7852917972726 34 9789 10178.1303028598 -389.130302859781 35 9437 9365.448288086 71.5517119140023 36 10096 9823.12401049701 272.875989502984 37 9776 10004.3932858900 -228.393285889979 38 9106 9093.85085049438 12.1491495056219 39 10258 9902.15949831728 355.840501682718 40 9766 9714.0348254981 51.9651745018972 41 9826 9805.22343335328 20.7765666467205 42 9957 9721.39438321793 235.605616782070 43 10036 10526.576158158 -490.576158157991 44 10508 10156.0866608838 351.913339116234 45 10146 10120.2789009975 25.7210990024515 46 10166 10148.3190575195 17.6809424805280 47 9365 9367.20189075307 -2.20189075307489 48 9968 9826.63121583117 141.368784168831 49 10123 9990.16961981258 132.830380187424 50 9144 9070.6643263408 73.3356736591955 51 10447 9959.63869684925 487.361303150749 52 9699 9692.40705927082 6.59294072918005 53 10451 9801.71622801913 649.283771980874 54 10192 9861.29290710252 330.707092897483 55 10404 10487.2175205192 -83.2175205191521 56 10597 10194.8607643002 402.139235699754 57 10633 10290.3783597040 342.621640295983 58 10727 10136.2386835907 590.761316409281 59 9784 9353.36791415724 430.632085842755 60 9667 9873.97848784225 -206.978487842248 61 10297 9989.58508559022 307.414914409783 62 9426 9089.75911093786 336.240889062135 63 10274 10001.5303161183 272.469683881688 64 9598 9585.04760709755 12.9523929024491 65 10400 9773.07405112353 626.925948876466 66 9985 9980.92757794533 4.07242205467463 67 10761 10643.8726921114 117.127307888637 68 11081 10169.3361032572 911.663896742763 69 10297 10243.6156219153 53.3843780847029 70 10751 10165.4653947087 585.534605291331 71 9760 9366.22766704914 393.772332950857 72 10133 9849.23320576238 283.766794237616 73 10806 9964.64495876957 841.355041230433 74 9734 9111.58172190593 622.418278094066 75 10083 9997.82826604337 85.1717339566282 76 10691 9655.97109274378 1035.02890725622 77 10446 9909.85505915554 536.14494084446 78 10517 9790.56426619708 726.435733802922 79 11353 10344.7860150043 1008.21398499566 80 10436 10625.4676414380 -189.467641438041 81 10721 10051.3038627592 669.696137240813 82 10701 10237.1682593180 463.83174068196 83 9793 9371.68331979116 421.316680208839 84 10142 9806.95189701175 335.04810298825 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.224370961433045 0.448741922866091 0.775629038566955 17 0.144159698320091 0.288319396640182 0.85584030167991 18 0.123781286366600 0.247562572733200 0.8762187136334 19 0.0792444379636867 0.158488875927373 0.920755562036313 20 0.0615474457181716 0.123094891436343 0.938452554281828 21 0.0550326798015203 0.110065359603041 0.94496732019848 22 0.0434507295669407 0.0869014591338814 0.95654927043306 23 0.0322296084500884 0.0644592169001768 0.967770391549912 24 0.0285384206549975 0.0570768413099951 0.971461579345003 25 0.0215867810755321 0.0431735621510643 0.978413218924468 26 0.0244749734421714 0.0489499468843427 0.975525026557829 27 0.0291890036925535 0.058378007385107 0.970810996307446 28 0.0205365239358798 0.0410730478717596 0.97946347606412 29 0.0294409096483986 0.0588818192967971 0.970559090351601 30 0.153610025903611 0.307220051807223 0.846389974096389 31 0.120170000578354 0.240340001156708 0.879829999421646 32 0.237369902341412 0.474739804682824 0.762630097658588 33 0.287661852868500 0.575323705736999 0.7123381471315 34 0.34606487556097 0.69212975112194 0.65393512443903 35 0.457172564392254 0.914345128784508 0.542827435607746 36 0.523819401080113 0.952361197839774 0.476180598919887 37 0.533321919433508 0.933356161132985 0.466678080566492 38 0.51564397927494 0.96871204145012 0.48435602072506 39 0.590891998311507 0.818216003376987 0.409108001688493 40 0.580257711117693 0.839484577764613 0.419742288882307 41 0.68549325883416 0.629013482331681 0.314506741165841 42 0.732737038462757 0.534525923074486 0.267262961537243 43 0.80752468027829 0.384950639443422 0.192475319721711 44 0.852773941577609 0.294452116844783 0.147226058422392 45 0.875111110206413 0.249777779587173 0.124888889793587 46 0.9182999301688 0.163400139662399 0.0817000698311994 47 0.923080910165946 0.153838179668107 0.0769190898340536 48 0.898966473992785 0.202067052014429 0.101033526007215 49 0.904792781672414 0.190414436655172 0.095207218327586 50 0.910040141136665 0.179919717726671 0.0899598588633354 51 0.914681124672247 0.170637750655507 0.0853188753277533 52 0.906747760064105 0.186504479871790 0.0932522399358952 53 0.928964585188937 0.142070829622126 0.0710354148110631 54 0.91394726573387 0.172105468532262 0.0860527342661309 55 0.9441760811217 0.111647837756599 0.0558239188782993 56 0.950597591699709 0.0988048166005817 0.0494024083002909 57 0.94637069796032 0.107258604079360 0.0536293020396799 58 0.941928769172605 0.116142461654790 0.0580712308273949 59 0.921546306936948 0.156907386126105 0.0784536930630525 60 0.910894242894332 0.178211514211336 0.0891057571056679 61 0.90799417899126 0.184011642017481 0.0920058210087406 62 0.884776339821227 0.230447320357545 0.115223660178773 63 0.830560709651137 0.338878580697725 0.169439290348863 64 0.99959394890197 0.000812102196059899 0.000406051098029949 65 0.999768531648946 0.000462936702108012 0.000231468351054006 66 0.999778064677923 0.000443870644154239 0.000221935322077120 67 0.999323475529247 0.00135304894150632 0.000676524470753158 68 0.99759091401496 0.00481817197008008 0.00240908598504004 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0943396226415094 NOK 5% type I error level 8 0.150943396226415 NOK 10% type I error level 14 0.264150943396226 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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