2e link

*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 08:46:37 -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/t1258733524yv3zh41laubgqjy.htm/, Retrieved Fri, 20 Nov 2009 17:12:15 +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/t1258733524yv3zh41laubgqjy.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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130 87.1 136.7 110.5 138.1 110.8 139.5 104.2 140.4 88.9 144.6 89.8 151.4 90 147.9 93.9 141.5 91.3 143.8 87.8 143.6 99.7 150.5 73.5 150.1 79.2 154.9 96.9 162.1 95.2 176.7 95.6 186.6 89.7 194.8 92.8 196.3 88 228.8 101.1 267.2 92.7 237.2 95.8 254.7 103.8 258.2 81.8 257.9 87.1 269.6 105.9 266.9 108.1 269.6 102.6 253.9 93.7 258.6 103.5 274.2 100.6 301.5 113.3 304.5 102.4 285.1 102.1 287.7 106.9 265.5 87.3 264.1 93.1 276.1 109.1 258.9 120.3 239.1 104.9 250.1 92.6 276.8 109.8 297.6 111.4 295.4 117.9 283 121.6 275.8 117.8 279.7 124.2 254.6 106.8 234.6 102.7 176.9 116.8 148.1 113.6 122.7 96.1 124.9 85 121.6 83.2 128.4 84.9 144.5 83 151.8 79.6 167.1 83.2 173.8 83.8 203.7 82.8 199.8 71.4

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 Multiple Linear Regression - Estimated Regression Equation Y[t] = -118.138386770027 + 3.78371344759034X[t] -19.2118191206132M1[t] -99.749746567234M2[t] -114.917254356113M3[t] -86.954702524727M4[t] -45.2971407566303M5[t] -59.7821994116779M6[t] -46.7920522368219M7[t] -59.1964986084116M8[t] -37.3590286359412M9[t] -44.9661323364949M10[t] -63.3430477153376M11[t] + 0.488172121119932t + 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) -118.138386770027 58.247416 -2.0282 0.048227 0.024113 X 3.78371344759034 0.62439 6.0599 0 0 M1 -19.2118191206132 29.603294 -0.649 0.519513 0.259756 M2 -99.749746567234 33.992088 -2.9345 0.005154 0.002577 M3 -114.917254356113 34.398262 -3.3408 0.001644 0.000822 M4 -86.954702524727 32.340004 -2.6888 0.009892 0.004946 M5 -45.2971407566303 31.063714 -1.4582 0.151436 0.075718 M6 -59.7821994116779 31.533311 -1.8958 0.064139 0.032069 M7 -46.7920522368219 31.405516 -1.4899 0.142923 0.071462 M8 -59.1964986084116 32.406213 -1.8267 0.074099 0.03705 M9 -37.3590286359412 31.67213 -1.1796 0.244113 0.122057 M10 -44.9661323364949 31.627685 -1.4217 0.161706 0.080853 M11 -63.3430477153376 32.699021 -1.9372 0.05875 0.029375 t 0.488172121119932 0.362647 1.3461 0.184716 0.092358 Multiple Linear Regression - Regression Statistics Multiple R 0.704334813036706 R-squared 0.496087528855452 Adjusted R-squared 0.356707483645258 F-TEST (value) 3.55924356393571 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 0.000665857165168315 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 48.7928286326505 Sum Squared Residuals 111894.785920834 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 130 192.699407515599 -62.6994075155985 2 136.7 201.188546863712 -64.488546863712 3 138.1 187.64432523023 -49.5443252302301 4 139.5 191.122540428640 -51.6225404286396 5 140.4 175.377458569724 -34.9774585697241 6 144.6 164.785914138628 -20.1859141386278 7 151.4 179.020976124122 -27.6209761241217 8 147.9 181.861184319254 -33.9611843192543 9 141.5 194.349171449110 -52.8491714491097 10 143.8 173.987242803110 -30.1872428031097 11 143.6 201.124689571712 -57.5246895717121 12 150.5 165.822617081303 -15.3226170813026 13 150.1 168.666136733074 -18.5661367330744 14 154.9 155.588109429923 -0.688109429922506 15 162.1 134.47646090126 27.6235390987401 16 176.7 164.440670232802 12.2593297671981 17 186.6 184.262494781236 2.33750521876447 18 194.8 181.995119934838 12.8048800651620 19 196.3 177.311614682380 18.9883853176198 20 228.8 214.961986595344 13.8380134046560 21 267.2 205.504435729175 61.6955642708246 22 237.2 210.115015837272 27.0849841627283 23 254.7 222.495980160272 32.2040198397283 24 258.2 203.085504149742 55.1144958502583 25 257.9 204.415538422477 53.4844615775228 26 269.6 195.499595911675 74.1004040883253 27 266.9 189.144429828614 77.7555701713855 28 269.6 196.784729819373 72.8152701806266 29 253.9 205.255414025036 48.6445859749639 30 258.6 228.338919277494 30.2610807225062 31 274.2 230.844469575458 43.3555304245423 32 301.5 266.981356109385 34.5186438906147 33 304.5 248.064521624241 56.4354783757591 34 285.1 239.81047601053 45.28952398947 35 287.7 240.083557301241 47.6164426987591 36 265.5 229.753993564928 35.7460064350723 37 264.1 232.975884561458 31.1241154385416 38 276.1 213.465544397403 62.634455602597 39 258.9 241.163799342656 17.7362006573442 40 239.1 211.345336202270 27.7546637977295 41 250.1 206.951394686126 43.1486053138742 42 276.8 258.034379450752 18.7656205492478 43 297.6 277.566640262873 20.0333597371274 44 295.4 290.24450342174 5.15549657825988 45 283 326.569885271415 -43.5698852714146 46 275.8 305.072842591138 -29.2728425911376 47 279.7 311.399865397993 -31.699865397993 48 254.6 309.394471246379 -54.7944712463785 49 234.6 275.157599111765 -40.5575991117649 50 176.9 248.458203397288 -71.5582033972878 51 148.1 221.670984697240 -73.5709846972397 52 122.7 183.906723316915 -61.2067233169146 53 124.9 184.053237937878 -59.1532379378784 54 121.6 163.245667198288 -41.6456671982883 55 128.4 183.156299355168 -54.7562993551677 56 144.5 164.050969554276 -19.5509695542763 57 151.8 173.511985926059 -21.7119859260594 58 167.1 180.014422757951 -12.9144227579510 59 173.8 164.395907568782 9.40409243121765 60 203.7 224.443413957650 -20.7434139576495 61 199.8 162.585433655626 37.2145663443736 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00676498977850726 0.0135299795570145 0.993235010221493 18 0.0010821720281339 0.0021643440562678 0.998917827971866 19 0.000194793220962173 0.000389586441924346 0.999805206779038 20 0.000441910606938794 0.000883821213877588 0.999558089393061 21 0.0634593325041474 0.126918665008295 0.936540667495853 22 0.0426346455642919 0.0852692911285838 0.957365354435708 23 0.0574275798753063 0.114855159750613 0.942572420124694 24 0.0387378436113169 0.0774756872226337 0.961262156388683 25 0.0327091883473940 0.0654183766947881 0.967290811652606 26 0.0194971395076855 0.038994279015371 0.980502860492314 27 0.0099903005777989 0.0199806011555978 0.990009699422201 28 0.00482361992655275 0.0096472398531055 0.995176380073447 29 0.00463134725042511 0.00926269450085022 0.995368652749575 30 0.0140344279376089 0.0280688558752179 0.98596557206239 31 0.0114451386560649 0.0228902773121298 0.988554861343935 32 0.00767768459387959 0.0153553691877592 0.99232231540612 33 0.00440661545737513 0.00881323091475026 0.995593384542625 34 0.00282097721459963 0.00564195442919927 0.9971790227854 35 0.00174346001154728 0.00348692002309456 0.998256539988453 36 0.0055894332699196 0.0111788665398392 0.99441056673008 37 0.0777919474576843 0.155583894915369 0.922208052542316 38 0.0628941930155178 0.125788386031036 0.937105806984482 39 0.094928388625246 0.189856777250492 0.905071611374754 40 0.115351286326832 0.230702572653665 0.884648713673168 41 0.0933042305050437 0.186608461010087 0.906695769494956 42 0.113144804957674 0.226289609915348 0.886855195042326 43 0.435526568029189 0.871053136058379 0.564473431970811 44 0.681969365090276 0.636061269819449 0.318030634909724 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.285714285714286 NOK 5% type I error level 15 0.535714285714286 NOK 10% type I error level 18 0.642857142857143 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/10rpi71258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/10rpi71258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/1e7fh1258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/1e7fh1258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/2mpp01258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/2mpp01258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/38wi31258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/38wi31258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/4jmob1258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/4jmob1258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/51qrw1258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/51qrw1258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/6wcj21258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/6wcj21258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/7d4f81258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/7d4f81258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/8y10t1258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/8y10t1258731992.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/9z75g1258731992.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258733524yv3zh41laubgqjy/9z75g1258731992.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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