*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, 19 Nov 2010 14:12:16 +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/Nov/19/t1290176147jdc3sx8eqhmvuyx.htm/, Retrieved Fri, 19 Nov 2010 15:16:04 +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/Nov/19/t1290176147jdc3sx8eqhmvuyx.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:

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No (this computation is public)

User-defined keywords:

Dataseries X:

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6 101,82 107,34 93,63 101,76 6 101,68 107,34 93,63 102,37 6 101,68 107,34 93,63 102,38 6 102,45 107,34 96,13 102,86 6 102,45 107,34 96,13 102,87 6 102,45 107,34 96,13 102,92 6 102,45 107,34 96,13 102,95 6 102,45 107,34 96,13 103,02 6 102,45 112,60 96,13 104,08 6 102,52 112,60 96,13 104,16 6 102,52 112,60 96,13 104,24 6 102,85 112,60 96,13 104,33 7 102,85 112,61 96,13 104,73 7 102,85 112,61 96,13 104,86 7 103,25 112,61 96,13 105,03 7 103,25 112,61 98,73 105,62 7 103,25 112,61 98,73 105,63 7 103,25 112,61 98,73 105,63 7 104,45 112,61 98,73 105,94 7 104,45 112,61 98,73 106,61 7 104,45 118,65 98,73 107,69 7 104,80 118,65 98,73 107,78 7 104,80 118,65 98,73 107,93 7 105,29 118,65 98,73 108,48 8 105,29 114,29 98,73 108,14 8 105,29 114,29 98,73 108,48 8 105,29 114,29 98,73 108,48 8 106,04 114,29 101,67 108,89 8 105,94 114,29 101,67 108,93 8 105,94 114,29 101,67 109,21 8 105,94 114,29 101,67 109,47 8 106,28 114,29 101,67 109,80 8 106,48 123,33 101,67 111,73 8 107,19 123,33 101,67 111,8 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 11 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Cultuuruitgaves[t] = + 60.4450364106322 + 0.0764799565278573Jaar[t] + 0.103348458934701Bioscoop[t] + 0.171281216338491Schouwburg[t] + 0.123399646201346Eendagattractie[t] + 0.170625237603976t + 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) 60.4450364106322 3.901815 15.4915 0 0 Jaar 0.0764799565278573 0.154562 0.4948 0.622812 0.311406 Bioscoop 0.103348458934701 0.018262 5.6592 1e-06 0 Schouwburg 0.171281216338491 0.020692 8.2777 0 0 Eendagattractie 0.123399646201346 0.032198 3.8326 0.000344 0.000172 t 0.170625237603976 0.02084 8.1872 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998655916446072 R-squared 0.997313639452744 Adjusted R-squared 0.99705533555397 F-TEST (value) 3861.00885113983 F-TEST (DF numerator) 5 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.302520033540805 Sum Squared Residuals 4.75895527606356 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.76 101.536716111741 0.223283888259383 2 102.37 101.692872565093 0.677127434906529 3 102.38 101.863497802697 0.516502197302551 4 102.86 102.422200469185 0.437799530815496 5 102.87 102.592825706788 0.277174293211525 6 102.92 102.763450944392 0.156549055607549 7 102.95 102.934076181996 0.0159238180035727 8 103.02 103.104701419600 -0.0847014196004097 9 104.08 104.176265855145 -0.0962658551448465 10 104.16 104.354125484874 -0.194125484874252 11 104.24 104.524750722478 -0.284750722478229 12 104.33 104.729480951531 -0.399480951530652 13 104.73 104.978298957826 -0.248298957825866 14 104.86 105.148924195430 -0.288924195429846 15 105.03 105.360888816608 -0.3308888166077 16 105.62 105.852353134335 -0.232353134335175 17 105.63 106.022978371939 -0.392978371939159 18 105.63 106.193603609543 -0.563603609543134 19 105.94 106.488246997869 -0.54824699786875 20 106.61 106.658872235473 -0.0488722354727233 21 107.69 107.864036019761 -0.17403601976119 22 107.78 108.070833217992 -0.290833217992307 23 107.93 108.241458455596 -0.311458455596277 24 108.48 108.462724438078 0.0172755619217406 25 108.14 107.963043528974 0.176956471025727 26 108.48 108.133668766578 0.346331233421755 27 108.48 108.304294004182 0.175705995817780 28 108.89 108.915225545819 -0.0252255458191837 29 108.93 109.075515937530 -0.145515937529682 30 109.21 109.246141175134 -0.0361411751336705 31 109.47 109.416766412738 0.053233587262359 32 109.8 109.622530126379 0.177469873620583 33 111.73 111.362207251470 0.367792748529712 34 111.85 111.606209894918 0.24379010508209 35 112.12 111.875016168510 0.244983831490159 36 112.15 112.053909282829 0.096090717171408 37 112.17 112.301014476960 -0.131014476960428 38 112.67 112.511945613549 0.158054386451063 39 112.8 112.682570851153 0.117429148847083 40 113.44 113.626911870439 -0.186911870439335 41 113.53 113.797537108043 -0.267537108043307 42 114.53 114.014669152168 0.515330847832102 43 114.51 114.185294389772 0.324705610228131 44 115.05 114.755878163453 0.294121836546853 45 116.67 116.203550349686 0.4664496503135 46 117.07 116.610070951771 0.459929048229178 47 116.92 116.780696189375 0.139303810625211 48 117 116.981292480070 0.0187075199301697 49 117.02 117.240799489274 -0.220799489273832 50 117.35 117.441395779969 -0.0913957799688716 51 117.36 117.624422832645 -0.264422832645006 52 117.82 118.096606081178 -0.276606081177636 53 117.88 118.277566164675 -0.39756616467508 54 118.24 118.502966085514 -0.262966085514448 55 118.5 118.675658292297 -0.175658292297111 56 118.8 118.846283529901 -0.0462835299010896 57 119.76 119.679767074735 0.08023292526498 58 120.09 119.850392312339 0.239607687661003 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.00109593735626365 0.00219187471252729 0.998904062643736 10 0.0553500914830609 0.110700182966122 0.94464990851694 11 0.0331771019266029 0.0663542038532058 0.966822898073397 12 0.183718036959414 0.367436073918829 0.816281963040586 13 0.109250944504299 0.218501889008599 0.8907490554957 14 0.0605769691129721 0.121153938225944 0.939423030887028 15 0.0498720843265786 0.0997441686531572 0.950127915673421 16 0.0290554067796489 0.0581108135592978 0.97094459322035 17 0.0150913922250498 0.0301827844500997 0.98490860777495 18 0.0114012686273410 0.0228025372546821 0.98859873137266 19 0.0119224336388372 0.0238448672776743 0.988077566361163 20 0.127630714012922 0.255261428025844 0.872369285987078 21 0.160502101770877 0.321004203541754 0.839497898229123 22 0.149828076179372 0.299656152358744 0.850171923820628 23 0.226253080228868 0.452506160457736 0.773746919771132 24 0.357585726756749 0.715171453513499 0.642414273243251 25 0.396793864266613 0.793587728533225 0.603206135733387 26 0.592131030882523 0.815737938234954 0.407868969117477 27 0.597196025363354 0.805607949273291 0.402803974636646 28 0.515568231568687 0.968863536862625 0.484431768431313 29 0.444367753427589 0.888735506855179 0.555632246572411 30 0.393306430740601 0.786612861481201 0.6066935692594 31 0.383066266836415 0.76613253367283 0.616933733163585 32 0.369814098952136 0.739628197904271 0.630185901047864 33 0.575548341902918 0.848903316194164 0.424451658097082 34 0.503987930705755 0.99202413858849 0.496012069294245 35 0.484630818052315 0.96926163610463 0.515369181947685 36 0.644299460364117 0.711401079271767 0.355700539635883 37 0.737512866913324 0.524974266173351 0.262487133086676 38 0.67258861186163 0.65482277627674 0.32741138813837 39 0.661045021888071 0.677909956223857 0.338954978111929 40 0.654183117502891 0.691633764994218 0.345816882497109 41 0.95565568059338 0.0886886388132386 0.0443443194066193 42 0.957213445447128 0.0855731091057442 0.0427865545528721 43 0.955302179180063 0.089395641639874 0.044697820819937 44 0.946516296615344 0.106967406769312 0.0534837033846558 45 0.919096359337591 0.161807281324818 0.0809036406624088 46 0.989023991396503 0.0219520172069931 0.0109760086034966 47 0.98304120821809 0.0339175835638194 0.0169587917819097 48 0.954322767284872 0.0913544654302553 0.0456772327151276 49 0.897117813399803 0.205764373200393 0.102882186600197 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.024390243902439 NOK 5% type I error level 6 0.146341463414634 NOK 10% type I error level 13 0.317073170731707 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/10qrll1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/10qrll1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/11por1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/11por1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/21por1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/21por1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/3tznu1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/3tznu1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/4tznu1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/4tznu1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/5tznu1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/5tznu1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/64q4x1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/64q4x1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/74q4x1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/74q4x1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/8fzli1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/8fzli1290175924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/9fzli1290175924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/19/t1290176147jdc3sx8eqhmvuyx/9fzli1290175924.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal 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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