paper dummys

*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, 17 Dec 2010 13:52:07 +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/17/t1292593940p87bjkoct64yk5r.htm/, Retrieved Fri, 17 Dec 2010 14:52:23 +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/17/t1292593940p87bjkoct64yk5r.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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100.00 0 100.42 0 100.50 0 101.14 0 101.98 0 102.31 0 103.27 0 103.80 0 103.46 0 105.06 0 106.08 0 106.74 0 107.35 0 108.96 0 109.85 0 109.81 0 109.99 0 111.60 0 112.74 0 112.78 0 113.66 0 115.37 0 116.26 0 116.24 0 116.73 0 118.76 0 119.78 0 120.23 0 121.48 0 124.07 0 125.82 0 126.92 0 128.48 0 131.44 0 133.51 0 134.58 0 136.68 0 140.10 0 142.45 0 143.91 0 146.19 0 149.84 0 152.31 0 153.62 0 155.79 0 159.89 0 163.21 0 165.32 0 167.68 0 171.79 0 175.38 0 177.81 0 181.09 0 186.48 0 191.07 0 194.23 0 197.82 0 204.41 0 209.26 0 212.24 0 214.88 0 218.87 0 219.86 0 219.75 0 220.89 0 224.02 0 222.27 0 217.27 1 213.23 1 212.44 1 207.87 1 199.46 1 198.19 1 199.77 1 200.10 1 195,76 1 191,27 1 195,79 1 192,7 1

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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation woningprijsindex_us[t] = + 78.4441884324657 -19.859749918121Dummy_[t] + 1.95106206102932t + 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) 78.4441884324657 2.97468 26.3706 0 0 Dummy_ -19.859749918121 4.828281 -4.1132 9.8e-05 4.9e-05 t 1.95106206102932 0.075996 25.6732 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.959769602367644 R-squared 0.921157689628946 Adjusted R-squared 0.919082891987602 F-TEST (value) 443.974714099082 F-TEST (DF numerator) 2 F-TEST (DF denominator) 76 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.0642601758941 Sum Squared Residuals 11061.5243929664 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 80.3952504934951 19.6047495065049 2 100.42 82.3463125545243 18.0736874454757 3 100.5 84.2973746155536 16.2026253844464 4 101.14 86.248436676583 14.891563323417 5 101.98 88.1994987376123 13.7805012623877 6 102.31 90.1505607986416 12.1594392013584 7 103.27 92.1016228596709 11.1683771403291 8 103.8 94.0526849207002 9.74731507929975 9 103.46 96.0037469817296 7.45625301827042 10 105.06 97.9548090427589 7.1051909572411 11 106.08 99.9058711037882 6.17412889621177 12 106.74 101.856933164818 4.88306683518245 13 107.35 103.807995225847 3.54200477415312 14 108.96 105.759057286876 3.2009427131238 15 109.85 107.710119347906 2.13988065209448 16 109.81 109.661181408935 0.148818591065158 17 109.99 111.612243469964 -1.62224346996418 18 111.6 113.563305530993 -1.9633055309935 19 112.74 115.514367592023 -2.77436759202282 20 112.78 117.465429653052 -4.68542965305214 21 113.66 119.416491714081 -5.75649171408148 22 115.37 121.367553775111 -5.99755377511079 23 116.26 123.31861583614 -7.05861583614012 24 116.24 125.269677897169 -9.02967789716945 25 116.73 127.220739958199 -10.4907399581988 26 118.76 129.171802019228 -10.4118020192281 27 119.78 131.122864080257 -11.3428640802574 28 120.23 133.073926141287 -12.8439261412867 29 121.48 135.024988202316 -13.5449882023161 30 124.07 136.976050263345 -12.9060502633454 31 125.82 138.927112324375 -13.1071123243747 32 126.92 140.878174385404 -13.958174385404 33 128.48 142.829236446433 -14.3492364464334 34 131.44 144.780298507463 -13.3402985074627 35 133.51 146.731360568492 -13.221360568492 36 134.58 148.682422629521 -14.1024226295213 37 136.68 150.633484690551 -13.9534846905507 38 140.1 152.58454675158 -12.48454675158 39 142.45 154.535608812609 -12.0856088126093 40 143.91 156.486670873639 -12.5766708736386 41 146.19 158.437732934668 -12.247732934668 42 149.84 160.388794995697 -10.5487949956973 43 152.31 162.339857056727 -10.0298570567266 44 153.62 164.290919117756 -10.6709191177559 45 155.79 166.241981178785 -10.4519811787853 46 159.89 168.193043239815 -8.3030432398146 47 163.21 170.144105300844 -6.9341053008439 48 165.32 172.095167361873 -6.77516736187324 49 167.68 174.046229422903 -6.36622942290255 50 171.79 175.997291483932 -4.20729148393189 51 175.38 177.948353544961 -2.5683535449612 52 177.81 179.899415605991 -2.08941560599052 53 181.09 181.85047766702 -0.760477667019837 54 186.48 183.801539728049 2.67846027195082 55 191.07 185.752601789078 5.3173982109215 56 194.23 187.703663850108 6.52633614989217 57 197.82 189.654725911137 8.16527408886285 58 204.41 191.605787972166 12.8042120278335 59 209.26 193.556850033196 15.7031499668042 60 212.24 195.507912094225 16.7320879057749 61 214.88 197.458974155254 17.4210258447456 62 218.87 199.410036216284 19.4599637837162 63 219.86 201.361098277313 18.4989017226869 64 219.75 203.312160338342 16.4378396616576 65 220.89 205.263222399372 15.6267776006283 66 224.02 207.214284460401 16.805715539599 67 222.27 209.16534652143 13.1046534785696 68 217.27 191.256658664339 26.0133413356613 69 213.23 193.207720725368 20.0222792746319 70 212.44 195.158782786397 17.2812172136026 71 207.87 197.109844847427 10.7601551525733 72 199.46 199.060906908456 0.399093091543993 73 198.19 201.011968969485 -2.82196896948534 74 199.77 202.963031030515 -3.19303103051465 75 200.1 204.914093091544 -4.814093091544 76 195.76 206.865155152573 -11.1051551525733 77 191.27 208.816217213603 -17.5462172136026 78 195.79 210.767279274632 -14.977279274632 79 192.7 212.718341335661 -20.0183413356613 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 1.64669767892782e-05 3.29339535785563e-05 0.99998353302321 7 9.99505191729243e-07 1.99901038345849e-06 0.999999000494808 8 3.29264214460411e-08 6.58528428920821e-08 0.999999967073579 9 5.3521649484616e-09 1.07043298969232e-08 0.999999994647835 10 4.97842769892793e-10 9.95685539785586e-10 0.999999999502157 11 1.02522821961037e-10 2.05045643922074e-10 0.999999999897477 12 1.08025863642543e-11 2.16051727285086e-11 0.999999999989197 13 7.87584922938774e-13 1.57516984587755e-12 0.999999999999212 14 6.18506744005807e-13 1.23701348801161e-12 0.999999999999381 15 2.22636352533418e-13 4.45272705066836e-13 0.999999999999777 16 1.64873547057416e-14 3.29747094114832e-14 0.999999999999983 17 1.35513505066878e-15 2.71027010133755e-15 0.999999999999999 18 1.55332514873598e-16 3.10665029747197e-16 1 19 3.27646351209948e-17 6.55292702419896e-17 1 20 2.43509510345977e-18 4.87019020691954e-18 1 21 1.86619569778387e-19 3.73239139556774e-19 1 22 7.72067557629546e-20 1.54413511525909e-19 1 23 2.54004802519238e-20 5.08009605038476e-20 1 24 2.01289191363934e-21 4.02578382727867e-21 1 25 1.46649677557205e-22 2.9329935511441e-22 1 26 5.30981564703314e-23 1.06196312940663e-22 1 27 2.12603950471969e-23 4.25207900943938e-23 1 28 2.79548844143416e-24 5.59097688286832e-24 1 29 7.02018823672896e-25 1.40403764734579e-24 1 30 2.0739058586075e-23 4.14781171721501e-23 1 31 6.63857932847925e-22 1.32771586569585e-21 1 32 3.56616403283738e-21 7.13232806567476e-21 1 33 1.79024741178903e-20 3.58049482357806e-20 1 34 9.41521800953994e-19 1.88304360190799e-18 1 35 2.69687044276253e-17 5.39374088552505e-17 1 36 1.39641648427973e-16 2.79283296855945e-16 1 37 8.15358264533715e-16 1.63071652906743e-15 1 38 1.70052657925076e-14 3.40105315850152e-14 0.999999999999983 39 2.32578512061968e-13 4.65157024123936e-13 0.999999999999767 40 1.20981962526933e-12 2.41963925053866e-12 0.99999999999879 41 5.76656586895903e-12 1.15331317379181e-11 0.999999999994233 42 5.23262275151579e-11 1.04652455030316e-10 0.999999999947674 43 3.52155938729298e-10 7.04311877458597e-10 0.999999999647844 44 1.21771956597598e-09 2.43543913195195e-09 0.99999999878228 45 3.91117055930639e-09 7.82234111861278e-09 0.99999999608883 46 2.11556276889887e-08 4.23112553779775e-08 0.999999978844372 47 1.23671941095673e-07 2.47343882191346e-07 0.999999876328059 48 5.67993751930802e-07 1.1359875038616e-06 0.999999432006248 49 2.53784237470376e-06 5.07568474940753e-06 0.999997462157625 50 1.45434092710866e-05 2.90868185421732e-05 0.999985456590729 51 8.86532008279876e-05 0.000177306401655975 0.999911346799172 52 0.000529901216015562 0.00105980243203112 0.999470098783984 53 0.00345897793093497 0.00691795586186994 0.996541022069065 54 0.0213045784605058 0.0426091569210117 0.978695421539494 55 0.0987220554460663 0.197444110892133 0.901277944553934 56 0.325909856157591 0.651819712315183 0.674090143842409 57 0.703835117627823 0.592329764744354 0.296164882372177 58 0.925773975175675 0.14845204964865 0.0742260248243251 59 0.986876627937225 0.0262467441255493 0.0131233720627746 60 0.998271343972243 0.00345731205551458 0.00172865602775729 61 0.999798984099247 0.000402031801505807 0.000201015900752903 62 0.999930534511206 0.000138930977588435 6.94654887942173e-05 63 0.999959359269266 8.12814614672177e-05 4.06407307336089e-05 64 0.999972706303873 5.45873922537004e-05 2.72936961268502e-05 65 0.999966072391895 6.78552162106406e-05 3.39276081053203e-05 66 0.99989507965288 0.000209840694242288 0.000104920347121144 67 0.999655284935374 0.000689430129252597 0.000344715064626299 68 0.999275650926025 0.0014486981479492 0.000724349073974602 69 0.998174012881239 0.00365197423752249 0.00182598711876124 70 0.998308848372794 0.00338230325441224 0.00169115162720612 71 0.99850970527417 0.00298058945165984 0.00149029472582992 72 0.993787361985224 0.0124252760295529 0.00621263801477647 73 0.97922671960628 0.0415465607874405 0.0207732803937202 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 60 0.88235294117647 NOK 5% type I error level 64 0.941176470588235 NOK 10% type I error level 64 0.941176470588235 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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