WS 4: Toevoeging van een trend

*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: Thu, 02 Dec 2010 17:54:13 +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/02/t1291312377zlinnjxxzag95h1.htm/, Retrieved Thu, 02 Dec 2010 18:53:08 +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/02/t1291312377zlinnjxxzag95h1.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:

» Textbox « » Textfile « » CSV «

9 13 13 14 13 3 1 1 0 9 12 12 8 13 5 1 0 0 9 15 10 12 16 6 0 0 0 9 12 9 7 12 6 2 0 1 9 10 10 10 11 5 0 1 2 9 12 12 7 12 3 0 0 1 9 15 13 16 18 8 1 1 1 9 9 12 11 11 4 1 0 0 9 12 12 14 14 4 4 0 0 9 11 6 6 9 4 0 0 0 9 11 5 16 14 6 0 2 1 9 11 12 11 12 6 2 0 0 9 15 11 16 11 5 0 2 2 9 7 14 12 12 4 1 1 1 9 11 14 7 13 6 0 1 0 9 11 12 13 11 4 0 0 1 9 10 12 11 12 6 1 1 0 9 14 11 15 16 6 2 0 1 9 10 11 7 9 4 1 0 0 9 6 7 9 11 4 1 0 0 9 11 9 7 13 2 0 1 1 9 15 11 14 15 7 1 2 0 9 11 11 15 10 5 1 2 1 9 12 12 7 11 4 2 0 0 9 14 12 15 13 6 1 0 0 9 15 11 17 16 6 1 1 0 9 9 11 15 15 7 1 1 0 9 13 8 14 14 5 2 2 0 9 13 9 14 14 6 0 0 2 9 16 12 8 14 4 1 1 1 9 13 10 8 8 4 0 1 2 9 12 10 14 13 7 1 1 1 9 14 12 14 15 7 1 2 1 9 11 8 8 13 4 0 2 0 9 9 12 11 11 4 1 1 0 9 16 11 16 15 6 2 2 0 9 12 12 10 15 6 1 1 1 9 10 7 8 9 5 1 1 2 9 13 11 14 13 6 1 0 1 9 16 11 16 16 7 1 3 1 9 14 12 13 13 6 0 1 2 9 15 9 5 11 3 1 0 0 9 5 15 8 12 3 1 0 0 9 8 11 10 12 4 1 0 0 9 11 11 8 12 6 0 1 1 9 16 11 13 14 7 2 0 1 9 1 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 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Popularity[t] = -0.983357375578792 + 0.0893813706555356month[t] + 0.104378264723487FindingFriends[t] + 0.211622867223575KnowingPeople[t] + 0.385193850989470Liked[t] + 0.594410135928509Celebrity[t] + 0.307649945898061bestfriend[t] -0.0324002150208811secondbestfriend[t] + 0.411552931191423thirdbestfriend[t] -0.00181007322403901t + 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) -0.983357375578792 6.011657 -0.1636 0.870292 0.435146 month 0.0893813706555356 0.640842 0.1395 0.889267 0.444634 FindingFriends 0.104378264723487 0.098559 1.059 0.291327 0.145664 KnowingPeople 0.211622867223575 0.06405 3.304 0.001199 6e-04 Liked 0.385193850989470 0.099038 3.8894 0.000152 7.6e-05 Celebrity 0.594410135928509 0.156961 3.787 0.000222 0.000111 bestfriend 0.307649945898061 0.211991 1.4512 0.148859 0.074429 secondbestfriend -0.0324002150208811 0.20236 -0.1601 0.873014 0.436507 thirdbestfriend 0.411552931191423 0.214612 1.9177 0.057107 0.028553 t -0.00181007322403901 0.006864 -0.2637 0.792389 0.396194 Multiple Linear Regression - Regression Statistics Multiple R 0.719101919830673 R-squared 0.51710757110416 Adjusted R-squared 0.487340229596882 F-TEST (value) 17.3716410307495 F-TEST (DF numerator) 9 F-TEST (DF denominator) 146 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.1026328891003 Sum Squared Residuals 645.475499683636 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13 11.2049026711581 1.79509732884189 2 12 11.0501976167471 0.94980238325289 3 15 13.1284642259693 1.87153577403075 4 12 11.4502389709335 0.549761029066484 5 10 10.9719246015601 -0.97192460156013 6 12 9.36122331907425 2.63877668092575 7 15 16.9268608320424 -1.92686083204242 8 9 10.3094079411661 -1.30940794116615 9 12 13.0209978602754 -1.02099786027543 10 11 7.54336622238227 3.45663377761773 11 11 13.0149485846245 -2.01494858462453 12 11 12.1838317170145 -1.18383171701454 13 15 12.2991592688119 2.70084073118813 14 7 11.4832734656525 -4.48327346565248 15 11 11.2781603020676 -0.278160302067564 16 11 10.8220760751143 0.177923924885652 17 10 11.8347311899754 -1.83473118997540 18 14 14.8674128169904 -0.867412816990426 19 10 8.568239700105 1.43176029989501 20 6 9.3425500044131 -3.34255000441309 21 11 8.77932092658329 2.22067907341672 22 15 14.0737626346785 0.926237365321513 23 11 11.5803388330651 -0.580338833065076 24 12 9.74160524658529 2.25839475341471 25 14 13.0843361390877 0.915663860912256 26 15 14.5245748735349 0.475425126465101 27 9 14.3087353508027 -5.30873535080275 28 13 12.4834032242154 0.516596775784634 29 13 13.4529879522718 -0.452987952271807 30 16 11.1694519977055 4.83054800229448 31 13 8.75162527439104 4.24837472560896 32 12 13.6248490819480 -1.62484908194797 33 14 14.5697830251290 -0.569783025128966 34 11 9.60790170281558 1.39209829718442 35 9 10.2281357490962 -1.22813574909621 36 16 14.1849071539586 1.81509284604136 37 12 13.1540413424309 -1.15404134243088 38 10 9.71307390046835 0.286926099531649 39 13 13.1545469131956 -0.154546913195559 40 16 15.2287736182529 0.771226381747053 41 14 13.1151849345199 0.884815065480128 42 15 8.0705833181084 6.9294166818916 43 5 9.71510528588548 -4.71510528588548 44 8 10.3134380241432 -2.31343802414315 45 11 11.1487052586015 -0.148705258601461 46 16 14.2175074662198 1.78249253378025 47 17 14.2475308431785 2.75246915682147 48 9 8.0287939713907 0.9712060286093 49 9 11.3995874334900 -2.39958743349003 50 13 14.8178416598702 -1.81784165987018 51 10 10.8886453480061 -0.888645348006098 52 6 11.9910442211409 -5.99104422114089 53 12 11.8495190412859 0.150480958714054 54 8 10.4331664434410 -2.43316644344105 55 14 11.8091063908163 2.19089360918366 56 12 12.8761180336080 -0.876118033608047 57 11 11.1092335643711 -0.109233564371141 58 16 14.1747570596022 1.82524294039776 59 8 10.2776291475077 -2.27762914750773 60 15 14.6575447101163 0.342455289883676 61 7 9.15112059843979 -2.15112059843979 62 16 13.9464110345232 2.05358896547682 63 14 12.9130473900452 1.08695260995478 64 16 13.661414209331 2.338585790669 65 9 10.2507224958308 -1.25072249583076 66 14 12.3894985830801 1.61050141691992 67 11 13.2954002601194 -2.29540026011941 68 13 10.4232974364030 2.57670256359698 69 15 13.0175810545881 1.98241894541193 70 5 5.82162779891743 -0.821627798917435 71 15 12.938348305903 2.061651694097 72 13 11.9737875527751 1.02621244722486 73 11 13.0575037847135 -2.05750378471349 74 11 14.1346593713084 -3.13465937130841 75 12 12.2462910576543 -0.246291057654347 76 12 13.4605564914878 -1.46055649148783 77 12 12.8158509750580 -0.81585097505803 78 12 12.1256704010635 -0.125670401063508 79 14 11.0986370101107 2.90136298988934 80 6 8.02152943171598 -2.02152943171598 81 7 9.46530618764306 -2.46530618764306 82 14 12.6302867701622 1.36971322983782 83 14 14.0434433176965 -0.0434433176964516 84 10 10.9996709892448 -0.999670989244844 85 13 9.39576007708632 3.60423992291368 86 12 12.4552688010383 -0.455268801038252 87 9 9.09895259471804 -0.0989525947180386 88 12 12.3827335866523 -0.382733586652303 89 16 15.1126325964851 0.887367403514898 90 10 10.8271360363588 -0.827136036358803 91 14 13.0149414140629 0.985058585937127 92 10 13.6743590227115 -3.67435902271151 93 16 15.0189494268598 0.98105057314016 94 15 13.3269385259384 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-0.207012696569090 143 15 12.4076173372964 2.59238266270357 144 8 8.9046262949945 -0.904626294994503 145 12 11.1211270460751 0.878872953924872 146 10 10.3911752139798 -0.391175213979777 147 8 11.2623122209065 -3.26231222090646 148 10 14.2774505151922 -4.27745051519218 149 15 14.1609097605415 0.839090239458515 150 16 13.9641477830056 2.03585221699443 151 13 13.1799642692192 -0.179964269219202 152 16 14.9690796300644 1.03092036993557 153 9 9.6862486002568 -0.6862486002568 154 14 13.2785335658949 0.721466434105089 155 14 13.3355686009417 0.664431399058298 156 12 9.96088127603094 2.03911872396906 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 13 0.836001792063908 0.327996415872184 0.163998207936092 14 0.90195839407854 0.196083211842921 0.0980416059214603 15 0.879240557644519 0.241518884710961 0.120759442355481 16 0.808616793673195 0.38276641265361 0.191383206326805 17 0.726585161757706 0.546829676484589 0.273414838242294 18 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121 0.740958354445508 0.518083291108985 0.259041645554492 122 0.688252418566369 0.623495162867262 0.311747581433631 123 0.674328362110953 0.651343275778093 0.325671637889046 124 0.631483536872456 0.737032926255088 0.368516463127544 125 0.612176877947887 0.775646244104226 0.387823122052113 126 0.612552819280793 0.774894361438414 0.387447180719207 127 0.54603567075918 0.90792865848164 0.45396432924082 128 0.50487872917079 0.99024254165842 0.49512127082921 129 0.482994137738118 0.965988275476237 0.517005862261882 130 0.465009250028874 0.930018500057748 0.534990749971126 131 0.392923101575466 0.785846203150932 0.607076898424534 132 0.371462252534147 0.742924505068294 0.628537747465853 133 0.325667334331613 0.651334668663226 0.674332665668387 134 0.281323824371727 0.562647648743455 0.718676175628273 135 0.237546151569608 0.475092303139215 0.762453848430392 136 0.219069151340992 0.438138302681984 0.780930848659008 137 0.184306688933709 0.368613377867419 0.81569331106629 138 0.197580908202362 0.395161816404724 0.802419091797638 139 0.616069745068915 0.76786050986217 0.383930254931085 140 0.526150517910436 0.947698964179127 0.473849482089564 141 0.405569862606172 0.811139725212344 0.594430137393828 142 0.400851681624013 0.801703363248025 0.599148318375987 143 0.278089005617193 0.556178011234387 0.721910994382807 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 29 0.221374045801527 NOK 10% type I error level 48 0.366412213740458 NOK

Charts produced by software:

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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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Software written by Ed van Stee & Patrick Wessa

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