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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("Rfwdmv-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('Rfwdmv') Loading required package: MASS > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "assign.groups" > > ### * assign.groups > > flush(stderr()); flush(stdout()) > > ### Name: assign.groups > ### Title: Tentative Group Assignments > ### Aliases: assign.groups > ### Keywords: utilities > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > # Create an fwdmv object containing just an initial fit # > > fondi.init <- fwdmv(fondi.dat) > > # Assign tentative groups # > > fondi.tgs <- assign.groups(fondi.init, groups = list(g1, g2)) > > > > cleanEx(); ..nameEx <- "bank.dat" > > ### * bank.dat > > flush(stderr()); flush(stdout()) > > ### Name: bank.dat > ### Title: Swiss bank notes > ### Aliases: bank.dat > ### Keywords: datasets > > ### ** Examples > > data(bank.dat) > plot(bank.dat) > > > > cleanEx(); ..nameEx <- "bb.subset" > > ### * bb.subset > > flush(stderr()); flush(stdout()) > > ### Name: bb.subset > ### Title: Initial Subset by Bivariate Box Plots > ### Aliases: bb.subset > ### Keywords: utilities > > ### ** Examples > > data(fondi.dat) > fondi.fwdmv <- fwdmv(fondi.dat, bsb = bb.subset) > > ### start with a subset size m=22 > data(fondi.dat) > fondi.fwdmv <- fwdmv(fondi.dat, bsb = bb.subset(fondi.dat,22)) > > > > cleanEx(); ..nameEx <- "bigunit.fwdmv" > > ### * bigunit.fwdmv > > flush(stderr()); flush(stdout()) > > ### Name: bigunit.fwdmv > ### Title: Generate the Big Unit Matrix > ### Aliases: bigunit.fwdmv > ### Keywords: utilities > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fondi.bigUnit <- bigunit.fwdmv(fondi.1) > > > > cleanEx(); ..nameEx <- "dyestuff.dat" > > ### * dyestuff.dat > > flush(stderr()); flush(stdout()) > > ### Name: dyestuff.dat > ### Title: Dyestuff data > ### Aliases: dyestuff.dat > ### Keywords: datasets > > ### ** Examples > > data(dyestuff.dat) > > > > cleanEx(); ..nameEx <- "eigenvalues.fwdmv" > > ### * eigenvalues.fwdmv > > flush(stderr()); flush(stdout()) > > ### Name: eigenvalues.fwdmv > ### Title: Compute the Eigenvalues from an fwdmv Object > ### Aliases: eigenvalues.fwdmv > ### Keywords: utilities > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fondi.evals <- eigenvalues.fwdmv(fondi.1) > > > > cleanEx(); ..nameEx <- "eigenvectors.fwdmv" > > ### * eigenvectors.fwdmv > > flush(stderr()); flush(stdout()) > > ### Name: eigenvectors.fwdmv > ### Title: Retrieve an Eigenvector of the Covariance Matrix Estimate from > ### an fwdmv Object > ### Aliases: eigenvectors.fwdmv > ### Keywords: methods > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fondi.evec1 <- eigenvectors.fwdmv(fondi.1, which.vector = 1) > fondi.evec3 <- eigenvectors.fwdmv(fondi.1, which.vector = 3) > > > > cleanEx(); ..nameEx <- "electrodes.dat" > > ### * electrodes.dat > > flush(stderr()); flush(stdout()) > > ### Name: electrodes.dat > ### Title: Electrodes data > ### Aliases: electrodes.dat > ### Keywords: datasets > > ### ** Examples > > data(electrodes.dat) > > > > cleanEx(); ..nameEx <- "ellipse.subset" > > ### * ellipse.subset > > flush(stderr()); flush(stdout()) > > ### Name: ellipse.subset > ### Title: Initial Subset by Robustly Centered Ellipses > ### Aliases: ellipse.subset > ### Keywords: utilities > > ### ** Examples > > data(fondi.dat) > > fondi.fwdmv <- fwdmv(fondi.dat, bsb = ellipse.subset) > > > > cleanEx(); ..nameEx <- "emilia.dat" > > ### * emilia.dat > > flush(stderr()); flush(stdout()) > > ### Name: emilia.dat > ### Title: Municipalities in Emilia-Romagna > ### Aliases: emilia.dat > ### Keywords: datasets > > ### ** Examples > > data(emilia.dat) > > > > cleanEx(); ..nameEx <- "fondi.dat" > > ### * fondi.dat > > flush(stderr()); flush(stdout()) > > ### Name: fondi.dat > ### Title: Investment Funds Data > ### Aliases: fondi.dat > ### Keywords: datasets > > ### ** Examples > > data(fondi.dat) > > > > cleanEx(); ..nameEx <- "fwdmv" > > ### * fwdmv > > flush(stderr()); flush(stdout()) > > ### Name: fwdmv > ### Title: Multivariate Forward Search > ### Aliases: fwdmv > ### Keywords: multivariate > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.fwdmv <- fwdmv(fondi.dat, groups = list(g1, g2)) > > > > cleanEx(); ..nameEx <- "fwdmv.init" > > ### * fwdmv.init > > flush(stderr()); flush(stdout()) > > ### Name: fwdmv.init > ### Title: Multivariate Forward Search for Ungrouped Data > ### Aliases: fwdmv.init > ### Keywords: multivariate > > ### ** Examples > > data(fondi.dat) > fondi.init <- fwdmv.init(fondi.dat) > > data(fondi.dat) > #### find the intial subset using robust bivariate ellipses > #### start with an initial subset size of 17 units > fondi.init <- fwdmv.init(fondi.dat,bsb=ellipse.subset(fondi.dat,17)) > > data(fondi.dat) > #### find the intial subset using robust bivariate boxplots and > #### start with an initial subset size of 17 units and > fondi.init <- fwdmv.init(fondi.dat,bsb=bb.subset(fondi.dat,17)) > > > > > cleanEx(); ..nameEx <- "fwdmv.object" > > ### * fwdmv.object > > flush(stderr()); flush(stdout()) > > ### Name: fwdmv.object > ### Title: fwdmv.object > ### Aliases: fwdmv.object > ### Keywords: datasets > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > > #fondi.1 is an fwdmv object. > > > > cleanEx(); ..nameEx <- "fwdmvChangePlot" > > ### * fwdmvChangePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvChangePlot > ### Title: Change Plot of an fwdmv Object > ### Aliases: fwdmvChangePlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvChangePlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvConfirmPlot" > > ### * fwdmvConfirmPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvConfirmPlot > ### Title: Confirmatory Group Assignment Plot > ### Aliases: fwdmvConfirmPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.fwdmv <- fwdmv(fondi.dat, groups = list(g1, g2)) > > fwdmvConfirmPlot(fondi.fwdmv, n.steps = 30) > > > > cleanEx(); ..nameEx <- "fwdmvCovariancePlot" > > ### * fwdmvCovariancePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvCovariancePlot > ### Title: Plot the Covariance Matrix of an fwdmv Object > ### Aliases: fwdmvCovariancePlot > ### Keywords: hplot iplot > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.fwdmv <- fwdmv(fondi.dat, groups = list(g1, g2)) > > fwdmvCovariancePlot(fondi.fwdmv) > > fwdmvCovariancePlot(fondi.fwdmv, id = "all") > > # Use 'id = TRUE' for interactive curve labels. > > > > cleanEx(); ..nameEx <- "fwdmvDeterminantPlot" > > ### * fwdmvDeterminantPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvDeterminantPlot > ### Title: Plot the Determinant in an fwdmv Object > ### Aliases: fwdmvDeterminantPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.fwdmv <- fwdmv(fondi.dat, groups = list(g1, g2)) > > fwdmvDeterminantPlot(fondi.fwdmv) > > > > cleanEx(); ..nameEx <- "fwdmvDistancePlot" > > ### * fwdmvDistancePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvDistancePlot > ### Title: Plot the Mahalanobis Distances in an fwdmv Object > ### Aliases: fwdmvDistancePlot > ### Keywords: hplot iplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > > fwdmvDistancePlot(fondi.1) > > # Use 'id = TRUE' for interactive trajectory identification. > > fwdmvDistancePlot(fondi.1, id = c(39, 52, 96)) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.2 <- fwdmv(fondi.dat, groups = list(g1, g2)) > > fwdmvDistancePlot(fondi.2, group = 2) > > > > cleanEx(); ..nameEx <- "fwdmvEccentricityPlot" > > ### * fwdmvEccentricityPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvEccentricityPlot > ### Title: Eccentricity Plot > ### Aliases: fwdmvEccentricityPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvEccentricityPlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvEigenvectorPlot" > > ### * fwdmvEigenvectorPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvEigenvectorPlot > ### Title: Plot the Components of an Eigenvector in an fwdmv Object > ### Aliases: fwdmvEigenvectorPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvEigenvectorPlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvEllipsePlot" > > ### * fwdmvEllipsePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvEllipsePlot > ### Title: Ellipses Plot of an fwdmv Object > ### Aliases: fwdmvEllipsePlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.fwdmv <- fwdmv(fondi.dat, groups = list(g1, g2)) > > fwdmvEllipsePlot(fondi.fwdmv, subset.size = 60) > > > > cleanEx(); ..nameEx <- "fwdmvEntryPlot" > > ### * fwdmvEntryPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvEntryPlot > ### Title: Plot the Entry Order in an fwdmv Object > ### Aliases: fwdmvEntryPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvEntryPlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvGapPlot" > > ### * fwdmvGapPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvGapPlot > ### Title: Plot the Gap in an fwdmv Object > ### Aliases: fwdmvGapPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvGapPlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvMinmaxPlot" > > ### * fwdmvMinmaxPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvMinmaxPlot > ### Title: Plot Minimum and Maximum Distances in an fwdmv Object > ### Aliases: fwdmvMinmaxPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvMinmaxPlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvPairsPlot" > > ### * fwdmvPairsPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvPairsPlot > ### Title: A Pairs-like Plots of an fwdmv Object > ### Aliases: fwdmvPairsPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > > g1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 51, 53, 55, 56) > > g2 <- c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103) > > fondi.fwdmv <- fwdmv(fondi.dat, groups = list(g1, g2)) > > fwdmvPairsPlot(fondi.fwdmv) > > > > cleanEx(); ..nameEx <- "fwdmvPartitionPlot" > > ### * fwdmvPartitionPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvPartitionPlot > ### Title: Plot a Partitioned fwdmv Object > ### Aliases: fwdmvPartitionPlot > ### Keywords: hplot > > ### ** Examples > > # data(fondi.dat) > # fondi.init <- fwdmv.init(fondi.dat) > # p1 <- partition(fondi.init) > > # draw a line segment intersecting several trajectories # > # view the allocation # > > # fwdmvPartitionPlot(p1) > > > > cleanEx(); ..nameEx <- "fwdmvPrePlot" > > ### * fwdmvPrePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvPrePlot > ### Title: Pairs Plot in Rfwdmv > ### Aliases: fwdmvPrePlot > ### Keywords: hplot > > ### ** Examples > > data(heads.dat) > > fwdmvPrePlot(heads.dat, panel = panel.be) > fwdmvPrePlot(heads.dat, panel = panel.me) > fwdmvPrePlot(heads.dat, panel = panel.bb) > > > > cleanEx(); ..nameEx <- "fwdmvPrincompPlot" > > ### * fwdmvPrincompPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvPrincompPlot > ### Title: Plot the Principal Components from an fwdmv Object > ### Aliases: fwdmvPrincompPlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvPrincompPlot(fondi.1) > > > > cleanEx(); ..nameEx <- "fwdmvQuantilePlot" > > ### * fwdmvQuantilePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdmvQuantilePlot > ### Title: Plot Trajectories over Quantiles of the Distances > ### Aliases: fwdmvQuantilePlot > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > fwdmvQuantilePlot(fondi.1, subset.size = 45) > > > > cleanEx(); ..nameEx <- "fwdtr" > > ### * fwdtr > > flush(stderr()); flush(stdout()) > > ### Name: fwdtr > ### Title: Maximum likelihood estimates of transformation parameters > ### Aliases: fwdtr > ### Keywords: multivariate > > ### ** Examples > > ## Forward search on untrasformed data > data(mussels.dat) > l.mle <- fwdtr(mussels.dat) > fwdtrMlePlot(l.mle) > > ## Forward search on transformed data as specified in vector lamdba > data(mussels.dat) > l.mle <- fwdtr(mussels.dat,lambda = c(1, 0.5, 1, 0, 1/3)) > fwdtrMlePlot(l.mle) > > ## estimate a common value of lambda for all the variables and use 1/3 to order Mahalanobis distances in each step of the search > data(mussels.dat) > l.mle <- fwdtr(mussels.dat, lambda = 1/3, one.lambda = TRUE) > fwdtrMlePlot(l.mle) > > ### Test variables 2 and 5 > ### The forward is based on untransformed data for variables 1, 3 and 4 > ### sqrt for variable 2 and third root for variable 5 > l.mle <- fwdtr(mussels.dat, lambda = c(0.5, 1/3), col.to.transform = c(2, 5)) > fwdtrMlePlot(l.mle) > > > > cleanEx(); ..nameEx <- "fwdtr.object" > > ### * fwdtr.object > > flush(stderr()); flush(stdout()) > > ### Name: fwdtr.object > ### Title: fwdtr.object > ### Aliases: fwdtr.object > ### Keywords: datasets > > ### ** Examples > > data(mussels.dat) > l.mle <- fwdtr(mussels.dat) > > #l.mle is a fwdtr object. > > > > cleanEx(); ..nameEx <- "fwdtr.test" > > ### * fwdtr.test > > flush(stderr()); flush(stdout()) > > ### Name: fwdtr.test > ### Title: Multivariate Fan Plot > ### Aliases: fwdtr.test > ### Keywords: multivariate > > ### ** Examples > > data(mussels.dat) > > ## reasonable values of transformation parameters for the data set > > lambda.R <- c(0.5, 0, 0.5, 0, 0) > l.rat <- fwdtr.test(mussels.dat, lambda.R) Expanding lambda = -1 around 0.5. Expanding lambda = -0.5 around 0.5. Expanding lambda = 0 around 0.5. Expanding lambda = 0.5 around 0.5. Expanding lambda = 1 around 0.5. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. Expanding lambda = -1 around 0.5. Expanding lambda = -0.5 around 0.5. Expanding lambda = 0 around 0.5. Expanding lambda = 0.5 around 0.5. Expanding lambda = 1 around 0.5. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. > plot.fwdtr.test(l.rat) NULL > > ## Produce a fan plot for columns 2 and 4 of dataset mussels.dat > > l.rat <- fwdtr.test(mussels.dat, parameters = lambda.R, col.to.compare = c(2,4)) Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. > plot.fwdtr.test(l.rat) NULL > > ## reasonable values of transformation parameters for the data set > > lambda.R <- c(0.5,0,0.5,0,0) > lambda.around <- c(0,1/3,0.5) > > ## Produce a fan plot for column 2 of dataset mussels.dat > ## The values of lambda which are tested are log, third root > ## and square root > > l.rat <- fwdtr.test(mussels.dat, parameters = lambda.R, col.to.compare = 2, lambda.around = lambda.around) Expanding lambda = 0 around 0. Expanding lambda = 0.3333333 around 0. Expanding lambda = 0.5 around 0. > plot.fwdtr.test(l.rat) NULL > > > > cleanEx(); ..nameEx <- "fwdtr.test.object" > > ### * fwdtr.test.object > > flush(stderr()); flush(stdout()) > > ### Name: fwdtr.test.object > ### Title: fwdtr.test.object > ### Aliases: fwdtr.test.object > ### Keywords: datasets > > ### ** Examples > > > data(mussels.dat) > > ## reasonable values of transformation parameters for the data set > ## found using procedure fwdtr > > lambda.R <- c(0.5, 0, 0.5, 0, 0) > l.rat <- fwdtr.test(mussels.dat, lambda.R, col.to.compare = 1:5) Expanding lambda = -1 around 0.5. Expanding lambda = -0.5 around 0.5. Expanding lambda = 0 around 0.5. Expanding lambda = 0.5 around 0.5. Expanding lambda = 1 around 0.5. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. Expanding lambda = -1 around 0.5. Expanding lambda = -0.5 around 0.5. Expanding lambda = 0 around 0.5. Expanding lambda = 0.5 around 0.5. Expanding lambda = 1 around 0.5. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. Expanding lambda = -1 around 0. Expanding lambda = -0.5 around 0. Expanding lambda = 0 around 0. Expanding lambda = 0.5 around 0. Expanding lambda = 1 around 0. > > ## l.rat is a fwdtr.test object > > plot.fwdtr.test(l.rat) NULL > > > > cleanEx(); ..nameEx <- "fwdtrLrPlot" > > ### * fwdtrLrPlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdtrLrPlot > ### Title: Plot method for fwdtr objects > ### Aliases: fwdtrLrPlot > ### Keywords: hplot > > ### ** Examples > > ## Forward search on untrasformed data > > data(mussels.dat) > l.mle <- fwdtr(mussels.dat) > > ## plot the likelihood ratio test > > fwdtrLrPlot(l.mle) > > > > cleanEx(); ..nameEx <- "fwdtrMlePlot" > > ### * fwdtrMlePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdtrMlePlot > ### Title: Plot method for fwdtr objects > ### Aliases: fwdtrMlePlot > ### Keywords: hplot > > ### ** Examples > > ## Forward search on untrasformed data > > data(mussels.dat) > l.mle<-fwdtr(mussels.dat) > > ## Plot maximum likelihood estimates of the trasnformation parameters > ## in each step of the forward search > > fwdtrMlePlot(l.mle) > > > ## Test variables 2 and 5 > ## The forward is based on untransformed data for variables 1, 3 and 4 > ## sqrt for variable 2 and third root for variable 5 > > l.mle <- fwdtr(mussels.dat, lambda = c(0.5, 1/3), col.to.transform = c(2,5)) > > ## plot trajectories of maximum likelihood estimates of transformation parameters > ## for variables 2 and 5 > > fwdtrMlePlot(l.mle) > > > > cleanEx(); ..nameEx <- "fwdtrProfilePlot" > > ### * fwdtrProfilePlot > > flush(stderr()); flush(stdout()) > > ### Name: fwdtrProfilePlot > ### Title: Plot method for profile.fwdtr objects > ### Aliases: fwdtrProfilePlot > ### Keywords: hplot > > ### ** Examples > data(mussels.dat) > > ## Forward search on untransformed data > ## Compute max lik. estimates of tramsformation parameters > > l.mle <- fwdtr(mussels.dat) > > ## Compute profile loglikelihoods for transformation parameters in the > ## last step of the search and create an object of class profile.fwdtr > > l.profile.mle <- profile.fwdtr(l.mle) 55 57 55 60 60 > > ## plot the profile loglikelihoods of transformation parameters for > ## each variable > > fwdtrProfilePlot(l.profile.mle) NULL > > ## Test variables 2 and 5 > ## The forward is based on untransformed data for variables 1, 3 and 4 > ## sqrt for variable 2 and third root for variable 5 > > l.mle <- fwdtr(mussels.dat, lambda = c(0.5, 1/3), col.to.transform = c(2, 5)) > > ## build profile likelihood for transformed variables. > ## Profile function takes into account that we have also untransformed variables. > > l.profile.mle <- profile.fwdtr(l.mle) 54 58 > fwdtrProfilePlot(l.profile.mle) NULL > > > > cleanEx(); ..nameEx <- "heads.dat" > > ### * heads.dat > > flush(stderr()); flush(stdout()) > > ### Name: heads.dat > ### Title: Swiss heads data > ### Aliases: heads.dat > ### Keywords: datasets > > ### ** Examples > > data(heads.dat) > > > > cleanEx(); ..nameEx <- "iris.dat" > > ### * iris.dat > > flush(stderr()); flush(stdout()) > > ### Name: iris.dat > ### Title: Iris data > ### Aliases: iris.dat > ### Keywords: datasets > > ### ** Examples > > data(iris.dat) > > > > cleanEx(); ..nameEx <- "mcd.subset" > > ### * mcd.subset > > flush(stderr()); flush(stdout()) > > ### Name: mcd.subset > ### Title: Initial Subset by MCD Distances > ### Aliases: mcd.subset > ### Keywords: utilities > > ### ** Examples > > data(fondi.dat) > > fondi.fwdmv <- fwdmv(fondi.dat, bsb = mcd.subset) Warning: package 'lqs' has been moved back to package 'MASS' > > > > cleanEx(); ..nameEx <- "milk.dat" > > ### * milk.dat > > flush(stderr()); flush(stdout()) > > ### Name: milk.dat > ### Title: Milk data > ### Aliases: milk.dat > ### Keywords: datasets > > ### ** Examples > > data(milk.dat) > pairs(milk.dat) > > > > cleanEx(); ..nameEx <- "ms.dat" > > ### * ms.dat > > flush(stderr()); flush(stdout()) > > ### Name: ms.dat > ### Title: Muscular dystrophy data > ### Aliases: ms.dat > ### Keywords: datasets > > ### ** Examples > > data(ms.dat) > > > > cleanEx(); ..nameEx <- "mssmall.dat" > > ### * mssmall.dat > > flush(stderr()); flush(stdout()) > > ### Name: mssmall.dat > ### Title: Muscular dystrophy data (small) > ### Aliases: mssmall.dat > ### Keywords: datasets > > ### ** Examples > > data(mssmall.dat) > > > > cleanEx(); ..nameEx <- "mussels.dat" > > ### * mussels.dat > > flush(stderr()); flush(stdout()) > > ### Name: mussels.dat > ### Title: Horse mussels > ### Aliases: mussels.dat > ### Keywords: datasets > > ### ** Examples > > data(mussels.dat) > > > > cleanEx(); ..nameEx <- "panel.bb" > > ### * panel.bb > > flush(stderr()); flush(stdout()) > > ### Name: panel.bb > ### Title: Bivariate Box Plots Panel Function > ### Aliases: panel.bb > ### Keywords: hplot > > ### ** Examples > > data(heads.dat) > > fwdmvPrePlot(heads.dat, panel = panel.bb) > > > > cleanEx(); ..nameEx <- "panel.be" > > ### * panel.be > > flush(stderr()); flush(stdout()) > > ### Name: panel.be > ### Title: Bivariate Ellipse Panel Function > ### Aliases: panel.be > ### Keywords: hplot > > ### ** Examples > > data(heads.dat) > > fwdmvPrePlot(heads.dat, panel = panel.be) > > > > cleanEx(); ..nameEx <- "panel.me" > > ### * panel.me > > flush(stderr()); flush(stdout()) > > ### Name: panel.me > ### Title: Bivariate Ellipse Panel Function > ### Aliases: panel.me > ### Keywords: hplot > > ### ** Examples > > data(heads.dat) > > fwdmvPrePlot(heads.dat, panel = panel.me) > > > > cleanEx(); ..nameEx <- "partition" > > ### * partition > > flush(stderr()); flush(stdout()) > > ### Name: partition > ### Title: Interactive Group Assignment > ### Aliases: partition > ### Keywords: hplot iplot > > ### ** Examples > > # data(fondi.dat) > # fondi.init <- fwdmv.init(fondi.dat) > # p1 <- partition(fondi.init) > > # draw a line segment intersecting several trajectories # > # and view the allocation # > > > > cleanEx(); ..nameEx <- "plot.fwdmv" > > ### * plot.fwdmv > > flush(stderr()); flush(stdout()) > > ### Name: plot.fwdmv > ### Title: Plot method for fwdmv objects. > ### Aliases: plot.fwdmv > ### Keywords: hplot > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > plot(fondi.1) NULL > > > > cleanEx(); ..nameEx <- "print.fwdmv" > > ### * print.fwdmv > > flush(stderr()); flush(stdout()) > > ### Name: print.fwdmv > ### Title: Print method for fwdmv objects. > ### Aliases: print.fwdmv > ### Keywords: methods > > ### ** Examples > > data(fondi.dat) > fondi.1 <- fwdmv(fondi.dat) > print(fondi.1) ## Equivalent to fondi.1 A multivariate forward search on the data fondi.dat. A preliminary forward search. The initial subset contains 34 units and the final subset 103 units. > > > > cleanEx(); ..nameEx <- "profile.fwdtr" > > ### * profile.fwdtr > > flush(stderr()); flush(stdout()) > > ### Name: profile.fwdtr > ### Title: Profile log-likelihood estimates of transformation parameters > ### Aliases: profile.fwdtr > ### Keywords: multivariate > > ### ** Examples > data(mussels.dat) > > ## Forward search on untransformed data > ## Compute max lik. estimates of tramsformation parameters > > l.mle <- fwdtr(mussels.dat) > > ## Compute profile loglikelihoods for transformation parameters in the > ## last step of the search and create an object of class profile.fwdtr > > l.profile.mle <- profile.fwdtr(l.mle) 55 57 55 60 60 > > ## plot the profile loglikelihoods of transformation parameters for > ## each variable > > fwdtrProfilePlot(l.profile.mle) NULL > > ## Forward search on transformed data as specified in vector lamdba > > data(mussels.dat) > l.mle <- fwdtr(mussels.dat, lambda = c(1, 0.5, 1, 0, 1/3)) > > ## Compute profile loglikelihoods for transformation parameters in the > ## last step of the search and create an object of class profile.fwdtr > > l.profile.mle <- profile.fwdtr(l.mle) 54 55 53 59 59 > > ## plot the profile loglikelihoods of transformation parameters for > ## each variable > > fwdtrProfilePlot(l.profile.mle) NULL > > ## estimate a common value of lambda for all the variables and use 1/3 > ## to order Mahalanobis distances in each step of the search > > data(mussels.dat) > l.mle <- fwdtr(mussels.dat, lambda = 1/3, one.lambda = TRUE) > l.profile.mle <- profile.fwdtr(l.mle) > fwdtrProfilePlot(l.profile.mle) NULL > > ## Test variables 2 and 5 > ## The forward is based on untransformed data for variables 1, 3 and 4 > ## sqrt for variable 2 and third root for variable 5 > > l.mle <- fwdtr(mussels.dat, lambda = c(0.5, 1/3), col.to.transform = c(2, 5)) > l.profile.mle <- profile.fwdtr(l.mle) 54 58 > fwdtrProfilePlot(l.profile.mle) NULL > > > > cleanEx(); ..nameEx <- "profile.fwdtr.object" > > ### * profile.fwdtr.object > > flush(stderr()); flush(stdout()) > > ### Name: profile.fwdtr.object > ### Title: profile.fwdtr.object > ### Aliases: profile.fwdtr.object > ### Keywords: datasets > > ### ** Examples > > > data(mussels.dat) > > ## Forward search on untrasformed data > ## Compute max lik. estimates of tramsformation parameters > > l.mle<-fwdtr(mussels.dat) > > ## Compute profile loglikelihoods for transformation parameters in the last step of the search > ## and create an object of class profile.fwdtr > > l.profile.mle <- profile.fwdtr(l.mle) 55 57 55 60 60 > > ## plot the profile loglikelihoods of transformation parameters for each variable > > fwdtrProfilePlot(l.profile.mle) NULL > > > > > cleanEx(); ..nameEx <- "quality.dat" > > ### * quality.dat > > flush(stderr()); flush(stdout()) > > ### Name: quality.dat > ### Title: Quality of life data > ### Aliases: quality.dat > ### Keywords: datasets > > ### ** Examples > > data(quality.dat) > > > > cleanEx(); ..nameEx <- "record.dat" > > ### * record.dat > > flush(stderr()); flush(stdout()) > > ### Name: record.dat > ### Title: Track rercords for women > ### Aliases: record.dat > ### Keywords: datasets > > ### ** Examples > > data(record.dat) > > > > cleanEx(); ..nameEx <- "sixtyeighty.dat" > > ### * sixtyeighty.dat > > flush(stderr()); flush(stdout()) > > ### Name: sixtyeighty.dat > ### Title: 60:80 data > ### Aliases: sixtyeighty.dat > ### Keywords: datasets > > ### ** Examples > > data(sixtyeighty.dat) > > > > cleanEx(); ..nameEx <- "threetwo.dat" > > ### * threetwo.dat > > flush(stderr()); flush(stdout()) > > ### Name: threetwo.dat > ### Title: Three clusters two outliers > ### Aliases: threetwo.dat > ### Keywords: datasets > > ### ** Examples > > data(threetwo.dat) > > > > ### *