# skyline

From ape v5.3
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##### Skyline Plot Estimate of Effective Population Size

skyline computes the generalized skyline plot estimate of effective population size from an estimated phylogeny. The demographic history is approximated by a step-function. The number of parameters of the skyline plot (i.e. its smoothness) is controlled by a parameter epsilon.

find.skyline.epsilon searches for an optimal value of the epsilon parameter, i.e. the value that maximizes the AICc-corrected log-likelihood (logL.AICc).

Keywords
manip
##### Usage
skyline(x, …)
# S3 method for phylo
skyline(x, …)
# S3 method for coalescentIntervals
skyline(x, epsilon=0, …)
# S3 method for collapsedIntervals
skyline(x, old.style=FALSE, …)
find.skyline.epsilon(ci, GRID=1000, MINEPS=1e-6, …)
##### Arguments
x

Either an ultrametric tree (i.e. an object of class "phylo"), or coalescent intervals (i.e. an object of class "coalescentIntervals"), or collapsed coalescent intervals (i.e. an object of class "collapsedIntervals").

epsilon

collapsing parameter that controls the amount of smoothing (allowed range: from 0 to ci$total.depth, default value: 0). This is the same parameter as in collapsed.intervals. old.style Parameter to choose between two slightly different variants of the generalized skyline plot (Strimmer and Pybus, pers. comm.). The default value FALSE is recommended. ci coalescent intervals (i.e. an object of class "coalescentIntervals") GRID Parameter for the grid search for epsilon in find.skyline.epsilon. MINEPS Parameter for the grid search for epsilon in find.skyline.epsilon. Any of the above parameters. ##### Details skyline implements the generalized skyline plot introduced in Strimmer and Pybus (2001). For epsilon = 0 the generalized skyline plot degenerates to the classic skyline plot described in Pybus et al. (2000). The latter is in turn directly related to lineage-through-time plots (Nee et al., 1995). ##### Value skyline returns an object of class "skyline" with the following entries: time A vector with the time at the end of each coalescent interval (i.e. the accumulated interval lengths from the beginning of the first interval to the end of an interval) interval.length A vector with the length of each interval. population.size A vector with the effective population size of each interval. parameter.count Number of free parameters in the skyline plot. epsilon The value of the underlying smoothing parameter. logL Log-likelihood of skyline plot (see Strimmer and Pybus, 2001). logL.AICc AICc corrected log-likelihood (see Strimmer and Pybus, 2001). find.skyline.epsilon returns the value of the epsilon parameter that maximizes logL.AICc. ##### References Strimmer, K. and Pybus, O. G. (2001) Exploring the demographic history of DNA sequences using the generalized skyline plot. Molecular Biology and Evolution, 18, 2298--2305. Pybus, O. G, Rambaut, A. and Harvey, P. H. (2000) An integrated framework for the inference of viral population history from reconstructed genealogies. Genetics, 155, 1429--1437. Nee, S., Holmes, E. C., Rambaut, A. and Harvey, P. H. (1995) Inferring population history from molecular phylogenies. Philosophical Transactions of the Royal Society of London. Series B. Biological Sciences, 349, 25--31. ##### See Also coalescent.intervals, collapsed.intervals, skylineplot, ltt.plot. ##### Aliases • skyline • skyline.phylo • skyline.coalescentIntervals • skyline.collapsedIntervals • find.skyline.epsilon ##### Examples # NOT RUN { # get tree data("hivtree.newick") # example tree in NH format tree.hiv <- read.tree(text = hivtree.newick) # load tree # corresponding coalescent intervals ci <- coalescent.intervals(tree.hiv) # from tree # collapsed intervals cl1 <- collapsed.intervals(ci,0) cl2 <- collapsed.intervals(ci,0.0119) #### classic skyline plot #### sk1 <- skyline(cl1) # from collapsed intervals sk1 <- skyline(ci) # from coalescent intervals sk1 <- skyline(tree.hiv) # from tree sk1 plot(skyline(tree.hiv)) skylineplot(tree.hiv) # shortcut plot(sk1, show.years=TRUE, subst.rate=0.0023, present.year = 1997) #### generalized skyline plot #### sk2 <- skyline(cl2) # from collapsed intervals sk2 <- skyline(ci, 0.0119) # from coalescent intervals sk2 <- skyline(tree.hiv, 0.0119) # from tree sk2 plot(sk2) # classic and generalized skyline plot together in one plot plot(sk1, show.years=TRUE, subst.rate=0.0023, present.year = 1997, col=c(grey(.8),1)) lines(sk2, show.years=TRUE, subst.rate=0.0023, present.year = 1997) legend(.15,500, c("classic", "generalized"), col=c(grey(.8),1),lty=1) # find optimal epsilon parameter using AICc criterion find.skyline.epsilon(ci) sk3 <- skyline(ci, -1) # negative epsilon also triggers estimation of epsilon sk3$epsilon
# }

Documentation reproduced from package ape, version 5.3, License: GPL (>= 2)

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