3 \title{Objective Function Employed in Nonparametric Rate Smoothing}
5 NPRS.criterion(phy, chrono, expo = 2, minEdgeLength = 1e-06)
8 \item{phy}{A non-clock-like phylogenetic tree (i.e. an object of class
9 \code{"phylo"}), where the branch lengths are measured in
11 \item{chrono}{A chronogram, i.e. a clock-like tree (i.e. an object of
12 class \code{"phylo"}), where the branch lengths are measured in
14 \item{expo}{Exponent in the objective function (default value: 2)}
15 \item{minEdgeLength}{Minimum edge length in the phylogram (default
16 value: 1e-06). If any branch lengths are smaller then they will be
20 \code{NPRS.criterion} computes the objective function to be minimized
21 in the NPRS (nonparametric rate smoothing) algorithm described in
25 Please refer to Sanderson (1997) for mathematical details. Note that
26 is is not computationally efficient to optimize the branch lengths in
27 a chronogram by using \code{NPRS.criterion} - please use
28 \code{\link{chronogram}} instead.
31 \code{NPRS.criterion} returns the value of the objective function given
32 a phylogram and a chronogram.
34 \author{Gangolf Jobb (\url{http://www.treefinder.de}) and Korbinian
35 Strimmer (\url{http://www.stat.uni-muenchen.de/~strimmer/})
38 \code{\link{ratogram}}, \code{\link{chronogram}}
41 Sanderson, M. J. (1997) A nonparametric approach to estimating
42 divergence times in the absence of rate constancy. \emph{Molecular
43 Biology and Evolution}, \bold{14}, 1218--1231.
47 data("landplants.newick") # example tree in NH format
48 tree.landplants <- read.tree(text = landplants.newick)
52 plot(tree.landplants, label.offset = 0.001)
55 chrono.plants <- chronogram(tree.landplants)
58 plot(chrono.plants, label.offset = 0.001)
60 # value of NPRS function for our estimated chronogram
61 NPRS.criterion(tree.landplants, chrono.plants)