ranges.like.bm: n-dimensional Maximumn Likelihood of Range Brownian Motion

In ignacioq/rase: Range Ancestral State Estimation for Phylogeography and Comparative Analyses

Description

Estimates Most Recent Common Ancestor (MRCA) states and the Brownian rate according to a Brownian Motion model of trait evolution using Maximum Likelihood. The input for each trait is a one-dimensional range for every tip of the phylogenetic tree (e.g., if your trait is weight, instead of a 1 value input for each tip, usually the average, you can input the whole range of weights for that tip). It can be used for several dimensions simultaneously.

Usage

ranges.like.bm(tree, lower_bounds, upper_bounds, start_values = NA, dimen = NA)

Arguments

tree	phylogenetic tree of class "phylo".
lower_bounds	a list, with each element being one-dimensional lower bounds for a trait.
upper_bounds	a list, with each element being one-dimensional upper bounds for a trait.
start_values	Optional. A vector of starting values for the Maximum Likelihood optimization. The funtion only estimates the MRCA and the rates for each trait, and that should be the order of the input starting values.
dimen	Optional. Number of dimensions (number of traits being analyzed). If dimen = NA, dimensions are obtained from values.

Value

Returns a list with the following components:

mrcas	Most recent common ancestor estimates for each trait
rates	Brownian motion rate parameters for each trait.
nlm.details	A list with the results from nlm optimization. For details see nlm.

Author(s)

Ignacio Quintero

References

Quintero, I., Keil, P., Jetz, W., Crawford, F. W. 2015 Historical Biogeography Using Species Geographical Ranges. Systematic Biology. doi: 10.1093/sysbio/syv057

See Also

Contrast with point.like.bm

Examples

	# Number of taxa to simulate tree
	ntaxa <- 10

	# Known parameters 
	# for three dimension
	mean_x <- 0
	mean_y <- 0
	mean_z <- 0

	sigma2x <- 1 
	sigma2y <- 2
	sigma2z <- 3

	# Create a random tree
	tree <- ape::rtree(n = ntaxa)

	# Create random data according to tree structure
	x_locs <- as.numeric(mvtnorm::rmvnorm(1, rep(mean_x,ntaxa), sigma=sigma2x*vcv(tree)))
	y_locs <- as.numeric(mvtnorm::rmvnorm(1, rep(mean_y,ntaxa), sigma=sigma2y*vcv(tree)))
	z_locs <- as.numeric(mvtnorm::rmvnorm(1, rep(mean_z,ntaxa), sigma=sigma2z*vcv(tree)))

	# random shifts to the mean to create ranges
	xshift <- rexp(ntaxa)
	yshift <- rexp(ntaxa)
	zshift <- rexp(ntaxa)
	x_lower <- x_locs - xshift
	x_upper <- x_locs + xshift
	y_lower <- y_locs - yshift
	y_upper <- y_locs + yshift
	z_lower <- z_locs - zshift
	z_upper <- z_locs + zshift

	# store rectangles: this is the trait data:
	lower_bounds <- list(xl=x_lower, yl=y_lower, zl=z_lower ) 
	upper_bounds <- list(xu=x_upper, yu=y_upper, zu=z_upper) 

	## Not run: 
	#run range.like.bm
		range_results <- ranges.like.bm(tree, lower_bounds, upper_bounds)
	
## End(Not run)

ignacioq/rase documentation built on Feb. 20, 2022, 3:16 p.m.