symbolic_to_Q_matrix_exper: Experimental version of 'symbolic_to_Q_matrix_exper',...
In BioGeoBEARS: BioGeography with Bayesian (and Likelihood) Evolutionary Analysis in R Scripts

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Still experimental.

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  symbolic_to_Q_matrix_exper(dedf, cellsplit = "\\+",
    mergesym = "*", d = 0.1, e = 0.01,
    basefreqs = rep(1, nrow(dedf))/nrow(dedf), ...)

`dedf`	The transition matrix or dispersal-extinction data.frame (dedf), contains the actual text of the formulas by which the transition probability matrix would be calculated.
`cellsplit`	The symbol to split the formulas on. Default "\\+" (plus symbol, with escape code).
`mergesym`	The symbol to merge the formulas with. Default "+".
`d`	The dispersal/range expansion rate. Default `d=0.1`.
`e`	The extinction/range contraction rate. Default `e=0.01`.
`basefreqs`	Base frequencies, i.e. the equilibrium probabilities of the different states; the meaning of such an idea is debatable in the context of a LAGRANGE-like model where the null range (extinct everywhere) is included in the matrix and is a nonreversible absorbing state. Default is `rep(1,nrow(dedf))/nrow(dedf)`.
`...`	Additional arguments to pass to `symbolic_cell_to_relprob_cell` via `sapply`, and thence to cell`strsplit`.

This function takes a transition probability matrix (in text form) and converts it to an instantaneous rate matrix (Q matrix), given values for d, e, or other parameters in the text formulas.

This is not particularly fast, but good for illustrative purposes.

dedf_vals The output data.frame, contains the Q matrix

Go BEARS!

Nicholas J. Matzke matzke@berkeley.edu

http://phylo.wikidot.com/matzke-2013-international-biogeography-society-poster

Matzke_2012_IBS

FosterIdiots

areas_list_to_states_list_old, make_relprob_matrix_de areas_list_to_states_list_new

testval=1

states_list = list("_", c("A"), c("B"), c("C"), c("A","B"),
c("B","C"), c("A","C"), c("A","B","C"))

states_list = areas_list_to_states_list_new(areas=c("A","B","C"),
include_null_range=TRUE, split_ABC=TRUE)
states_list

dedf = make_relprob_matrix_de(states_list=states_list, split_ABC=FALSE,
split="", remove_simultaneous_events=TRUE, add_multiple_Ds=TRUE,
dispersal_multiplier_matrix=make_dispersal_multiplier_matrix(states_list=states_list))
dedf

# Right
Qmat = symbolic_to_Q_matrix_exper(dedf, cellsplit="\\\\+", mergesym="+", d=0.1, e=0.01)
Qmat

# Wrong
Qmat = symbolic_to_Q_matrix_exper(dedf, cellsplit="\\\\+", mergesym="*", d=0.1, e=0.01)
Qmat

# You don't have to split, if the formulas are directly parsable
Qmat = symbolic_to_Q_matrix_exper(dedf, cellsplit="yadda", mergesym="", d=0.1, e=0.01)
Qmat

# Compare to symbolic_to_Q_matrix
Qmat = symbolic_to_Q_matrix(dedf, cellsplit="yadda", mergesym="", d=0.1, e=0.01)
Qmat