prob_of_states_from_prior_prob_areas: Calculate probability of detection data for each OTU at each...
In BioGeoBEARS: BioGeography with Bayesian (and Likelihood) Evolutionary Analysis in R Scripts

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

View source: R/BioGeoBEARS_detection_v1.R

This function calculates P(data|range,dp), i.e. the probability of some detection and taphonomic control counts, given the true geographic range/state, and dp, a detection probability (and, optionally, a false detection probability, fdp).

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  prob_of_states_from_prior_prob_areas(states_list_0based_index,
    numareas = NULL, prior_prob_presence = 0.01,
    null_range_gets_0_prob = TRUE, normalize_probs = TRUE)

`states_list_0based_index`	A states_list, 0-based, e.g. from `rcpp_areas_list_to_states_list`.
`numareas`	The number of areas being considered in the analysis. If `NULL` (default), this is calculated to be the maximum range length, or one plus the maximum 0-based index in any of the ranges.
`prior_prob_presence`	The prior probability of presence, i.e. when no detection or taphonomic control data whatsoever is available. Default is set to 0.01 which expresses my totally uninformed bias that in whatever your data is, your species of interest probably doesn't live in the typical area you are looking at.
`null_range_gets_0_prob`	If `TRUE` (default), then the null range is given zero probability. A null range has no areas occupied. This is equivalent to saying that you are sure/are willing to assume that the OTU exists somewhere in your study area, at the timepoint being considered. Null ranges are identified by length=1, containing NULL, NA, "", "_", etc.
`normalize_probs`	If `TRUE`, the probabilities of each range will be normalized so that they sum to 1. Otherwise, they won't.

This function performs the operation for all states/ranges for all tips.

The idea of taphonomic controls dates back at least to work of Bottjer & Jablonski (1988). The basic idea is that if you have taxa of roughly similar detectability, then detections of other taxa give some idea of overall detection effort. Obviously this is a very simple model that can be criticized in any number of ways (different alpha diversity in each region, different detectability of individual taxa, etc.), but it is a useful starting point as there has been no implementation of any detection model in historical/phylogenetic biogeography to date.

One could imagine (a) every OTU and area has a different count of detections and taphonomic control detections, or (b) the taphonomic control detections are specified by area, and shared across all OTUs. Situation (b) is likely more common, but this function assumes (a) as this is the more thorough case. Behavior (b) could be reproduced by summing each column, and/or copying this sum to all cells for a particular area.

prob_of_each_range The probability of each range, given the prior probability of presence in each area.

Go BEARS!

Nicholas J. Matzke matzke@berkeley.edu

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

Matzke_2012_IBS

Bottjer_Jablonski_1988

rcpp_areas_list_to_states_list, Pdata_given_rangerow, calc_obs_like, mapply, read_detections, read_controls

testval=1

prior_prob_presence = 0.01

areas = c("K", "O", "M", "H")
numareas = length(areas)
states_list_0based_index =
rcpp_areas_list_to_states_list(areas=areas, maxareas=4, include_null_range=TRUE)
states_list_0based_index
numareas = 4

mean_frequency=0.1
dp=1
fdp=0

prob_of_states_from_prior_prob_areas(states_list_0based_index, numareas=numareas,
prior_prob_presence=prior_prob_presence, null_range_gets_0_prob=TRUE,
normalize_probs=TRUE)

prob_of_states_from_prior_prob_areas(states_list_0based_index, numareas=numareas,
prior_prob_presence=prior_prob_presence, null_range_gets_0_prob=TRUE,
normalize_probs=FALSE)

prob_of_states_from_prior_prob_areas(states_list_0based_index, numareas=numareas,
prior_prob_presence=prior_prob_presence, null_range_gets_0_prob=FALSE,
normalize_probs=TRUE)

prob_of_states_from_prior_prob_areas(states_list_0based_index, numareas=numareas,
prior_prob_presence=prior_prob_presence, null_range_gets_0_prob=FALSE,
normalize_probs=FALSE)