View source: R/chrono.subsets.R
chrono.subsets  R Documentation 
Splits the data into a chronological (time) subsets list.
chrono.subsets(
data,
tree = NULL,
method,
time,
model,
inc.nodes = FALSE,
FADLAD = NULL,
verbose = FALSE,
t0 = FALSE,
bind.data = FALSE
)
data 
A 
tree 

method 
The time subsampling method: either 
time 
Either a single 
model 
One of the following models: 
inc.nodes 
A 
FADLAD 

verbose 
A 
t0 
If 
bind.data 
If 
The data is considered as the multidimensional space with rows as elements and columns as dimensions and is not transformed (e.g. if ordinated with negative eigen values, no correction is applied to the matrix).
If method = "continuous"
and when the sampling is done along an edge of the tree, the data selected for the time subsets can be one of the following:
Punctuated models:
"acctran"
: always selecting the value from the ancestral node.
"deltran"
: always selecting the value from the descendant node or tip.
"random"
: randomly selecting between the ancestral node or the descendant node/tip.
"proximity"
: selecting the ancestral node or the descendant node/tip with a probability relative to branch length.
Gradual models:
"equal.split"
: randomly selecting from the ancestral node or the descendant node or tip with a 50% probability each.
"gradual.split"
: selecting the ancestral node or the descendant with a probability relative to branch length.
N.B. "equal.split"
and "gradual.split"
differ from the punctuated models by outputting a node/tip probability table rather than simply the node and the tip selected. In other words, when bootstrapping using boot.matrix
, the two former models will properly integrate the probability to the bootstrap procedure (i.e. different tips/nodes can be drawn) and the two latter models will only use the one node/tip determined by the model before the bootstrapping.
Thomas Guillerme
Guillerme T. & Cooper N. 2018. Time for a rethink: time subsampling methods in disparitythroughtime analyses. Palaeontology. DOI: 10.1111/pala.12364.
tree.age
, slice.tree
, cust.subsets
, boot.matrix
, dispRity
.
## Load the Beck & Lee 2014 data
data(BeckLee_tree) ; data(BeckLee_mat50)
data(BeckLee_mat99) ; data(BeckLee_ages)
## Time binning (discrete method)
## Generate two discrete time bins from 120 to 40 Ma every 40 Ma
chrono.subsets(data = BeckLee_mat50, tree = BeckLee_tree, method = "discrete",
time = c(120, 80, 40), inc.nodes = FALSE, FADLAD = BeckLee_ages)
## Generate the same time bins but including nodes
chrono.subsets(data = BeckLee_mat99, tree = BeckLee_tree, method = "discrete",
time = c(120, 80, 40), inc.nodes = TRUE, FADLAD = BeckLee_ages)
## Time slicing (continuous method)
## Generate five equidistant time slices in the dataset assuming a proximity
## evolutionary model
chrono.subsets(data = BeckLee_mat99, tree = BeckLee_tree,
method = "continuous", model = "acctran", time = 5,
FADLAD = BeckLee_ages)
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