Continuous variables are modeled using phylolm::phylolm, while binary traits are modeled using phylolm::phyloglm.
phylo_path(
model_set,
data,
tree,
model = "lambda",
method = "logistic_MPLE",
order = NULL,
parallel = NULL,
na.rm = TRUE,
...
)A list of directed acyclic graphs. These are matrices,
typically created with define_model_set.
A data.frame with data. If you have binary variables, make
sure they are either character values or factors!
A phylogenetic tree of class phylo.
The evolutionary model used for the regressions on continuous variables. See phylolm::phylolm for options and details. Defaults to Pagel's lambda model
The estimation method for the binary models. See phylolm::phyloglm for options and details. Defaults to logistic MPLE.
Causal order of the included variable, given as a character vector. This is used to determine which variable should be the dependent in the dsep regression equations. If left unspecified, the order will be automatically determined. If the combination of all included models is itself a DAG, then the ordering of that full model is used. Otherwise, the most common ordering between each pair of variables is used to create a general ordering.
Superseded From v1.2 phylopath uses the future package
for all parallel processing, see details.
Should rows that contain missing values be dropped from the data as necessary (with a message)?
Arguments passed on to phylolm:
lower.bound: optional lower bound for the optimization of the phylogenetic model parameter.
upper.bound: optional upper bound for the optimization of the phylogenetic model parameter.
starting.value: optional starting value for the optimization of the phylogenetic model parameter.
measurement_error: a logical value indicating whether there is measurement error sigma2_error (see Details).
Arguments passed on to phyloglm:
btol: bound on the linear predictor to bound the searching space.
log.alpha.bound: bound for the log of the parameter alpha.
start.beta: starting values for beta coefficients.
start.alpha: starting values for alpha (phylogenetic correlation).
A phylopath object, with the following components:
for each model a table with separation statements and statistics.
the DAGs
the supplied data
the supplied tree
the employed model of evolution in phylolm
the employed method in phyloglm
any additional arguments given, these are passed on to downstream functions
any warnings generated by the models
Parallel processing: From v1.2, phylopath uses the future framework
for parallel processing. This is compatible with the parallel computation
within the underlying phylolm, making it easy to enable parallel
processing of multiple models, and of bootstrap replicates. To enable,
simply set a parallel plan() using the future package. Typically, you'll
want to run future::plan("multisession", workers = n), where n is the
number of cores. Now parallel processing is enabled. Return to sequential
processing using future::plan("sequential")
#see vignette('intro_to_phylopath') for more details
candidates <- define_model_set(
A = NL ~ BM,
B = NL ~ LS,
.common = c(LS ~ BM, DD ~ NL)
)
p <- phylo_path(candidates, rhino, rhino_tree)
# Printing p gives some general information:
p
#> A phylogenetic path analysis, on the variables:
#> Continuous: BM LS NL DD
#> Binary:
#>
#> Evaluated for these models: A B
#>
#> Containing 6 phylogenetic regressions, of which 4 unique
# And the summary gives statistics to compare the models:
summary(p)
#> model k q C p CICc delta_CICc l w
#> A A 3 7 2.65 0.85113 17.9 0.0 1.00e+00 1.00e+00
#> B B 3 7 22.11 0.00115 37.3 19.5 5.94e-05 5.94e-05