vistla | R Documentation |
Detects influence paths.
vistla(x, ...)
## S3 method for class 'formula'
vistla(formula, data, ..., yn)
## S3 method for class 'data.frame'
vistla(
x,
y,
...,
flow,
iomin,
targets,
estimator = c("mle", "kt"),
verbose = FALSE,
yn = "Y",
threads
)
## Default S3 method:
vistla(x, ...)
x |
data frame of predictors. |
... |
pass-through arguments, ignored. |
formula |
alternatively, formula describing the task, in a form |
data |
|
yn |
name of the root ( |
y |
vistla tree root, a feature from which influence paths will be traced. |
flow |
algorithm mode, specifying the iota function which gives local score to an edge of an edge graph.
If in doubt, use the default, |
iomin |
score threshold below which path is not considered further.
The higher value the less paths are generated, which also lowers the time taken by the function.
The default value of 0 turns of this filtering.
The same effect can be later achieved with the |
targets |
a vector of target feature names.
If given, the algorithm will stop just after reaching the last of them, rather than after tracing all paths from the root.
The same effect can be later achieved with the |
estimator |
mutual information estimator to use.
|
verbose |
when set to |
threads |
number of threads to use. When missing or set to 0, vistla uses all available cores. |
The tracing results represented as an object of a class vistla
.
Use paths
and path_to
functions to extract individual paths,
branches
to get the whole tree and mi_scores
to get the basic score matrix.
"Kendall transformation brings a robust categorical representation of ordinal data" M.B. Kursa. SciRep 12, 8341 (2022).
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