circtree: Regression Tree for a Circular Response

In circmax: Circular Regression with Maximum Likelihood Estimation and Regression Trees

Description

Trees based maximum-likelihood estimation of parameters for a circular response employing a von Mises distribution.

Usage

circtree(formula, data, start, subset, na.action, weights, offset,
  control = partykit::mob_control(), fit_control = circfit_control(...), ...)

Arguments

formula	A symbolic description of the model to be fit. This should be of type y ~ x1 + x2 where y should be the response variable and x1 and x2 are used as partitioning variables.
data	An optional data frame containing the variables in the model.
start	Starting values of distribution parameters used in the optimization. Currently, not supported.
subset	An optional vector specifying a subset of observations to be used for fitting. Currently, not supported.
na.action	A function which indicates what should happen when the data contain NAs. Currently, not supported.
weights	Optional numeric vector of case weights.
offset	Optional numeric vector with a priori known component to be included in the linear predictor for the location. Currently, not supported.
control	Control arguments passed to mob.
fit_control	A list of control parameters passed to optim in circfit. Default is circfit_control()
...	Arguments to be used to form the default control argument if it is not supplied directly.

Details

Regression trees employing a von Mises distribution is an application of model-based recursive partitioning and unbiased recursive partitioning based on the implementation in mob.

Value

An object of S3 class circtree inheriting from class modelparty or constparty.

See Also

mob, circfit

Examples

sdat <- circtree_simulate()

m1.circtree <- circtree(y ~ x1 + x2, data = sdat)
#plot(m1.circtree, tp_args = list(kernel_density = TRUE)) 
plot(m1.circtree) 

circmax documentation built on May 2, 2019, 5:16 p.m.