Von Mises regression of concentration on covariates
Source:R/concentration_regression.R
concentration_regression.RdFits a von Mises model in which the concentration \(\kappa\) varies with one
or more covariates while the mean direction \(\mu\) is a single constant:
\(\theta_i \sim \mathrm{vM}(\mu, \kappa_i)\) with
\(\kappa_i = h(x_i'\gamma)\) for an inverse link \(h\). It is the
concentration-regression complement to circ_regression (which
models the mean with a constant concentration), and the estimator that pairs
with simulate_tracks's concentration_slope. The model is
fitted by maximum likelihood with optim.
Usage
concentration_regression(
data,
formula,
link = c("log", "identity"),
init = NULL
)
# S3 method for class 'concentration_regression'
summary(object, conf.level = 0.95, ...)
# S3 method for class 'concentration_regression'
predict(object, newdata = NULL, ...)
# S3 method for class 'concentration_regression'
fitted(object, ...)
# S3 method for class 'concentration_regression'
print(x, ...)Arguments
- data
A data frame containing the response and predictor columns.
- formula
A formula
heading ~ x1 + x2; the LHS is the angle column (radians), the RHS the covariate(s) modelling \(\kappa\). The intercept is retained (it is the baseline concentration on the link scale).- link
Link relating \(\kappa\) to the linear predictor:
"log"(default; \(\kappa = e^{x'\gamma}\), always positive) or"identity"(\(\kappa = x'\gamma\), floored at a small positive value).- init
Optional numeric starting values for the concentration coefficients (length = number of design-matrix columns, intercept first).
- object
A
concentration_regressionobject.- conf.level
Confidence level for the coefficient interval. Default 0.95.
- ...
Unused.
- newdata
Optional data frame of new covariate values. Default uses the training data.
- x
A
concentration_regressionobject.