Circular-linear regression of a heading on linear covariates

Fits the Fisher-Lee circular-linear regression \(\theta_i \sim \mathrm{vM}(\mu_0 + 2\arctan(x_i'\beta), \kappa)\) via lm.circular (type = "c-l"), behind a formula interface. The response is a heading in radians (unit-circle convention); the right-hand side supplies one or more linear covariates (factors and interactions are expanded by model.matrix).

Usage

circ_regression(data, formula, init = NULL)

# S3 method for class 'circ_regression'
summary(object, conf.level = 0.95, ...)

# S3 method for class 'circ_regression'
predict(object, newdata = NULL, ...)

# S3 method for class 'circ_regression'
fitted(object, ...)

# S3 method for class 'circ_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.

init

Optional numeric starting values for the slope coefficients (length = number of predictor columns). Default a vector of zeros.

object

A circ_regression object.

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 circ_regression object.

Value

An S3 object of class "circ_regression". Use summary() for a tidy coefficient data frame, predict() / fitted() for fitted mean angles, and print() for a compact report. On non-convergence or too few rows, converged is FALSE and the coefficients are NA.

References

Fisher, N. I. & Lee, A. J. (1992). Regression models for an angular response. Biometrics 48, 665-677. Mardia, K. V. & Jupp, P. E. (2000). Directional Statistics. Wiley.

See also

circ_cor, vonmises_fit, simulate_tracks