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CircularRegression fits regression models for circular response data, such as movement directions or angles measured in radians. The package implements the general angular regression framework of Rivest, Duchesne, Nicosia and Fortin (2016), including homogeneous angular regression, consensus regression, a two-step workflow, and selected special-case wrappers.
install.packages("remotes")
remotes::install_github("AurelienNicosiaULaval/CircularRegression")library(CircularRegression)
data(bison)
d <- bison[seq_len(100), ]
fit <- circular_regression(
y.dir ~ y.prec + x.meadow:z.meadow,
data = d
)
summary(fit)
coef(fit)
head(predict(fit))The formula syntax uses angular variables as terms. A term of the
form x adds a direction directly. A term of the form
x:z adds a direction x weighted by a finite
non-negative modifier z.
The original interfaces remain available:
angular() fits the homogeneous angular regression
model.consensus() fits the consensus angular regression
model.angular_two_step() fits the consensus model, selects a
reference direction, and then fits the homogeneous model.angular_re() fits the random-intercept extension for
clustered circular outcomes.The package provides S3 methods for printing, summarising, coefficients, fitted values, residuals, predictions, plots, information criteria, and log-likelihoods where appropriate.
Rivest, L.-P., Duchesne, T., Nicosia, A. and Fortin, D. (2016). A general angular regression model for the analysis of data on animal movement in ecology. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(3), 445-463.
Rivest, L.-P. and Kato, S. (2019). A random-effects model for clustered circular data. Canadian Journal of Statistics, 47(4), 712-728.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.