These functions wrap various modelling tools to ensure a consistent input
for trending functions. They work by capturing the underlying model call
and decoupling it from the data specification. This makes it easy to use
the same underlying model specification and fitting procedure across
different data sets. See details for available model interfaces.
Usage
lm_model(formula, ...)
glm_model(formula, family = gaussian, ...)
glm_nb_model(formula, ...)
brm_model(formula, ...)
Value
A trending_model object.
Arguments
formula
The formula of the model, with the response variable on the
left of a tilde symbol, and predictors on the right hand-side; variable
names used in the formula will need to be matched by columns in the data
input to other functions.
...
Further arguments passed to the underlying models with the
exception of data.
family
Link function to be used for the glm model.
Author
Tim Taylor
Details
The following interfaces are available:
lm_model: interface for linear models implemented in
stats::lm().
glm_model: interface for generalised linear models (GLMs) implemented in
stats::glm().
glm_nb_model: interface for negative binomial generalied linear models
implemented in MASS::glm.nb().
brm_model: interface for Bayesian regression models implemented in
brms::brm().
These interfaces will accept the same inputs as the underlying model
functions but do not require, nor will they accept, a data argument.
Fitting is handled instead by the fit() generic and associated methods.