FixedEffectsSet: Create a set of fixed effects models

In allometric: Structured Allometric Models for Trees

Create a set of fixed effects models

Description

A FixedEffectsSet represents a group of fixed-effects models that all have the same functional structure. Fitting a large family of models (e.g., for many different species) using the same functional structure is a common pattern in allometric studies, and FixedEffectsSet facilitates the installation of these groups of models by allowing the user to specify the parameter estimates and descriptions in a dataframe.

Usage

FixedEffectsSet(
  response,
  covariates,
  parameter_names,
  predict_fn,
  model_specifications,
  descriptors = list(),
  response_definition = NA_character_,
  covariate_definitions = list()
)

Arguments

response	A named list containing one element, with a name representing the response variable and a value representing the units of the response variable using the units::as_units function.
covariates	A named list containing the covariate specifications, with names representing the covariate name and the values representing the units of the coavariate using the units::as_units function
parameter_names	A character vector naming the columns in model_specifications that represent the parameters
predict_fn	A function that takes the covariate names as arguments and returns a prediction of the response variable. This function should be vectorized.
model_specifications	A dataframe such that each row of the dataframe provides model-level descriptors and parameter estimates for that model. Models must be uniquely identifiable using the descriptors. This is usually established using the load_parameter_frame() function.
descriptors	An optional named list of descriptors that describe the context of the allometric model
response_definition	A string containing an optional custom response definition, which is used instead of the description given by the variable naming system.
covariate_definitions	An optional named list of custom covariate definitions that will supersede the definitions given by the variable naming system. The names of the list must match the covariate names given in covariates.

Value

A set of fixed effects models

Slots

response_unit

A one-element list with the name indicating the response variable and the value as the response variable units obtained using units::as_units()

covariate_units

A list containing the covariate names as names and values as the values of the covariate units obtained using units::as_units()

predict_fn

The prediction function, which takes covariates as arguments and returns model predictions

descriptors

A tibble::tbl_df containing the model descriptors

set_descriptors

A tibble::tbl_df containing the set descriptors

pub_descriptors

A tibble::tbl_df containing the publication descriptors

citation

A RefManageR::BibEntry object containing the reference publication

covariate_definitions

User-provided covariate definitions

model_type

The model type, which is parsed from the response_unit name

parameter_names

A character vector indicating the parameter names

model_specifications

A tibble::tbl_df of model specifications, where each row reprents one model identified with descriptors and containing the parameter estimates.

Examples

fixef_set <- FixedEffectsSet(
  response = list(
    vsia = units::as_units("ft^3")
  ),
  covariates = list(
    dsob = units::as_units("in")
  ),
  predict_fn = function(dsob) {
    a * dsob^2
  },
  parameter_names = "a",
  model_specifications = tibble::tibble(mod = c(1,2), a = c(1, 2))
)

allometric documentation built on Nov. 8, 2023, 1:07 a.m.