MixedEffectsModel: Create a mixed effects model

In allometric: Structured Allometric Models for Trees

Create a mixed effects model

Description

MixedEffectsModel represents an allometric model that uses fixed and random effects.

Usage

MixedEffectsModel(
  response,
  covariates,
  predict_ranef,
  predict_fn,
  parameters,
  fixed_only = FALSE,
  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
predict_ranef	A function that predicts the random effects, takes any named covariates in covariates as arguments
predict_fn	A function that takes the covariate names as arguments and returns a prediction of the response variable. This function should be vectorized.
parameters	A named list of parameters and their values
fixed_only	A boolean value indicating if the model produces predictions using only fixed effects. This is useful when publications do not provide sufficient information to predict the random effects.
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

An instance of MixedEffectsModel

Slots

parameters

A named list of parameters and their values

predict_fn_populated

The prediction function populated with the parameter values

specification

A tibble::tbl_df of the model specification, which are the parameters and the descriptors together

predict_ranef

The function that predicts the random effects

predict_ranef_populated

The function that predicts the random effects populated with the fixed effect parameter estimates

fixed_only

A boolean value indicating if the model produces predictions using only fixed effects

Examples

MixedEffectsModel(
  response = list(
    hst = units::as_units("m")
  ),
  covariates = list(
    dsob = units::as_units("cm")
  ),
  parameters = list(
    beta_0 = 40.4218,
    beta_1 = -0.0276,
    beta_2 = 0.936
  ),
  predict_ranef = function() {
    list(b_0_i = 0, b_2_i = 0)
  },
  predict_fn = function(dsob) {
    1.37 + (beta_0 + b_0_i) * (1 - exp(beta_1 * dsob)^(beta_2 + b_2_i))
  },
  fixed_only = TRUE
)

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