flexFitR (version 1.2.2)

modeler: Modeler: Non-linear regression for curve fitting

Description

A versatile function for performing non-linear least squares optimization on grouped data. It supports customizable optimization methods, flexible initial/fixed parameters, and parallel processing.

Usage

modeler(
  data,
  x,
  y,
  grp,
  keep,
  fn = "fn_lin_plat",
  parameters = NULL,
  lower = -Inf,
  upper = Inf,
  fixed_params = NULL,
  method = c("subplex", "pracmanm", "anms"),
  subset = NULL,
  options = modeler.options(),
  control = list()
)

Value

An object of class modeler, which is a list containing the following elements:

param

Data frame containing optimized parameters and related information.

dt

Data frame with input data, fitted values, and residuals.

metrics

Metrics and summary of the models.

execution

Total execution time for the analysis.

response

Name of the response variable analyzed.

keep

Metadata retained based on the keep argument.

fun

Name of the curve-fitting function used.

parallel

List containing parallel execution details (if applicable).

fit

List of fitted models for each group.

Arguments

data

A data.frame containing the input data for analysis.

x

The name of the column in data representing the independent variable (x points).

y

The name of the column in data containing the dependent variable to analyze (response variable).

grp

Column(s) in data used as grouping variable(s). Defaults to NULL. (Optional)

keep

Names of columns to retain in the output. Defaults to NULL. (Optional)

fn

A string. The name of the function used for curve fitting. Example: "fn_lin". Defaults to "fn_lin_plat".

parameters

A numeric vector, named list, or data.frame providing initial values for parameters:

Numeric vector

Named vector specifying initial values (e.g., c(k = 0.5, t1 = 30)).

Data frame

Requires a uid column with group IDs and parameter values for each group.

List

Named list where parameter values can be numeric or expressions (e.g., list(k = "max(y)", t1 = 40)).

Defaults to NULL.

lower

A numeric vector specifying lower bounds for parameters. Defaults to -Inf for all parameters.

upper

A numeric vector specifying upper bounds for parameters. Defaults to Inf for all parameters.

fixed_params

A list or data.frame for fixing specific parameters:

List

Named list where parameter values can be numeric or expressions (e.g., list(k = "max(y)", t1 = 40)).

Data frame

Requires a uid column for group IDs and fixed parameter values.

Defaults to NULL.

method

A character vector specifying optimization methods. Check available methods using list_methods() and their dependencies using optimx::checkallsolvers(). Defaults to c("subplex", "pracmanm", "anms").

subset

A vector (optional) containing levels of grp to filter the data for analysis. Defaults to NULL (all groups are included).

options

A list of additional options. See modeler.options()

progress

Logical. If TRUE a progress bar is displayed. Default is FALSE. Try this before running the function: progressr::handlers("progress", "beepr").

parallel

Logical. If TRUE the model fit is performed in parallel. Default is FALSE.

workers

The number of parallel processes to use. parallel::detectCores()

trace

If TRUE , convergence monitoring of the current fit is reported in the console. FALSE by default.

return_method

Logical. If TRUE, includes the optimization method used in the result. Default is FALSE.

control

A list of control parameters to be passed to the optimization function. For example: list(maxit = 500).

Examples

library(flexFitR)
data(dt_potato)
explorer <- explorer(dt_potato, x = DAP, y = c(Canopy, GLI), id = Plot)
# Example 1
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = GLI,
    grp = Plot,
    fn = "fn_lin_pl_lin",
    parameters = c(t1 = 38.7, t2 = 62, t3 = 90, k = 0.32, beta = -0.01),
    subset = 195
  )
plot(mod_1, id = 195)
print(mod_1)
# Example 2
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_lin_plat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = 195
  )
plot(mod_2, id = 195)
print(mod_2)