modeler: Modeler: Non-linear regression for curve fitting
In flexFitR: Flexible Non-Linear Least Square Model Fitting

modeler

R Documentation

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()
)

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)`.

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.

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)

flexFitR documentation built on April 16, 2025, 5:09 p.m.