Modeler: Non-linear regression for curve fitting

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_linear_sat",
  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_linear_sat".

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)
#> 
#> Call:
#> GLI ~ fn_lin_pl_lin(DAP, t1, t2, t3, k, beta) 
#> 
#> Residuals (`Standardized`):
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -1.6286 -0.1474  0.0000 -0.2773  0.0000  0.0000 
#> 
#> Optimization Results `head()`:
#>  uid   t1   t2   t3     k     beta      sse
#>  195 40.1 63.1 91.4 0.325 -0.00809 0.000139
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       1 0.4183 secs        100%  3156 (id)
#> 
# Example 2
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = 195
  )
plot(mod_2, id = 195)

print(mod_2)
#> 
#> Call:
#> Canopy ~ fn_linear_sat(DAP, t1, t2, k) 
#> 
#> Residuals (`Standardized`):
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#> -1.6489  0.0000  0.2623  0.1414  0.5246  1.2064 
#> 
#> Optimization Results `head()`:
#>  uid   t1   t2   k      sse
#>  195 38.8 61.6 100 2.65e-18
#> 
#> Metrics:
#>  Groups      Timing Convergence Iterations
#>       1 0.1261 secs        100%   351 (id)
#>