gaussplotR (version 0.2.5)

autofit_gaussian_2D: Automatically determine the best-fitting 2D-Gaussian for a data set

Description

Automatically determine the best-fitting 2D-Gaussian for a data set

Usage

autofit_gaussian_2D(
  data,
  comparison_method = "rmse",
  maxiter = 1000,
  simplify = TRUE
)

Arguments

data

A data.frame that contains the raw data (generally rectilinearly gridded data, but this is not a strict requirement). Columns must be named "X_values", "Y_values" and "response".

comparison_method

One of "rmse", "rss", or "AIC"; what metric should be used to determine the "best-fitting" Gaussian?

maxiter

Default 1000. A positive integer specifying the maximum number of iterations allowed. See stats::nls.control() for more details.

simplify

TRUE or FALSE. If TRUE, return only the coefficients, model, model_error_stats, and fit_method for the best-fitting model. If FALSE, a model comparison table is also included in the returned list as $model_comparison. This table is obtained via compare_gaussian_fits().

Value

If simplify = TRUE, a list with the components:

• "coefs" A data.frame of fitted model parameters.

• "model" The model object, fitted by stats::nls().

• "model_error_stats" A data.frame detailing the rss, rmse, deviance, and AIC of the fitted model.

• "fit_method" A character vector that indicates which method and orientation strategy was used by this function.

If simplify = FALSE, a model comparison table is also included in the returned list as $model_comparison. This table is obtained via compare_gaussian_fits().

Details

This function runs fit_gaussian_2D() three times: once for each of the "main" types of models: 1) elliptical, unconstrained; 2) elliptical, log; 3) circular. In all three cases, amplitudes and orientations are unconstrained. The function compare_gaussian_fits() is then used to determine which of these three models is the best-fitting, using the comparison_method argument to make the decision.

Examples

# NOT RUN {
if (interactive()) {
}
# }