Title:	Managing Results from Maximum Likelihood Estimation
Version:	1.0.0
Description:	Managing and exploring parameter estimation results derived from Maximum Likelihood Estimation (MLE) using the 'likelihood' package. It provides functions for organizing, visualizing, and summarizing MLE outcomes, streamlining statistical analysis workflows. By improving interpretation and facilitating model evaluation, it helps users gain deeper insights into parameter estimation and model fitting, making MLE result exploration more efficient and accessible. See Goffe et al. (1994) <doi:10.1016/0304-4076(94)90038-8> for details on MLE, and Canham and Uriarte (2006) <doi:10.1890/04-0657> for application of MLE using 'likelihood'.
License:	GPL (≥ 3)
Encoding:	UTF-8
URL:	https://github.com/ajpelu/likelihoodTools, https://ajpelu.github.io/likelihoodTools/
BugReports:	https://github.com/ajpelu/likelihoodTools/issues
Depends:	R (≥ 4.1.0)
RoxygenNote:	7.3.2
Imports:	dplyr, ggplot2, rlang
Suggests:	testthat (≥ 3.0.0), likelihood, kableExtra, MASS, patchwork, spelling
Config/testthat/edition:	3
Config/Needs/website:	rmarkdown
Language:	en-US
NeedsCompilation:	no
Packaged:	2025-02-28 14:53:55 UTC; ajpelu
Author:	Antonio Jesus Perez-Luque [cre, aut, cph]
Maintainer:	Antonio Jesus Perez-Luque <ajpelu@gmail.com>
Repository:	CRAN
Date/Publication:	2025-03-04 12:40:02 UTC

likelihoodTools: Tools for managing results from maximum likelihood estimation

Description

Tools for managing and exploring parameter estimation results derived from Maximum Likelihood Estimation (MLE) using the 'likelihood' package.

Author(s)

Maintainer: Antonio Jesús Pérez-Luque ajpelu@gmail.com (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/ajpelu/likelihoodTools

• https://ajpelu.github.io/likelihoodTools/

• Report bugs at https://github.com/ajpelu/likelihoodTools/issues

Extract and format results from Simulated Annealing (Maximum Likelihood Estimation)

Description

Extract and format results from Simulated Annealing (Maximum Likelihood Estimation)

Usage

mle_format(x, yvar)

Arguments

Value

A dataframe with outputs from the results of the simulated annealing maximum parameter estimation. This dataframe contains the following columns (see help in likelihood::anneal()):

• max_likeli The maximum likelihood value of the model

• n_params The number of the estimated parameters

• aic_corr The value of Akaike's Information Criterion “corrected” for small sample size. See the "Simulated Annealing Algorithm" help page of the likelihood package for more information.

• aic The value of Akaike's Information Criterion. See the "Simulated Annealing Algorithm" help page of the likelihood package for more information. slope Slope of observed values linearly regressed on those predicted by model, using the parameter maximum likelihood estimates. The intercept is forced at zero.

• R2 Proportion of variance explained by the model relative to that explained by the simple mean of the data.

• rmse Root Mean Square Error, i.e. the standard deviation of the residuals. It is computed as:

RMSE=\sqrt{ \frac{\sum_{i=1}^{N}(obs_i - exp_i)^2}{n -1}}

Examples

# Get the results of the maximum likelihood estimation from the example in
# the anneal function of the likelihood pkg.

library(likelihood)
data(crown_rad)
dataset <- crown_rad

# Create our model function
modelfun <- function (a, b, DBH) {a + b * DBH}

# Compute the MLE of the parameters
results <- anneal(model = modelfun,
  par = list(a = 0, b = 0),
  var = list(DBH = "DBH", x = "Radius", mean = "predicted",
             sd = 0.815585, log = TRUE),
  source_data = dataset,
  par_lo = list(a = 0, b = 0),
  par_hi = list(a = 50, b = 50),
  pdf = dnorm,
  dep_var = "Radius",
  max_iter = 20000,
  show_display = FALSE)

# Format the results
mle_format(results, yvar = "DBH")

Plots Observed vs. Predicted MLE

Description

Plots observed values vs. predicted values. The predicted values are obtained from the model with the parameters values estimated by maximum likelihood estimation using simulated annealing.

Usage

mle_plot_observed(
  x,
  yvar,
  annotate = TRUE,
  lab_x = "Observed",
  lab_y = "Predicted",
  ...
)

Arguments

Value

A ggplot object displaying the observed vs. predicted values, with optional annotations for R^2 and regression slope.

Plots Residuals vs. Predicted MLE

Description

Plots residuals (observed - residuals) values vs. predicted values. The predicted values are obtained from the model with the parameters values estimated by maximum likelihood estimation using simulated annealing.

Usage

mle_plot_residuals(
  x,
  yvar,
  lab_residuals = "Residuals",
  lab_predicted = "Predicted",
  ...
)

Arguments

Value

A ggplot object displaying the residuals vs. predicted values, with a horizontal line at zero.