GPEMR (version 0.1.0)

real_data: Real Data Analysis with Model Fitting and Visualization

Description

This function performs parameter estimation for specified models (Logistic, Von-Bertalanffy, or Gompertz) using real data from a TXT or PDF file. It calculates global and local estimates of the model parameters, including their covariance matrices, and optionally generates plots of the estimates and p-values.

Usage

real_data(
  data_path,
  window_size,
  model,
  parameter,
  cov = FALSE,
  Plot_est = FALSE,
  p_value_plot = FALSE,
  tolerance = 0.05
)

Value

A list containing:

est_global

A vector of global parameter estimates.

cov_global

A matrix of global covariance estimates (if cov is TRUE).

est_local

A matrix of local parameter estimates.

cov_local

A list of local covariance matrices (if cov is TRUE).

est_plot

A plot of the estimated parameters (if Plot_est is TRUE).

p_value_plot

A plot of p-values for local estimates (if p_value_plot is TRUE).

Arguments

data_path

A character string specifying the path to the data file. Supported file types are TXT and CSV.

window_size

An integer specifying the size of the moving window for localized estimation. Default is 3.

model

A character string specifying the model to fit. Options are "Logistic", "Von-Bertalanffy", and "Gompertz".

parameter

A list containing initial parameter estimates. For "Logistic" and "Von-Bertalanffy" models, the list should include r and K. For the "Gompertz" model, it should include c and b.

cov

A logical value indicating whether to return the covariance matrices. Default is FALSE.

Plot_est

A logical value indicating whether to generate plots of the estimated parameters. Default is FALSE.

p_value_plot

A logical value indicating whether to generate plots of p-values for local estimates. Default is FALSE.

tolerance

A numeric value specifying the alpha level for p-value calculation. Default is 0.05.

Examples

data_csv <- system.file("extdata", "sample_data.csv", package = "GPEMR")
results_logistic <- real_data(data_csv, window_size = 5, model = "Logistic",
                              parameter = list(r= 0.7), cov = TRUE, Plot_est = TRUE)