neutroSurvey (version 0.1.0)

calculate_all_mse_neutrosophic: Calculate All MSE Neutrosophic

Description

Computes various Mean Squared Error (MSE) estimates for neutrosophic interval data using different adjustment methods.

Usage

calculate_all_mse_neutrosophic(
  theta_L,
  theta_U,
  Y_L,
  Y_U,
  X_L,
  X_U,
  Cx_L,
  Cx_U,
  Cy_L,
  Cy_U,
  rho_L,
  rho_U,
  B_L,
  B_U
)

Value

A list containing five types of MSE estimates:

• MSE - Standard MSE estimates (Lower, Upper)

• MSE1 - Ratio-adjusted MSE estimates

• MSE2 - Kurtosis-adjusted MSE estimates

• MSE_exp - Exponential MSE estimates

• MSE_r - Regression MSE estimates

Arguments

theta_L

Lower theta value (1/n_L - 1/N_L)

theta_U

Upper theta value (1/n_U - 1/N_U)

Y_L

Lower study mean

Y_U

Upper study mean

X_L

Lower auxiliary mean

X_U

Upper auxiliary mean

Cx_L

Lower auxiliary CV

Cx_U

Upper auxiliary CV

Cy_L

Lower study CV

Cy_U

Upper study CV

rho_L

Lower correlation

rho_U

Upper correlation

B_L

Lower kurtosis

B_U

Upper kurtosis

Author

Neha Purwar, Kaustav Aditya, Pankaj Das and Bharti

Examples

# First compute metrics from data
data(japan_neutro)
metrics <- compute_all_metrics(japan_neutro)

# Define population parameters (non-interactive example)
inputs <- list(theta_L = 0.01, theta_U = 0.02)

# Calculate all MSE types
mse_results <- calculate_all_mse_neutrosophic(
  inputs$theta_L, inputs$theta_U,
  metrics$mean_interval_Y[1], metrics$mean_interval_Y[2],
  metrics$mean_interval_X[1], metrics$mean_interval_X[2],
  metrics$cv_interval_X[1], metrics$cv_interval_X[2],
  metrics$cv_interval_Y[1], metrics$cv_interval_Y[2],
  metrics$correlation_results[1], metrics$correlation_results[2],
  metrics$kurtosis_interval_X[1], metrics$kurtosis_interval_X[2]
)

# Print results
print(mse_results)