calculate_all_mse_neutrosophic {neutroSurvey} | R Documentation |
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
)
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 |
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
Author(s)
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)
[Package neutroSurvey version 0.1.0 Index]