simulDiagnosis {DisaggregateTS} | R Documentation |
Simulation Diagnostics
Description
This function provides diagnostics for evaluating the accuracy of simulated data. Specifically, it computes the Mean Squared Error (MSE) between the true and estimated response vectors, and optionally, the sign recovery percentage of the coefficient vector.
Usage
simulDiagnosis(data_Hat, data_True, sgn = FALSE)
Arguments
data_Hat |
List containing the estimated high-frequency data, with components |
data_True |
List containing the true high-frequency data, with components |
sgn |
Logical value indicating whether to compute the sign recovery percentage. Default is |
Details
The function takes in the generated high-frequency data (data_True
) and the estimated high-frequency data (data_Hat
), and returns the Mean Squared Error (MSE) between the true and estimated values of the response vector. If the sgn
parameter is set to TRUE
, the function additionally computes the percentage of correctly recovered signs of the coefficient vector.
Value
If sgn
is FALSE
, the function returns the Mean Squared Error (MSE) between the true and estimated response vectors. If sgn
is TRUE
, the function returns a list containing both the MSE and the sign recovery percentage.
Examples
true_data <- list(y_Gen = c(1, 2, 3), Beta_Gen = c(1, -1, 0))
est_data <- list(y_Est = c(1.1, 1.9, 2.8), beta_Est = c(1, 1, 0))
mse <- simulDiagnosis(est_data, true_data)
results <- simulDiagnosis(est_data, true_data, sgn = TRUE)