evaluate_solution.optim: Evaluate MSE Equation (using optim)

In minMSE: Implementation of the minMSE Treatment Assignment Method for One or Multiple Treatment Groups

Evaluate MSE Equation (using optim)

Description

This function calls evaluate_solution, but since optim requires fn and gr to have the same parameters, it has two additional ones.

Usage

evaluate_solution.optim(par,
                        data,
                        evaluation_function = evaluate_solution,
                        swap_treatment_function = NULL,
                        mse_weights = NULL,
                        change = NULL,
                        prev_index_list = NULL)

Arguments

par	a treatment assignment. The treatment and the data must have the same number of observations (rows).
data	a matrix containing the covariate vectors for each attribute.
evaluation_function	the function used to evaluate the MSE treatment. Default is evaluate_solution, which does not take into account outcome or treatment weights. Other options are evaluate_solution_vector and evaluate_solution_matrix.
swap_treatment_function	the parameter is only needed for optim, it does not play any role.
mse_weights	a vector containing the mse_weights for each treatment, or a matrix containing the mse_weights for treatments and outcomes and scaling factors.
change	the parameter is only needed for optim, it does not play any role.
prev_index_list	the parameter is only needed for optim, it does not play any role.

Value

Returns the mean square error value for the current treatment assignment.

Author(s)

Sebastian Schneider [e-mail]; [e-mail], Giulia Baldini [e-mail]

References

Schneider and Schlather (2017),

See Also

ginv, optim

Examples

input <- matrix(1:30, nrow = 10, ncol = 3)

evaluate_solution.optim(par = c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0),
                        input)

minMSE documentation built on March 18, 2022, 8:05 p.m.