minMSE (version 0.5.1)

evaluate_solution.optim: 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.

References

Schneider and Schlather (2017),

See Also

ginv, optim

Examples

# NOT RUN {
input <- matrix(1:30, nrow = 10, ncol = 3)

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