minMSE (version 0.3.1)

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,
                        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.

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)
# }