| evaluate_solution.optim {minMSE} | R Documentation |
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 sschneider@coll.mpg.de; sebastian@sebastianschneider.eu, Giulia Baldini giulia.baldini@uni-bonn.de
References
Schneider and Schlather (2017),
See Also
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)