evaluate_solution
From minMSE v0.3.1
by Sebastian Schneider
Evaluate MSE Equation
The function computes the mean squared error for a given treatment assignment. More precisely: it computes the mean squared error of the treatment effect estimator resulting from the treatment groups as specified by the argument, the treatment assignment vector. The function uses matrix multiplication and the Moore-Penrose generalized inverse.
- Keywords
- treatment, optim, Assignment, MSE
Usage
evaluate_solution(par, data, mse_weights = 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.
Value
Returns the mean squared error value for the current treatment assignment.
References
See Also
Examples
# NOT RUN {
input <- matrix(1:30, nrow = 10, ncol = 3)
evaluate_solution(par = c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0),
input,
mse_weights = c(1, 2))
# }
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