# 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))
# }
```

*Documentation reproduced from package minMSE, version 0.3.1, License: GNU General Public License*

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