diag_check_point: Empirical check
In DTWilson/BOSSS: Bayesian Optimisation for Simulation-based Sample Size
diag_check_point R Documentation
Empirical check
Description
Empirical check
Usage
diag_check_point(design, problem, solution, N, current = NULL)
Arguments
design
Design to evaluate.
problem
BOSSS problem.
solution
BOSSS solution.
N
Number of simulations to use when computing Monte Carlo estimates.
current
An optional matrix containing the results of previous
check_point calls, to be built upon.
Value
A matrix with each row corresponds to a model of the BOSSS solution
object, giving the Monte Carlo estimates of the mean and variance
along side the number of simulations used to compute them.
DTWilson/BOSSS documentation built on April 14, 2025, 8:35 a.m.
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| diag_check_point | R Documentation |
Empirical check
Description
Empirical check
Usage
diag_check_point(design, problem, solution, N, current = NULL)
Arguments
design |
Design to evaluate. |
problem |
BOSSS problem. |
solution |
BOSSS solution. |
N |
Number of simulations to use when computing Monte Carlo estimates. |
current |
An optional matrix containing the results of previous check_point calls, to be built upon. |
Value
A matrix with each row corresponds to a model of the BOSSS solution object, giving the Monte Carlo estimates of the mean and variance along side the number of simulations used to compute them.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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