View source: R/optimal_search.R
| initOptimalExperimentalDesignObject | R Documentation |
This method creates an object of type optimal_experimental_design and will immediately initiate
a search through allocation space. Since this search takes exponential time, for most machines,
this method is futile beyond 28 samples. You've been warned! For debugging, you can use set
num_cores = 1 to be assured of deterministic output.
initOptimalExperimentalDesignObject(
X = NULL,
objective = "mahal_dist",
Kgram = NULL,
wait = FALSE,
start = TRUE,
num_cores = 1,
verbose = TRUE,
use_safe_inverse = FALSE
)
X |
The design matrix with $n$ rows (one for each subject) and $p$ columns (one for each measurement on the subject). This is the design matrix you wish to search for a more optimal design. |
objective |
The objective function to use when searching design space. This is a string
with valid values " |
Kgram |
If the |
wait |
Should the |
start |
Should we start searching immediately (default is |
num_cores |
The number of CPU cores you wish to use during the search. The default is |
verbose |
Should the algorithm emit progress output? Default is |
use_safe_inverse |
Should a regularized inverse be used for the Mahalanobis objective?
Default is |
An object of type optimal_experimental_design_search which can be further operated upon
Adam Kapelner
## Not run:
set.seed(1)
X = matrix(rnorm(12), nrow = 6)
obj = initOptimalExperimentalDesignObject(
X,
objective = "abs_sum_diff",
num_cores = 1,
start = TRUE,
wait = TRUE,
verbose = FALSE
)
obj
## End(Not run)
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