isr.optimal.select: Returns the next items that should be assigned to the...
In yuehmeir2/CATSimulator: Library for Simulation of Adaptive Item Selection
Description
Usage
Arguments
Value
Examples
Description
The 'optimal' algorithm uses information value of each item as the objective,
then uses lpSolveAPI to find a complete test solution that maximizes overall test
information while still meeting blueprint constraints. Then it selects the max
information item from the unassigned items in the solution.
Usage
1
isr.optimal.select(simuleeOut, simulation)
Arguments
simuleeOut
A tibble containing the in-progress simulee test output.
simulation
An object defining the test that is being run.
Value
A list containing the selected moduleIndex, plus other values useful for understanding the selection.
Examples
1
2
3
4
simulation = initSimulation(readRDS(system.file("example/passage-optimal.rds", package = "CATSimulator")))
simulation$control$solver = list(name = "lpsolve", external = F, mipGap = 0.0001, timeout = 1000, verbose = F)
simuleeOut = initSimulee(generateSimuleesByTrueTheta(-2, 10001), simulation)
selection = isr.optimal.select(simuleeOut, simulation)
yuehmeir2/CATSimulator documentation built on June 13, 2021, 7:02 p.m.
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Description Usage Arguments Value Examples
Description
The 'optimal' algorithm uses information value of each item as the objective, then uses lpSolveAPI to find a complete test solution that maximizes overall test information while still meeting blueprint constraints. Then it selects the max information item from the unassigned items in the solution.
Usage
1 | isr.optimal.select(simuleeOut, simulation)
|
Arguments
simuleeOut |
A tibble containing the in-progress simulee test output. |
simulation |
An object defining the test that is being run. |
Value
A list containing the selected moduleIndex, plus other values useful for understanding the selection.
Examples
1 2 3 4 | simulation = initSimulation(readRDS(system.file("example/passage-optimal.rds", package = "CATSimulator")))
simulation$control$solver = list(name = "lpsolve", external = F, mipGap = 0.0001, timeout = 1000, verbose = F)
simuleeOut = initSimulee(generateSimuleesByTrueTheta(-2, 10001), simulation)
selection = isr.optimal.select(simuleeOut, simulation)
|
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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