mcdeloof: Average rank with Deloof's approximation (parallel computing)
In MARMoT: Matching on Poset-Based Average Rank for Multiple Treatments (MARMoT)
mcdeloof R Documentation
Average rank with Deloof's approximation (parallel computing)
Description
Compute the average with using Deloof's approximation, using parallel
computing; it can be used only on Linux and Mac systems!
Usage
mcdeloof(comparable_data, n.cores)
Arguments
comparable_data
A dataframe or equivalent, which columns are ordered factors or numeric
variables.
n.cores
Number of cores to be used.
Value
A vector containing the average rank of all the observations (it is
recommended to normalized it before use).
References
Caperna, G., 2019. Approximation of AverageRank by means of a formula.
https://doi.org/10.5281/zenodo.2565699
Examples
AR = mcdeloof(deloof_data, n.cores = 1)
MARMoT documentation built on May 29, 2024, 9:56 a.m.
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| mcdeloof | R Documentation |
Average rank with Deloof's approximation (parallel computing)
Description
Compute the average with using Deloof's approximation, using parallel computing; it can be used only on Linux and Mac systems!
Usage
mcdeloof(comparable_data, n.cores)
Arguments
comparable_data |
A dataframe or equivalent, which columns are ordered factors or numeric variables. |
n.cores |
Number of cores to be used. |
Value
A vector containing the average rank of all the observations (it is recommended to normalized it before use).
References
Caperna, G., 2019. Approximation of AverageRank by means of a formula. https://doi.org/10.5281/zenodo.2565699
Examples
AR = mcdeloof(deloof_data, n.cores = 1)
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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