EC_time: Time Required to Run N Variable Combinations on N Cores

In Lauren-Eyler/ECforshiny: Population-Specific Economic Groups Defined by Few Asset Variables

Description

Running the 'EconomicClusters' algorithm takes significant computing time. 'EC_time' determines the amount of time needed to run 'EconomicClusters' for one combination of variables on each available core in parallel. These results can be used to estimate the overall duration of the algorithm for all variable combinations.

Usage

EC_time(X, Y, nvars, kmin, kmax, ncores)

Arguments

X	a data frame with Column 1 containing weighted number of household members (coded as numeric) and Columns 2 through n containing all asset variables to be considered for variable selection (coded as factors). A data frame in this form can be produced by function 'EC_DHSwts' for DHS data. Column names should be specified.
Y	optional variable in vector form to be included in all variable combinations. See details below.
nvars	number of asset variables used to define economic clusters. If Y is not missing, the number of asset variables used to define economic clusters will be nvars+1.
kmin	minimum number of clusters to be considered
kmax	maximum number of clusters to be considered
ncores	number of computing cores to be used in parallel

Value

Time needed to run 'EconomicClusters' on n combinations of variables in parallel using n computing cores

Note

Running the full 'EconomicClusters' algorithm on a real household survey data set will take a significant amount of computing time. Don't worry! We have a free and publically available solution for you. Please see the help files for 'EconomicClusters-package' for further details.

Author(s)

Lauren Eyler [e-mail]

See Also

EconomicClusters-package, EconomicClusters, EC_DHSwts, data_for_EC

Lauren-Eyler/ECforshiny documentation built on May 8, 2019, 8:52 p.m.