get.subsamp: Find the subset of districts which have a minumim number of...

In gilesjohnr/hmob: Tools for analyzing human mobility data

Description

This function takes the output from a the mob.data.array function and finds the largest subset of locations (districts) that have a minumum number of observations for all ij routes. The subset is found by sequentially removing the location with the largest number of routes below the defined threshold (min.samp) until all locations contain at least min.samp number of observations for each route.

Usage

get.subsamp(x, min.locations, min.samp)

Arguments

x	a three dimensional array produced by the mob.data.array function
min.locations	minimum number of locations (rows and columns) to keep
min.samp	minimum sample size

Value

a three dimensional array containing the subset of x

Author(s)

John Giles

See Also

Other data synthesis: calc.prop.tot.trips(), calc.route.type(), calc.samp.size(), get.crossdist(), get.distance.class(), get.distance.counts(), get.distance.matrix(), get.district.names.xy(), get.district.pop(), get.duration.counts(), get.holidays(), get.sparse.mob.matrix(), get.stay.data(), get.xy.counts(), mob.data.array(), parse.longform()

Examples

load('./data/duration_data_arrays_3day_full.Rdata') # y.month, y.route, y.pop 

subsamp <- get.subsamp(y.route, min.locations=30, min.samp=10)

district.subset <- dimnames(subsamp)$origin
trip.map(as.numeric(district.subset))

gilesjohnr/hmob documentation built on Aug. 8, 2020, 1:26 a.m.