example: Example of using the aggregation package

In bogdanoancea/aggregation: R package for aggregation of device numbers into population number

Description

This is just an example on how to use this package to generate random values from a Poisson multinomial distribution in order to obtain a point estimate of the number of individuals detected by mobile network in a region.

Usage

example()

Details

This is a script that shows how to use the functions of this package to compute a point estimate of the number individuals detected by the network in each region and the number of individuals moving from one region to another. From the set of random values one can obtain any point estimate: mean. mode, median.

References

https://github.com/MobilePhoneESSnetBigData

Examples

# set the folder where the necessary input files are stored and the prefix of
# the input file names.
path      <- 'extdata'

prefix='postLocDevice'

# gets the series of time instants from the simulation.xml file.
simParams <-deduplication::readSimulationParams(system.file(path,
'simulation.xml', package = 'aggregation'))
time_from <- simParams$start_time
time_to <- simParams$end_time
time_incr <- simParams$time_increment
times<-seq(from=time_from, to=time_to-time_incr, by = time_incr)

# set the grid file name, i.e. the file the parameters of the grid
grFile <- system.file(path, 'grid.csv', package = 'aggregation')

# set the duplicity probabilities file name, i.e. the file with duplicity
# probability for each device
dpFile<-system.file(path, 'duplicity.csv', package = 'aggregation')

# set the regions file name, i.e. the file defining the regions for which we
# need the estimation of the number of individuals detected by network.
rgFile<-system.file(path, 'regions.csv', package = 'aggregation')

# generate n random values
n <- 1e3
nNet <- rNnetEvent(n, grFile, dpFile, rgFile, system.file(path,
package = 'aggregation'), prefix, times = times)

# print the mean number of detected individuals for each region, for each
# time instant
regions <- as.numeric(unique(nNet$region))
times <- unique(nNet$time)

for(r in regions) {
    print(paste0("region: ", r))
    for(t in times) {
        print(paste0("time instant: ", t, " number of individuals: " ,
        round(mean(nNet[region == r][time ==t]$N))))
    }
}


# For the origin-destination matrix we proceed in a similar way
prefixJ <- 'postLocJointProbDevice'
nnetOD <- rNnetEventOD(n, dpFile, rgFile, system.file(path,
package = 'aggregation'), prefixJ)

# The origin-destination matrix can be computed now very simple
# First we choose two consecutive time instants
t1 <- 0
t2 <- 10
# The we extract the regions:
regions_from <- sort(as.numeric(unique(nnetOD$region_from)))
regions_to <- sort(as.numeric(unique(nnetOD$region_to)))

# Now we compute the origin-destination matrix:
ODmat <- matrix(nrow = length(regions_from), ncol = length(regions_to))
for(r1 in regions_from) {
   for(r2 in regions_to) {
       ODmat[r1,r2] <-
          round(mean(nnetOD[time_from==t1][time_to==t2][region_from==r1][region_to==r2]$Nnet))
   }
}
ODmat

bogdanoancea/aggregation documentation built on Nov. 29, 2020, 5:09 p.m.