simcounty: Simulate data to mimic 'county_bins' and 'county_true'

In binsmooth: Generate PDFs and CDFs from Binned Data

Description

Samples from a selection of distributions (Gamma, Lognormal, Weibull, Triangle) to simulate income data in the format used in the American Community Survey data (county_bins and county_true).

Usage

simcounty(numCounties, minPop = 1000, maxPop = 100000,
          bin_minimums = c(0, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000,
                           50000, 60000, 75000, 100000, 125000, 150000, 200000))

Arguments

numCounties	The number of counties to simulate data for
minPop	Minimum population to sample (default = 1000)
maxPop	Maximum population to sample (default = 100000)
bin_minimums	Bin edges. Defaults to the edges used in the Census data.

Details

The county names will tell which distributions were sampled to simulate each county.

Value

Returns a list of two data frames:

county_bins	Simulated binned income data
county_true	Statistics computed from the raw data

Author(s)

David J. Hunter and McKalie Drown

References

Hunter, D., Drown, M., and von Hippel, P. (2016) Optimized smoothing techniques for binned data, in preparation.

See Also

county_bins, county_true

Examples

l <- simcounty(5)
cb <- l$county_bins
ct <- l$county_true
sbl <- splinebins(cb$bin_max[cb$fips==103], cb$households[cb$fips==103],
                  ct$mean_true[ct$fips==103])
stl <- stepbins(cb$bin_max[cb$fips==105], cb$households[cb$fips==105],
                ct$mean_true[ct$fips==105])
plot(sbl$splinePDF, 0, 300000, n=500)
plot(stl$stepPDF, do.points=FALSE, main=cb$county[cb$fips==105][1])

binsmooth documentation built on May 30, 2017, 4:23 a.m.