simcounty: Simulate data to mimic 'county_bins' and 'county_true'
In binsmooth: Generate PDFs and CDFs from Binned Data

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/simcounty.R

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).

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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))

`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.

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

Returns a list of two data frames:

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

David J. Hunter and McKalie Drown

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

county_bins, county_true

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])