dg: Density function of a candidate distribution in the...
In MobilePhoneESSnetBigData/pestim: Population Estimations Using Mobile Phone Data

Description Usage Arguments Details Value See Also Examples

Generate values of a candidate distribution density function in the accept-reject method of generation of random variables applied to the distribution of the lambda parameter

1 2	dg(lambda, nMNO, nReg, fu, fv, flambda, relTol = 1e-06, nSim = 10000, nStrata = c(1, 100), verbose = FALSE)

`lambda`	numeric vector with the lambda parameter values
`nMNO`	non-negative integer vectors with the number of individuals detected in each cell according to the network operator
`nReg`	non-negative integer vectors with the number of individuals detected in each cell according to the population register
`fu`	named list with the prior marginal distribution for the parameter u
`fv`	named list with the prior marginal distribution for the parameter v
`flambda`	named list with the prior distribution of the parameter λ
`relTol`	relative tolerance in the computation of the `kummer` function. Default value is `1e-6`
`nSim`	number of two-dimensional points to generate to compute the integral. Default value is `1e4`
`nStrata`	integer vector of length 2 with the number of strata in each dimension. Default value is `c(1, 1e2)`
`verbose`	logical (default `FALSE`) to report progress of the computation

The candidate distribution is a gamma distribution with parameters shape = nMNO + 1 and scale = λ^{*} / nMNO, where λ^{*} stands for the mode of the posterior distribution of the lambda parameter.

It is important to know that currently this function accepts only parameters for a single cell at a time. In case of interest for the candidate density function values for a set of cells, the user should program his/her own routine to apply this function to every cell.

dg generates length(lambda) values of the density probability function of the candidate distribution in the accept-reject method.

modeLambda, dlambda for related functions.

curve(dg(x, nMNO = 20, nReg = 115, fu = list('unif', xMin = 0.3, xMax = 0.5),
        fv = list('unif', xMin = 100, xMax = 120),
        flambda = list('gamma', shape = 11, scale = 12)), xlim = c(0, 150),
        main = '', ylab = 'density', xlab = 'lambda')