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Maltiel et al Transmission Bias Model

maltiel_tbm.Rd

Bayesian model for ARD with transmission bias parameters.

  • maltiel_tbm_count – transmission bias model for count responses. All tau_k are estimated from the data.

Usage

maltiel_tbm_count(N, K, y, m, L, eta, v, ...)

Arguments

N

integer; population size

K

integer; number of sub-populations

y

numeric matrix; ARD counts (N x K)

m

numeric vector; fractional sub-population sizes (length K)

L

numeric vector; lower bounds on individual degrees (length N). Must satisfy L[i] >= max(y[i,]) for each respondent i so that the degree is always at least as large as the maximum observed count.

eta

numeric vector; Beta prior shape 1 for each transmission bias (length K)

v

numeric vector; Beta prior shape 2 for each transmission bias (length K)

...

other arguments passed to cmdstanr::sample()

Value

Object of class cmdstanr::CmdStanMCMC containing the samples.

Details

Calling this function(s) will load the model object using get_model() and sample from the posterior using the cmdstanr::sample() method.

References

Maltiel, R., Raftery, A. E., McCormick, T. H., & Baraff, A. J. (2015). Estimating Population Size Using the Network Scale Up Method. The Annals of Applied Statistics, 9(3), 1247–1277. doi:10.1214/15-AOAS827

See also

Use get_model() to return Stan model object of class cmdstanr::CmdStanModel without fitting it.

Other models in this package: maltiel_bem, maltiel_cm, maltiel_rdm, test_model(), zheng_bem, zheng_gp

Other models of Maltiel et al (2015): maltiel_bem, maltiel_cm, maltiel_rdm

Examples

data("Fake_maltiel_RD", package = "stansum")
K <- ncol(Fake_maltiel_RD)
r <- maltiel_tbm_count(
  N = nrow(Fake_maltiel_RD),
  K = K,
  y = data.matrix(Fake_maltiel_RD),
  m = rep(0.01, K),
  L = apply(data.matrix(Fake_maltiel_RD), 1, max),
  eta = rep(1, K),
  v = rep(1, K),
  chains = 1,
  iter_warmup = 200,
  iter_sampling = 10
)
#> Running MCMC with 1 chain...
#> 
#> Chain 1 Iteration:   1 / 210 [  0%]  (Warmup) 
#> Chain 1 Iteration: 100 / 210 [ 47%]  (Warmup) 
#> Chain 1 Iteration: 200 / 210 [ 95%]  (Warmup) 
#> Chain 1 Iteration: 201 / 210 [ 95%]  (Sampling) 
#> Chain 1 Iteration: 210 / 210 [100%]  (Sampling) 
#> Chain 1 finished in 9.8 seconds.