debinfer_par function

debinfer_par

Creates an object containing all the necessary bits for a parameter i.e. initial values, prior distributions, hyper-parameters, tuning parameters, etc. to set up a debinfer analysis

debinfer_par(
  name,
  var.type,
  fixed,
  value,
  joint = NULL,
  prior = NULL,
  hypers = NULL,
  prop.var = NULL,
  samp.type = NULL
)

Arguments

• name: character vector; name of the variable
• var.type: character vector; type of the variable "de" = parameter for the differential equation, "obs" = parameter of the observation model, "init" = initial condition for a state variable in the differential equation
• fixed: boolean; TRUE = parameter is taken to be fixed, FALSE = parameter is to be estimated by MCMC
• value: numeric; parameter value. For fixed parameters this is the value used in the analysis for free parameters this is the starting value used when setting up the MCMC chain
• joint: integer; number of block for joint proposal; NULL means the parameter is not to be jointly proposed
• prior: character; name of the probability distribution for the prior on the parameter. Must conform to standard R naming of d/r function pairs, e.g. beta ( foo = beta), binomial binom, Cauchy cauchy, chi-squared chisq, exponential exp, Fisher F f, gamma gamma, geometric geom, hypergeometric hyper, logistic logis, lognormal lnorm, negative binomial nbinom, normal norm, Poisson pois, Student t t, uniform unif, Weibull weibull. Priors from the truncdist package are available by default. User priors can be provided but must be available in the environment from which de_mcmc is called.
• hypers: list of numeric vectors, hyperparameters for the prior; mean only for mvnorm. Can include trunc for truncated pdfs from package truncdist.
• prop.var: numeric; tuning parameters. For Normal proposals (samp.type="rw" or samp.type="rw-ref"), this must be a positive number representing the standard deviation of the proposal distribution for each parameter. For the asymmetric uniform proposal distribution (samp.type="rw-unif") two positive numeric values are required and the proposal will then have the bounds prop.var[1]/prop.var[2]*current_proposal and prop.var[2]/prop.var[1]*current_proposal. See Boersch-Supan et al. (2016).
• samp.type: character; type of sampler: "rw" = Normal random walk, "ind" = independence, "rw-unif" = asymmetric uniform distribution, "rw-ref" = reflecting random walk sampler on the bounds of the prior support (cf. Hoff 2009, Chapter 10.5.1; Yang and Rodriguez 2013)

Returns

returns an object of class debinfer_par to be fed to the mcmc setup function

References

Boersch-Supan et al. 2016, MEE 8:511-518 tools:::Rd_expr_doi("10.1111/2041-210X.12679")

Hoff 2009, A First Course in Bayesian Statistical Methods, Springer

Yang and Rodriguez 2013, PNAS 110:19307-19312 tools:::Rd_expr_doi("10.1073/pnas.1311790110")