debinfer_par | R Documentation |
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 )
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 an object of class debinfer_par to be fed to the mcmc setup function
Boersch-Supan et al. 2016, MEE 8:511-518 doi: 10.1111/2041-210X.12679
Hoff 2009, A First Course in Bayesian Statistical Methods, Springer
Yang and Rodriguez 2013, PNAS 110:19307-19312 doi: 10.1073/pnas.1311790110
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