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, that is the standard deviation of the proposal distribution for each parameter |
| 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
Hoff 2009, A First Course in Bayesian Statistical Methods, Springer Yang and Rodriguez 2013, PNAS 110:19307-19312 http://doi.org/10.1073/pnas.1311790110