fit_k: Fit a k-aggregated model
In HVoltBb/konez: K-aggregated transformation for modeling excess ones

Description Usage Arguments Examples

View source: R/fun.r

Fit a k-aggregated model with a fixed k.

fit_k(model = c("poisson", "negbinom", "cmp", "Tpoisson", "Tnegbinom",
  "Tcmp"), count, k = 0, type = 0, model_par = list(Xc = NA, Xz = NA,
  Xrc = NA, Xrz = NA, maxiter = 50, size_upper = 100),
  jags_par = list(chain = 3, sample = 500, thin = 10, method =
  "rjparallel", burnin = 1000, inits = inix))

`model`	character string, partially matched to `c('poisson', 'negbinom', 'cmp', 'Tpoisson','Tnegbinom', 'Tcmp')`: see 'Details'.
`k`	numeric (non-negative integer).
`type`	numeric value, specifying the type of regression model: 0: no covariates; 1: with covariates; 2: with covariates and random effects. Defaults to `0`.
`model_par`	list of model parameters. `Xc` is the covariate dataframe for the count model, `Xrc` is a dataframe of the covariate of the random effect for the count model, `Xz` is the covariate dataframe for the zero/one model, `Xrz` is the covariate dataframe of the random effect for the zero/one model, `size_upper` is the upper bound of the prior of the size parameter of a negative binomial distribution (defaults to 100) and `maxiter` is a positive integer, specifying the number of positive term to keep in the calculation of the Conway-Maxwell-Poisson distribution (defaults to 50).
`jags_par`	list of variables to pass to run.jags function.
`data`	numeric vector of counts.

1	fit = fit_k('n', poss$X.Lb, k=1, model_par = list(Xc = log(1+poss['X.Stags']), Xz = log(1+poss['X.Stags'])) # Fit 1-aggregated negative model to the Leadbeater's possum abundance data