Update a jsdmStanFit model object with new data or Stan arguments

This function allows you to update a jsdmStanFit model with new data or new arguments to Stan. It does not support changes that require recompiling stancode - for that you should use stan_jsdm(). Changes to the number of sites, species or covariates do not require recompiling stancode and can therefore be done using this function.

Usage

# S3 method for class 'jsdmStanFit'
update(
  object,
  newY = NULL,
  newX = NULL,
  newD = NULL,
  newNtrials = NULL,
  newZi_X = NULL,
  save_data = TRUE,
  ...
)

Arguments

object: The jsdmStanFit model object
newY: New Y data, by default NULL
newX: New X data, by default NULL
newD: New number of latent variables, by default NULL
newNtrials: New number of trials (binomial model only), by default NULL
newZi_X: New predictor data for the zi parameter in zero-inflated models, by default NULL. In cases where the model was originally fit with the same X and zi_X data and only newX is supplied to update.jsdmStanFit the zi_X data will also be set to newX.
save_data: Whether to save the data in the jsdmStanFit object, by default TRUE
...: Arguments passed to rstan::sampling()

Value

An object of class jsdmStanFit

Examples

if (FALSE) { # \dontrun{
# MGLMM - specified by using the mglmm aliases and with direct reference to Y and
# X matrices:
mglmm_data <- mglmm_sim_data(
  N = 100, S = 10, K = 3,
  family = "gaussian"
)
mglmm_fit <- stan_mglmm(
  Y = mglmm_data$Y, X = mglmm_data$X,
  family = "gaussian"
)
mglmm_fit2 <- update(mglmm_fit, iter = 4000)

# You can also run a model by supplying the data as a list:
gllvm_data <- jsdm_sim_data(
  method = "gllvm", N = 100, S = 6, D = 2,
  family = "bernoulli"
)
gllvm_fit <- stan_jsdm(
  dat_list = gllvm_data, method = "gllvm",
  family = "bernoulli"
)
gllvm_fit
gllvm_data <- jsdm_sim_data(
  method = "gllvm", N = 500, S = 4, D = 2,
  family = "bernoulli"
)
gllvm_fit2 <- update(gllvm_fit, newY = gllvm_data$Y)
gllvm_fit2
} # }