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,
  newShp_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.
newShp_X: New predictor data for the family parameter in models where the family parameter is modelled in response to data, by default NULL. In cases where the model was originally fit with the same X and shp_X data and only newX is supplied to update.jsdmStanFit the shp_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
} # }