Performs spatio-temporal prediction in R using an ssnbayes object from a fitted model.

It will take an observed and a prediction data frame. It requires the same number of observation/locations per day. It requires location id (locID) and points id (pid). The locID are unique for each site. The pid is unique for each observation. Missing values are allowed in the response but not in the covariates.

# S3 method for ssnbayes
predict(
  object = object,
  ...,
  path = path,
  obs_data = obs_data,
  pred_data = pred_data,
  net = net,
  nsamples = nsamples,
  addfunccol = addfunccol,
  locID_pred = locID_pred,
  chunk_size = chunk_size,
  seed = seed
)

Arguments

object: A stanfit object returned from ssnbayes
...: Other parameters
path: Path with the name of the SSN object
obs_data: The observed data frame
pred_data: The predicted data frame
net: (optional) Network from the SSN object
nsamples: The number of samples to draw from the posterior distributions. (nsamples <= iter)
addfunccol: The variable used for spatial weights
locID_pred: (optional) the location id for the predictions. Used when the number of pred locations is large.
chunk_size: (optional) the number of locID to make prediction from
seed: (optional) A seed for reproducibility

Value

A data frame

Author

Edgar Santos-Fernandez

Examples

# \donttest{
#require('SSNdata')
#clear_preds <- readRDS(system.file("extdata/clear_preds.RDS", package = "SSNdata"))
#clear_preds$y <- NA
#pred <- predict(object = fit_ar,
#                 path = path,
#                 obs_data = clear,
#                 pred_data = clear_preds,
#                 net = 2,
#                 nsamples = 100, # numb of samples from the posterior
#                 addfunccol = 'afvArea', # var for spatial weights
#                 locID_pred = locID_pred,
#                 chunk_size = 60)
# }