Create, Run, and Forecast Multi-model Inference TSGLM Model Runs

Using the tscount (Liboschik et al. 2017) package to forecast time series of counts.
meta_tsglm: Combines the model running with the covariate preparation functions for a multi-model tsglm (from the tscount (Liboschik et al. 2017) package) model.
forecast.tsglm: A wrapper around the predict function for tsglm objects that produces a "forecast"-class object.

Usage

meta_tsglm(
  ts,
  model,
  distr,
  link,
  lag,
  submodels,
  covariates,
  metadata,
  quiet = FALSE
)

# S3 method for tsglm
forecast(object, h, level, ...)

Arguments

ts: Non-negative integer-conformable vector of rodent abundances to use in forecasting. See prepare_abundance.
model: A named list of model linear predictors. See tsglm.
distr: character of the response distribution. See tsglm.
link: character of the link function. See tsglm.
lag: integer-conformable value of the number of timesteps used in a bulk lagging for all covariates in all submodels.
submodels: list of character vectors defining the covariates to include in each of the submodels.
covariates: data.frame of covariates used in modeling. See prepare_covariates.
metadata: list of model control elements. See prepare_metadata.
quiet: logical indicator controlling if messages are printed.
object: A tsglm-class object.
h: integer-conformable number of steps forward to forecast. Passed into predict as n.ahead.
level: numeric of the confidence level to use in summarizing the predictions.
...: Additional parameters passed into predict.

Value

meta_tsglm: An object of class "tsglm" with additional elements defining the submodel and lag.

forecast.tsglm: list with "forecast"-class with named elements including "mean", "lower", "upper", and "newxreg" (if provided for prediction) as well as the other elements returned by predict.

References

Liboschik T., K. Fokianos, and R. Fried. 2017. tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models. Journal of Statistical Software, 82:1-51. URL.

Examples

if (FALSE) {
   main1 <- file.path(tempdir(), "metatsglm")

   setup_dir(main = main1)
   dataset <- "all"
   species <- "DM"
   model   <- "pevGARCH"
 
   abundance      <- prepare_abundance(main    = main1,
                                       dataset = dataset,
                                       species = species,
                                       model   = model)
   model_controls <- models_controls(main       = main1,
                                     models     = model)[[model]]
   metadata       <- read_metadata(main        = main1)
   newmoons       <- read_newmoons(main        = main1)                                        
   covariates     <- read_covariates(main      = main1)
   model          <- list(past_obs = 1, past_mean = 13)
   distr          <- "poisson"
   link           <- "log"
   lag            <- 6
   submodels      <- list(c("mintemp", "ndvi"),
                          c("maxtemp"),
                          c("meantemp"),
                          c("precipitation"),
                          c(NULL))

   fit_tsglm      <- meta_tsglm(ts         = abundance, 
                                model      = model, 
                                distr      = distr, 
                                link       = link, 
                                lag        = lag, 
                                submodels  = submodels, 
                                covariates = covariates, 
                                metadata   = metadata, 
                                quiet      = FALSE)
   newmoons_in <- match(metadata$time$forecast_newmoonnumbers - lag, covariates$newmoonnumber)
   newxreg     <- covariates[newmoons_in, unlist(fit_tsglm$submodel)]

   forecast(object  = fit_tsglm,   
            h       = metadata$time$lead_time_newmoons,   
            level   = metadata$confidence_level,   
            newxreg = newxreg)

   unlink(main1, recursive = TRUE)
}