Predict using vector autoregressive spatio-temporal model

Predicts values given new covariates using a tinyVAST model

Usage

# S3 method for class 'tinyVAST'
predict(
  object,
  newdata,
  remove_origdata = FALSE,
  what = c("mu_g", "p_g", "palpha_g", "pgamma_g", "pepsilon_g", "pomega_g", "pdelta_g",
    "pxi_g", "p2_g", "palpha2_g", "pgamma2_g", "pepsilon2_g", "pomega2_g", "pdelta2_g",
    "pxi2_g"),
  se.fit = FALSE,
  bias.correct = FALSE,
  ...
)

Arguments

object: Output from tinyVAST().
newdata: New data-frame of independent variables used to predict the response.
remove_origdata: Whether to eliminate the original data from the TMB object, thereby speeding up the TMB object construction. However, this also eliminates information about random-effect variance, and is not appropriate when requesting predictive standard errors or epsilon bias-correction.
what: What REPORTed object to output, where mu_g is the inverse-linked transformed predictor including both linear components, p_g is the first linear predictor, palpha_g is the first predictor from fixed covariates in formula, pgamma_g is the first predictor from random covariates in formula (e.g., splines), pomega_g is the first predictor from spatial variation, pepsilon_g is the first predictor from spatio-temporal variation, pxi_g is the first predictor from spatially varying coefficients, p2_g is the second linear predictor, palpha2_g is the second predictor from fixed covariates in formula, pgamma2_g is the second predictor from random covariates in formula (e.g., splines), pomega2_g is the second predictor from spatial variation, pepsilon2_g is the second predictor from spatio-temporal variation, and pxi2_g is the second predictor from spatially varying coefficients.
se.fit: Calculate standard errors?
bias.correct: whether to epsilon bias-correct the predicted value
...: Not used.

Value

Either a vector with the prediction for each row of newdata, or a named list with the prediction and standard error (when se.fit = TRUE).