Predicts values given new covariates using a tinyVAST model
# S3 method for 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,
...
)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).
Output from tinyVAST().
New data-frame of independent variables used to predict the response.
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 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.
Calculate standard errors?
Not used.