# nocov start
make_gen_additive_reg <- function() {
parsnip::set_new_model("gen_additive_reg")
parsnip::set_model_mode("gen_additive_reg", "regression")
}
make_gen_additive_reg_stan <- function() {
#### REGRESION
model = "gen_additive_reg"
mode = "regression"
engine = "stan"
parsnip::set_model_engine(model = model, mode = mode, eng = engine)
parsnip::set_dependency(model = model, eng = engine, pkg = "brms")
parsnip::set_dependency(model = model, eng = engine, pkg = "bayesmodels")
parsnip::set_model_arg(
model = model,
eng = "stan",
parsnip = "markov_chains",
original = "chains",
func = list(pkg = "bayesmodels", fun = "markov_chains"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = model,
eng = "stan",
parsnip = "chain_iter",
original = "iter",
func = list(pkg = "bayesmodels", fun = "chain_iter"),
has_submodel = FALSE
)
parsnip::set_model_arg(
model = model,
eng = "stan",
parsnip = "warmup_iter",
original = "warmup",
func = list(pkg = "bayesmodels", fun = "warmup_iter"),
has_submodel = FALSE
)
parsnip::set_encoding(
model = model,
eng = engine,
mode = mode,
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_fit(
model = model,
eng = engine,
mode = mode,
value = list(
interface = "formula",
protect = c("formula", "data"),
func = c(fun = "gen_additive_stan_fit_impl"),
defaults = list()
)
)
parsnip::set_pred(
model = model,
eng = engine,
mode = mode,
type = "numeric",
value = list(
pre = NULL,
post = NULL, #function(x, object) res<-tibble::as_tibble(x) %>% dplyr::pull(1) %>% as.numeric(),
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
new_data = rlang::expr(new_data)
)
)
)
}
# nocov end
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