# These functions are tested indirectly when the models are used. Since this
# function is executed on package startup, you can't execute them to test since
# they are already in the parsnip model database. We'll exclude them from
# coverage stats for this reason.
# nocov start
make_stan_poisson_reg <- function() {
parsnip::set_model_engine("poisson_reg", "regression", "stan_glmer")
parsnip::set_dependency(
"poisson_reg",
eng = "stan_glmer",
pkg = "rstanarm",
mode = "regression"
)
parsnip::set_dependency(
"poisson_reg",
eng = "stan_glmer",
pkg = "multilevelmod",
mode = "regression"
)
parsnip::set_encoding(
model = "poisson_reg",
eng = "stan_glmer",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_fit(
model = "poisson_reg",
eng = "stan_glmer",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "rstanarm", fun = "stan_glmer"),
defaults = list(family = rlang::expr(stats::poisson), refresh = 0)
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "stan_glmer",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = function(results, object) {
tibble::tibble(.pred = apply(results, 2, mean, na.rm = TRUE))
},
func = c(pkg = "rstanarm", fun = "posterior_predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
seed = rlang::expr(sample.int(10^5, 1))
)
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "stan_glmer",
mode = "regression",
type = "pred_int",
value = list(
pre = NULL,
post = function(results, object) {
res <-
tibble::tibble(
.pred_lower = parsnip::convert_stan_interval(
results,
level = object$spec$method$pred$pred_int$extras$level
),
.pred_upper = parsnip::convert_stan_interval(
results,
level = object$spec$method$pred$pred_int$extras$level,
lower = FALSE
),
)
if (object$spec$method$pred$pred_int$extras$std_error)
res$.std_error <- apply(results, 2, sd, na.rm = TRUE)
res
},
func = c(pkg = "rstanarm", fun = "posterior_predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
seed = rlang::expr(sample.int(10^5, 1))
)
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "stan_glmer",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "posterior_predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data)
)
)
)
}
# ------------------------------------------------------------------------------
make_lme4_poisson_reg <- function() {
parsnip::set_model_engine("poisson_reg", "regression", "glmer")
parsnip::set_dependency(
"poisson_reg",
eng = "glmer",
pkg = "lme4",
mode = "regression"
)
parsnip::set_dependency(
"poisson_reg",
eng = "glmer",
pkg = "multilevelmod",
mode = "regression"
)
parsnip::set_encoding(
model = "poisson_reg",
eng = "glmer",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_fit(
model = "poisson_reg",
eng = "glmer",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data", "weights"),
func = c(pkg = "lme4", fun = "glmer"),
defaults = list(family = rlang::expr(stats::poisson))
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "glmer",
mode = "regression",
type = "numeric",
value = list(
pre = reformat_lme_pred_data,
post = NULL,
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
allow.new.levels = TRUE,
re.form = NA,
na.action = rlang::expr(na.exclude),
type = "response"
)
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "glmer",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data)
)
)
)
}
# ------------------------------------------------------------------------------
make_gee_poisson_reg <- function() {
parsnip::set_model_engine("poisson_reg", "regression", "gee")
parsnip::set_dependency(
"poisson_reg",
eng = "gee",
pkg = "gee",
mode = "regression"
)
parsnip::set_dependency(
"poisson_reg",
eng = "gee",
pkg = "multilevelmod",
mode = "regression"
)
parsnip::set_encoding(
model = "poisson_reg",
eng = "gee",
mode = "regression",
options = list(
predictor_indicators = "none",
compute_intercept = FALSE,
remove_intercept = FALSE,
allow_sparse_x = FALSE
)
)
parsnip::set_fit(
model = "poisson_reg",
eng = "gee",
mode = "regression",
value = list(
interface = "formula",
protect = c("formula", "data"),
func = c(pkg = "multilevelmod", fun = "gee_fit"),
defaults = list(family = rlang::expr(stats::poisson))
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "gee",
mode = "regression",
type = "numeric",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data),
type = "response"
)
)
)
parsnip::set_pred(
model = "poisson_reg",
eng = "gee",
mode = "regression",
type = "raw",
value = list(
pre = NULL,
post = NULL,
func = c(fun = "predict"),
args = list(
object = rlang::expr(object$fit),
newdata = rlang::expr(new_data)
)
)
)
}
# nocov end
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