set_delta_model: Set delta model for 'ggeffects::ggpredict()'

In pbs-assess/sdmTMB: Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

Set delta model for ggeffects::ggpredict()

Description

Set a delta model component to predict from with ggeffects::ggpredict().

Usage

set_delta_model(x, model = c(NA, 1, 2))

Arguments

x	An sdmTMB() model fit with a delta family such as delta_gamma().
model	Which delta/hurdle model component to predict/plot with. NA does the combined prediction, 1 does the binomial part, and 2 does the positive part.

Details

A complete version of the examples below would be:

fit <- sdmTMB(density ~ poly(depth_scaled, 2), data = pcod_2011,
  spatial = "off", family = delta_gamma())

# binomial part:
set_delta_model(fit, model = 1) |>
  ggeffects::ggpredict("depth_scaled [all]")

# gamma part:
set_delta_model(fit, model = 2) |>
  ggeffects::ggpredict("depth_scaled [all]")

# combined:
set_delta_model(fit, model = NA) |>
  ggeffects::ggpredict("depth_scaled [all]")

But cannot be run on CRAN until a version of ggeffects > 1.3.2 is on CRAN. For now, you can install the GitHub version of ggeffects. https://github.com/strengejacke/ggeffects.

Value

The fitted model with a new attribute named delta_model_predict. We suggest you use set_delta_model() in a pipe (as in the examples) so that this attribute does not persist. Otherwise, predict.sdmTMB() will choose this model component by default. You can also remove the attribute yourself after:

attr(fit, "delta_model_predict") <- NULL

Examples

fit <- sdmTMB(density ~ poly(depth_scaled, 2), data = pcod_2011,
  spatial = "off", family = delta_gamma())

# binomial part:
set_delta_model(fit, model = 1)

# gamma part:
set_delta_model(fit, model = 2)

# combined:
set_delta_model(fit, model = NA)

pbs-assess/sdmTMB documentation built on Dec. 20, 2024, 1:51 p.m.