Set delta model for ggeffects::ggpredict()

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.

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

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.

Examples

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

# binomial part:
set_delta_model(fit, model = 1)
#> Model fit by ML ['sdmTMB']
#> Formula: density ~ poly(depth_scaled, 2)
#> Mesh: NULL (isotropic covariance)
#> Data: pcod_2011
#> Family: delta_gamma(link1 = 'logit', link2 = 'log')
#> 
#> Delta/hurdle model 1: -----------------------------------
#> Family: binomial(link = 'logit') 
#>                        coef.est coef.se
#> (Intercept)               -0.48    0.09
#> poly(depth_scaled, 2)1   -23.06    3.15
#> poly(depth_scaled, 2)2   -48.79    4.45
#> 
#> 
#> Delta/hurdle model 2: -----------------------------------
#> Family: Gamma(link = 'log') 
#>                        coef.est coef.se
#> (Intercept)                4.24    0.08
#> poly(depth_scaled, 2)1    -5.49    3.50
#> poly(depth_scaled, 2)2   -13.26    3.23
#> 
#> Dispersion parameter: 0.64
#> 
#> ML criterion at convergence: 2936.579
#> 
#> See ?tidy.sdmTMB to extract these values as a data frame.

# gamma part:
set_delta_model(fit, model = 2)
#> Model fit by ML ['sdmTMB']
#> Formula: density ~ poly(depth_scaled, 2)
#> Mesh: NULL (isotropic covariance)
#> Data: pcod_2011
#> Family: delta_gamma(link1 = 'logit', link2 = 'log')
#> 
#> Delta/hurdle model 1: -----------------------------------
#> Family: binomial(link = 'logit') 
#>                        coef.est coef.se
#> (Intercept)               -0.48    0.09
#> poly(depth_scaled, 2)1   -23.06    3.15
#> poly(depth_scaled, 2)2   -48.79    4.45
#> 
#> 
#> Delta/hurdle model 2: -----------------------------------
#> Family: Gamma(link = 'log') 
#>                        coef.est coef.se
#> (Intercept)                4.24    0.08
#> poly(depth_scaled, 2)1    -5.49    3.50
#> poly(depth_scaled, 2)2   -13.26    3.23
#> 
#> Dispersion parameter: 0.64
#> 
#> ML criterion at convergence: 2936.579
#> 
#> See ?tidy.sdmTMB to extract these values as a data frame.

# combined:
set_delta_model(fit, model = NA)
#> Model fit by ML ['sdmTMB']
#> Formula: density ~ poly(depth_scaled, 2)
#> Mesh: NULL (isotropic covariance)
#> Data: pcod_2011
#> Family: delta_gamma(link1 = 'logit', link2 = 'log')
#> 
#> Delta/hurdle model 1: -----------------------------------
#> Family: binomial(link = 'logit') 
#>                        coef.est coef.se
#> (Intercept)               -0.48    0.09
#> poly(depth_scaled, 2)1   -23.06    3.15
#> poly(depth_scaled, 2)2   -48.79    4.45
#> 
#> 
#> Delta/hurdle model 2: -----------------------------------
#> Family: Gamma(link = 'log') 
#>                        coef.est coef.se
#> (Intercept)                4.24    0.08
#> poly(depth_scaled, 2)1    -5.49    3.50
#> poly(depth_scaled, 2)2   -13.26    3.23
#> 
#> Dispersion parameter: 0.64
#> 
#> ML criterion at convergence: 2936.579
#> 
#> See ?tidy.sdmTMB to extract these values as a data frame.