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Conditional Effects for BMM Models

Source: R/conditional_effects.R
conditional_effects.bmmfit.Rd

Compute conditional effects for parameters of a bmmfit object. This method provides a more intuitive interface than directly calling brms::conditional_effects() on bmmfit objects, by:

  • Accepting model parameter names directly (e.g., "kappa", "thetat")

  • Automatically determining whether parameters are distributional or non-linear

  • Optionally applying inverse link transformations to show parameters on their natural scale

Usage

# S3 method for class 'bmmfit'
conditional_effects(x, par = NULL, scale = c("native", "sampling"), ...)

Arguments

x

A bmmfit object (created by bmm())

par

Character string. Name of the model parameter to compute effects for. This should be one of the parameter names from the original model specification (see names(x$bmm$model$parameters)). If NULL (the default), conditional effects are computed for all estimated (non-fixed) parameters.

scale

Character. Scale on which to show the parameter:

"native" (default)

Show on natural scale using inverse link transformation. For example, kappa with log link shown on exp scale, thetat with logit (mixture2p) or softmax (mixture3p) link shown on the probability scale.

"sampling"

Show on the sampling scale (as used during MCMC). For example, kappa with log link shown on log scale.

...

Additional arguments passed to brms::conditional_effects(). Common arguments include:

  • effects: Character vector specifying which predictor effects to plot

  • conditions: Named list for setting values of covariates

  • int_conditions: Conditions for interactions

  • prob: Probability mass to include in credible intervals (default 0.95)

  • spaghetti: Logical, whether to add spaghetti lines

  • method: Method for computing effects ("posterior_predict" or "posterior_epred")

Value

A brms_conditional_effects object (from brms), which can be:

  • Plotted directly using plot()

  • Converted to a data frame for custom plotting

  • Combined with other conditional effects plots

Details

Parameter Types

bmm models use two types of parameters internally:

  • Non-linear parameters (nlpar): Core model parameters like kappa, c, a, thetat

  • Distributional parameters (dpar): Derived parameters used in brms mixture distributions

Users should not need to know this distinction - conditional_effects.bmmfit() automatically routes to the correct parameter type.

Scale Transformations

By default (scale = "native"), parameters are shown on their natural scale by applying inverse link transformations:

  • log link → exp transformation

  • logit link → inverse logit (probability scale)

  • tan_half link → 2*atan transformation (radians)

  • identity link → no transformation

Use scale = "sampling" to see parameters on the scale used during MCMC sampling.

See also

brms::conditional_effects() for the underlying brms function

Examples

if (FALSE) { # \dontrun{
# Fit a mixture model with set size effect on kappa
fit <- bmm(
  formula = bmf(kappa ~ 0 + setsize, thetat ~ 1),
  data = zhang_luck_2008,
  model = mixture3p(
    resp_error = "response_error",
    nt_features = paste0("col_lure", 1:5),
    set_size = "setsize"
  )
)

# Get conditional effects for kappa on natural scale (exp of log)
ce_kappa <- conditional_effects(fit, par = "kappa")
plot(ce_kappa)

# Get conditional effects for kappa on log scale (sampling scale)
ce_kappa_log <- conditional_effects(fit, par = "kappa", scale = "sampling")
plot(ce_kappa_log)

# Get effects for thetat (memory probability)
ce_thetat <- conditional_effects(fit, par = "thetat")
plot(ce_thetat)

# Specify which effects to plot
ce_specific <- conditional_effects(fit, par = "kappa", effects = "setsize")

# Combine with other brms options
ce_detailed <- conditional_effects(
  fit,
  par = "kappa",
  effects = "setsize",
  spaghetti = TRUE,
  ndraws = 100
)
} # }