native_transform: Transform parameter draws from the sampling scale to the...

In bmm: Easy and Accessible Bayesian Measurement Models Using 'brms'

Transform parameter draws from the sampling scale to the native scale

Description

Applies a bmmodel's inverse link functions to posterior draws of its parameters. This is the extension point used by native_parameters(): the transformation is a property of the model, not of an individual parameter, because some models map several parameters jointly (e.g. mixture weights through a softmax).

A model whose parameters transform elementwise, or through one softmax group that is active on every row of the data, needs no method — declaring links in its ⁠.model_*()⁠ constructor is sufficient, and that covers every model bmm currently ships. Write a method when either condition fails. Two cases that are known to fail it: a model with several independent multinomial branches, because a "softmax" link carries no group identity and all such parameters are softmaxed together as one group; and a transformation that depends on the design rather than only on the parameter's own value.

Usage

native_transform(model, linpred, data, ...)

## Default S3 method:
native_transform(model, linpred, data, ...)

## S3 method for class 'non_targets'
native_transform(model, linpred, data, ...)

Arguments

model	A bmmodel object.
linpred	A named list of matrices of linear predictor draws, one per model parameter, each with draws in rows and prediction grid cells in columns. These are brms::posterior_linpred() output, so they are on the link scale for distributional parameters and are the raw value for non-linear parameters. For bmm's nlpar-based models the link is a fiction maintained by the model's own nlf() expression — imm declares c = "log" because configure_model.imm_abc() writes exp(c) into the formula, not because brms applies a link — so a model that declares a link it does not actually apply in its nlf() will get silently wrong output here.
data	The prediction grid the draws were computed on, with one row per column of the linpred matrices. These are rows of the fitted model's own data, so they carry the bmm-internal columns a design-dependent transformation keys on (⁠LureIdx*⁠, inv_ss, ⁠Idx_*⁠, matrix columns). native_transform.non_targets() is the worked example.
...	Currently unused.

Details

Methods for specific models follow the bmm S3 chain, which runs from the general classes to the specific ones. A method should therefore transform the parameters it owns, remove them from linpred, and call NextMethod() so the remaining parameters reach the default method:

native_transform.mymodel <- function(model, linpred, data, ...) {
  own <- .my_joint_transform(linpred[c("p1", "p2")])
  linpred <- linpred[not_in(names(linpred), c("p1", "p2"))]
  c(NextMethod(), own)
}

Removing them before NextMethod() is what keeps a method idempotent with respect to the parameters it does not own: a parameter left in linpred is transformed a second time by the next method in the chain, which does not error and does not warn.

Value

A named list of matrices on the native scale. A method must return one element for every name it was given and preserve each matrix's dimensions; native_parameters() checks both and errors, naming the model, if either is broken. The order of the list is free.

See Also

native_parameters()

bmm documentation built on Oct. 16, 2026, 9:08 a.m.