native_parameters: Posterior draws of model parameters on the native scale

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

Posterior draws of model parameters on the native scale

Description

bmm samples all parameters on their link scale (log for precision and boundary parameters, logit or softmax for mixture weights, tan_half for circular locations). native_parameters() returns posterior draws of those parameters on the scale they are interpreted and reported on, evaluated over a grid of predictor values.

Because draws are returned rather than summaries, any contrast is ordinary arithmetic on the draws, with the baseline made explicit by the grid.

Usage

native_parameters(
  x,
  newdata = NULL,
  pars = NULL,
  re_formula = NULL,
  scale = c("native", "sampling"),
  ndraws = NULL,
  draw_ids = NULL,
  summary = FALSE,
  prob = 0.95,
  robust = FALSE,
  ...
)

Arguments

x	A bmmfit object returned by bmm().
newdata	Data to evaluate the parameters on. If NULL (the default), the unique observed combinations of the parameters' predictors are used, taken as rows of the model's own data. Columns you do not supply are filled from the first row of the model data.
pars	Character vector of parameters to return. If NULL (the default), all parameters of the model are returned, including those fixed to a constant. This filters the output only; parameters transformed jointly are always computed together.
re_formula	Which group-level effects to include, as in brms::posterior_linpred(). See Details.
scale	Either "native" (the default) to apply the model's inverse link functions, or "sampling" to return the untransformed linear predictor.
ndraws	Number of posterior draws to use. If NULL (the default), all draws are used. The same draws are used for every parameter.
draw_ids	Indices of the draws to use. Overrides ndraws and makes the result reproducible.
summary	Logical. If FALSE (the default), return draws. If TRUE, return posterior summaries computed after the transformation.
prob	Probability mass of the credible interval when summary = TRUE.
robust	Logical. If TRUE, summary = TRUE reports the median and median absolute deviation instead of the mean and standard deviation.
...	Further arguments passed to brms::posterior_linpred(), such as allow_new_levels and sample_new_levels.

Value

If summary = FALSE, a data.frame with one row per draw, grid cell and parameter, with columns .chain, .iteration, .draw, the grid variables, parameter and value. The draw indices refer to the draws of x, so they can be joined with the output of posterior::as_draws_df(x). If summary = TRUE, a data.frame with one row per grid cell and parameter, with columns Estimate, Est.Error and the interval bounds.

Transform first, then summarise

Quantiles are preserved by monotone inverse links but means are not, so the median and the credible interval of a native-scale parameter are exact while its mean is not the inverse link of the linear predictor's mean. This function transforms the draws and only then summarises, which is why summary = TRUE is not the same as transforming the output of summary.bmmfit().

The same order is what makes summary = TRUE correct for the mixture weights, whose softmax is not an elementwise map at all: summarising the transformed draws reports quantiles of the weight's own marginal posterior, whereas transforming the summaries would report the softmax of three separate quantiles, which is not a quantile of anything.

Transforming coefficients is not the same thing

The inverse link applies to the linear predictor of a grid cell, not to an individual regression coefficient. For a model with kappa ~ condition, exp(b_kappa_conditionB) is a multiplicative factor, not kappa in condition B; kappa in condition B is exp(b_kappa_Intercept + b_kappa_conditionB). Building the grid is exactly what removes this step, which is why contrasts are taken between rows of the output rather than read off the coefficients.

Group-level effects

re_formula = NULL (the default) returns subject-specific parameters and adds the grouping variables to the grid. re_formula = NA sets all group-level effects to zero, which under a non-identity link gives the median subject, not the population mean. The population mean requires marginalising over the distribution of group-level effects, which is done by predicting for a new level:

native_parameters(fit, newdata = transform(nd, id = "new"),
                  allow_new_levels = TRUE, sample_new_levels = "gaussian")

These three quantities differ substantially at between-subject standard deviations typical for working memory data.

What is returned

Parameters fixed to a constant are returned at that constant, transformed to the native scale. Because bmm fixes parameters on the link scale, the native value can differ from the value shown by parameter_info(): the ddm relative starting point zr is fixed at 0 under a logit link and is therefore reported as 0.5, and the ezdm and cswald diffusion constant s is fixed at 0 under a log link and is reported as 1.

Trial-level derived quantities, such as the response probabilities of the mixture models or the category probabilities of m3, are not returned; use brms::posterior_epred() for those.

The default grid contains the observed combinations of the predictors, not their full crossing, so cells that were never presented do not appear. A continuous predictor therefore produces one grid cell per observed value; supply newdata for such models.

Circular location parameters

The circular models sample their location parameter (mu for sdm, mu1 for mixture2p, mixture3p and imm) through a tan_half link, and it is returned in radians in ⁠(-pi, pi)⁠ — a response bias relative to the target, since the response variable is the angular error. It is fixed to 0 unless the bmmformula predicts it explicitly, so an all-zero mu1 means the model never estimated one.

The inverse link is 2 * atan(), and the caveat above applies to it with particular force: 2 * atan(b_mu1_conditionB) is neither the bias in condition B nor the difference between conditions. The bias in condition B is 2 * atan(b_mu1_Intercept + b_mu1_conditionB), and the difference between the conditions is that value minus 2 * atan(b_mu1_Intercept), taken draw by draw.

Mixture weights

For mixture3p the returned thetat and thetant are probabilities but they do not sum to 1: brms holds the linear predictor of one mixture component at zero, and that component is the guessing distribution, so the remaining mass 1 - thetat - thetant is the probability of a guess. thetant is the total probability of a non-target response, summed over the lures, not the probability per lure.

At set sizes where no non-target was presented the model switches its non-target components off, so thetant is reported as exactly 0 and thetat as plogis() of its linear predictor, rather than as the value the set-size regression extrapolates to. This correction reads the lure indicators out of the prediction grid; if you supply newdata, columns you do not supply are filled from the first row of the model data rather than recomputed, and native_parameters() warns when the two disagree.

See Also

parameter_info(), native_transform(), conditional_effects.bmmfit()

Examples

fit <- bmm(
  bmf(c ~ 0 + set_size, kappa ~ 1),
  data = oberauer_lin_2017,
  model = sdm(resp_error = "dev_rad")
)

# draws of c and kappa for every set size, on the native scale
np <- native_parameters(fit, re_formula = NA)
head(np)

# a contrast is plain arithmetic on the draws
c_draws <- subset(np, parameter == "c")
quantile(
  c_draws$value[c_draws$set_size == 1] - c_draws$value[c_draws$set_size == 4],
  probs = c(0.025, 0.5, 0.975)
)

# posterior summaries of the transformed draws
native_parameters(fit, re_formula = NA, summary = TRUE)

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