posterior_samples: Draw samples from the posterior distribution of an estimated...

posterior_samplesR Documentation

Draw samples from the posterior distribution of an estimated model

Description

Draw samples from the posterior distribution of an estimated model

Usage

posterior_samples(model, ...)

## S3 method for class 'gam'
posterior_samples(
  model,
  n,
  data = newdata,
  seed,
  scale = c("response", "linear_predictor"),
  freq = FALSE,
  unconditional = FALSE,
  weights = NULL,
  ncores = 1L,
  ...,
  newdata = NULL
)

Arguments

model

a fitted model of the supported types

...

arguments passed to other methods. For fitted_samples(), these are passed on to predict.gam().

n

numeric; the number of posterior samples to return.

data

data frame; new observations at which the posterior draws from the model should be evaluated. If not supplied, the data used to fit the model will be used for data, if available in model.

seed

numeric; a random seed for the simulations.

scale

character;

freq

logical; TRUE to use the frequentist covariance matrix of the parameter estimators, FALSE to use the Bayesian posterior covariance matrix of the parameters.

unconditional

logical; if TRUE (and freq == FALSE) then the Bayesian smoothing parameter uncertainty corrected covariance matrix is used, if available.

weights

numeric; a vector of prior weights. If data is null then defaults to object[["prior.weights"]], otherwise a vector of ones.

ncores

number of cores for generating random variables from a multivariate normal distribution. Passed to mvnfast::rmvn(). Parallelization will take place only if OpenMP is supported (but appears to work on Windows with current R).

newdata

Deprecated: use data instead.

Value

A tibble (data frame) with 3 columns containing the posterior predicted values in long format. The columns are

  • row (integer) the row of data that each posterior draw relates to,

  • draw (integer) an index, in range 1:n, indicating which draw each row relates to,

  • response (numeric) the predicted response for the indicated row of data.

Author(s)

Gavin L. Simpson


gratia documentation built on Feb. 16, 2023, 10:40 p.m.