gratia (version 0.9.0)

fitted_samples: Draw fitted values from the posterior distribution

Description

Expectations (fitted values) of the response drawn from the posterior distribution of fitted model using a Gaussian approximation to the posterior or a simple Metropolis Hastings sampler.

Usage

fitted_samples(model, ...)
# S3 method for gam
fitted_samples(
  model,
  n = 1,
  data = newdata,
  seed = NULL,
  scale = c("response", "linear_predictor"),
  method = c("gaussian", "mh", "inla", "user"),
  n_cores = 1,
  burnin = 1000,
  thin = 1,
  t_df = 40,
  rw_scale = 0.25,
  freq = FALSE,
  unconditional = FALSE,
  draws = NULL,
  ...,
  newdata = NULL,
  ncores = NULL
)

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.

Arguments

model

a fitted model of the supported types

...

arguments passed to other methods. For fitted_samples(), these are passed on to predict.gam(). For posterior_samples() these are passed on to fitted_samples(). For predicted_samples() these are passed on to the relevant simulate() method.

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; what scale should the fitted values be returned on? "linear predictor" is a synonym for "link" if you prefer that terminology.

method

character; which method should be used to draw samples from the posterior distribution. "gaussian" uses a Gaussian (Laplace) approximation to the posterior. "mh" uses a Metropolis Hastings sampler that alternates t proposals with proposals based on a shrunken version of the posterior covariance matrix. "inla" uses a variant of Integrated Nested Laplace Approximation due to Wood (2019), (currently not implemented). "user" allows for user-supplied posterior draws (currently not implemented).

n_cores

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).

burnin

numeric; number of samples to discard as the burnin draws. Only used with method = "mh".

thin

numeric; the number of samples to skip when taking n draws. Results in thin * n draws from the posterior being taken. Only used with method = "mh".

t_df

numeric; degrees of freedome for t distribution proposals. Only used with method = "mh".

rw_scale

numeric; Factor by which to scale posterior covariance matrix when generating random walk proposals. Negative or non finite to skip the random walk step. Only used with method = "mh".

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.

draws

matrix; user supplied posterior draws to be used when method = "user".

newdata

Deprecated: use data instead.

ncores

Deprecated; use n_cores instead. The 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).

Author

Gavin L. Simpson

References

Wood, S.N., (2020). Simplified integrated nested Laplace approximation. Biometrika 107, 223--230. tools:::Rd_expr_doi("10.1093/biomet/asz044")

Examples

load_mgcv()
# \dontshow{
op <- options(pillar.sigfig = 3, cli.unicode = FALSE)
# }
dat <- data_sim("eg1", n = 1000, dist = "normal", scale = 2, seed = 2)
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

fs <- fitted_samples(m1, n = 5, seed = 42)
# \donttest{
fs
# }

# can generate own set of draws and use them
drws <- generate_draws(m1, n = 2, seed = 24)
fs2 <- fitted_samples(m1, method = "user", draws = drws)
# \donttest{
fs2
# }
# \dontshow{
options(op)
# }