gratia (version 0.8.2)

smooth_samples: Posterior draws for individual smooths

Description

Returns draws from the posterior distributions of smooth functions in a GAM. Useful, for example, for visualising the uncertainty in individual estimated functions.

Usage

smooth_samples(model, ...)
# S3 method for gam
smooth_samples(
  model,
  term = NULL,
  n = 1,
  data = newdata,
  seed = NULL,
  freq = FALSE,
  unconditional = FALSE,
  ncores = 1L,
  n_vals = 200,
  ...,
  newdata = NULL
)

Value

A tibble with additional classes "smooth_samples" and `"posterior_samples".

For the "gam" method, the columns currently returned (not in this order) are:

• smooth; character vector. Indicates the smooth function for that particular draw,

• term; character vector. Similar to smooth, but will contain the full label for the smooth, to differentiate factor-by smooths for example.

• by_variable; character vector. If the smooth involves a by term, the by variable will be named here, NA_character_ otherwise.

• row; integer. A vector of values seq_len(n_vals), repeated if n > 1L. Indexes the row in data for that particular draw.

• draw; integer. A vector of integer values indexing the particular posterior draw that each row belongs to.

• value; numeric. The value of smooth function for this posterior draw and covariate combination.

• xxx; numeric. A series of one or more columns containing data required for the smooth, named as per the variables involved in the respective smooth.

• Additional columns will be present in the case of factor by smooths, which will contain the level for the factor named in by_variable for that particular posterior draw.

Arguments

model

a fitted model of the supported types

...

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

term

character; select which smooth's posterior to draw from. The default (NULL) means the posteriors of all smooths in model wil be sampled from. If supplied, a character vector of requested terms.

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.

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.

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).

n_vals

numeric; how many locations to evaluate the smooth at if data not supplied

newdata

Deprecated: use data instead.

Warning

The set of variables returned and their order in the tibble is subject to change in future versions. Don't rely on position.

Author

Gavin L. Simpson

Examples

load_mgcv()
op <- options(cli.unicode = FALSE, pillar.sigfig = 3)
dat <- data_sim("eg1", n = 400, seed = 2)
m1 <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

sms <- smooth_samples(m1, term = "s(x0)", n = 5, seed = 42)
# \donttest{
sms
# }

## A factor by example (with a spurious covariate x0)
dat <- data_sim("eg4", n = 1000, seed = 2)

## fit model...
m2 <- gam(y ~ fac + s(x2, by = fac) + s(x0), data = dat)
sms <- smooth_samples(m2, n = 5, seed = 42)
draw(sms)
options(op)