predicted_samples {gratia}

Draw new response values from the conditional distribution of the response

Description

Predicted values of the response (new response data) are drawn from the fitted model, created via simulate() (e.g. simulate.gam()) and returned in a tidy, long, format. These predicted values do not include the uncertainty in the estimated model; they are simply draws from the conditional distribution of the response.

Usage

predicted_samples(model, ...)

## S3 method for class 'gam'
predicted_samples(
  model,
  n = 1,
  data = newdata,
  seed = NULL,
  weights = NULL,
  ...,
  newdata = NULL
)

Arguments

model

a fitted model of the supported types

...

arguments passed to other methods. For fitted_samples(), these are passed on to mgcv::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.

weights

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

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

Examples

load_mgcv()

dat <- data_sim("eg1", n = 1000, dist = "normal", scale = 2, seed = 2)
m <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat, method = "REML")

predicted_samples(m, n = 5, seed = 42)

## Can pass arguments to predict.gam()

newd <- data.frame(
  x0 = runif(10), x1 = runif(10), x2 = runif(10),
  x3 = runif(10)
)

## Exclude s(x2)
predicted_samples(m, n = 5, newd, exclude = "s(x2)", seed = 25)

## Exclude s(x1)
predicted_samples(m, n = 5, newd, exclude = "s(x1)", seed = 25)

## Select which terms --- result should be the same as previous
## but note that we have to include any parametric terms, including the
## constant term
predicted_samples(m,
  n = 5, newd, seed = 25,
  terms = c("Intercept", "s(x0)", "s(x2)", "s(x3)")
)