Draw new response values from the conditional distribution of the response

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

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)
#> # A tibble: 5,000 x 3
#>     .row .draw .response
#>    <int> <int>     <dbl>
#>  1     1     1      8.93
#>  2     2     1      4.23
#>  3     3     1      7.71
#>  4     4     1      8.51
#>  5     5     1     10.1 
#>  6     6     1      8.20
#>  7     7     1      8.95
#>  8     8     1      7.20
#>  9     9     1     18.1 
#> 10    10     1     12.7 
#> # i 4,990 more rows

## 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)
#> # A tibble: 50 x 3
#>     .row .draw .response
#>    <int> <int>     <dbl>
#>  1     1     1      9.42
#>  2     2     1      6.97
#>  3     3     1      8.10
#>  4     4     1      9.95
#>  5     5     1      6.75
#>  6     6     1     10.3 
#>  7     7     1     10.8 
#>  8     8     1     10.5 
#>  9     9     1      8.43
#> 10    10     1     12.2 
#> # i 40 more rows

## Exclude s(x1)
predicted_samples(m, n = 5, newd, exclude = "s(x1)", seed = 25)
#> # A tibble: 50 x 3
#>     .row .draw .response
#>    <int> <int>     <dbl>
#>  1     1     1      6.05
#>  2     2     1      5.28
#>  3     3     1      5.96
#>  4     4     1     13.7 
#>  5     5     1      4.36
#>  6     6     1      5.11
#>  7     7     1     12.5 
#>  8     8     1      5.66
#>  9     9     1     12.6 
#> 10    10     1      8.38
#> # i 40 more rows

## 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)")
)
#> # A tibble: 50 x 3
#>     .row .draw .response
#>    <int> <int>     <dbl>
#>  1     1     1    -1.94 
#>  2     2     1    -2.71 
#>  3     3     1    -2.03 
#>  4     4     1     5.73 
#>  5     5     1    -3.63 
#>  6     6     1    -2.87 
#>  7     7     1     4.48 
#>  8     8     1    -2.33 
#>  9     9     1     4.65 
#> 10    10     1     0.395
#> # i 40 more rows