predict.trending_model: Predict method for trending_model objects

Description

Adds estimated values and associated confidence and/or prediction intervals to data based on trending_model fit.

Usage

# S3 method for trending_model
predict(
  object,
  data,
  name = "estimate",
  alpha = 0.05,
  add_ci = TRUE,
  ci_names = c("lower_ci", "upper_ci"),
  add_pi = TRUE,
  pi_names = c("lower_pi", "upper_pi"),
  simulate_pi = FALSE,
  sims = 2000,
  uncertain = TRUE,
  as_tibble = TRUE,
  ...
)

Value

If as_tibble = FALSE, a trending_predict object, which is a list subclass, with entries:

result: the input data frame with additional estimates and, optionally, confidence and or prediction intervals. NULL if the associated predict method fails.
warnings: any warnings generated during prediction.
errors: any errors generated during prediction.

If as_tibble = TRUE, a trending_predict_tbl object which is a tibble subclass with one row per model and columns 'result', 'warnings' and 'errors' with contents as above.

Arguments

object: A trending_model object.
data: A data.frame containing data to which the model is to be fit and estimates derived.
name: Character vector of length one giving the name to use for the calculated estimate.
alpha: The alpha threshold to be used for prediction intervals, defaulting to 0.05, i.e. 95% prediction intervals are derived.
add_ci: Should a confidence interval be added to the output. Default TRUE.
ci_names: Names to use for the resulting confidence intervals.
add_pi: Should a prediction interval be added to the output. Default TRUE.
pi_names: Names to use for the resulting prediction intervals.
simulate_pi: Should the prediction intervals for glm models be simulated. If TRUE, default, predict() uses the ciTools::add_pi() function to generate the intervals.
sims: The number of simulations to run when simulating prediction intervals for a glm model.
uncertain: Only used for glm models and when simulate_pi = FALSE. Default TRUE. If FALSE uncertainty in the fitted parameters is ignored when generating the parametric prediction intervals.
as_tibble: Should the output be converted to a tibble subclass.
...: Not currently used.

Author

Tim Taylor

Examples

Run this code

x = rnorm(100, mean = 0)
y = rpois(n = 100, lambda = exp(1.5 + 0.5*x))
dat <- data.frame(x = x, y = y)
poisson_model <- glm_model(y ~ x , family = "poisson")
predict(poisson_model, dat)
predict(poisson_model, dat, as_tibble = FALSE)