predict.list: Predict method for trending_model objects

In trending: Model Temporal Trends

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 class 'list'
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,
  ...
)

Arguments

object	A list of trending_model objects.
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.
...	Not currently used.

Value

A trending_predict_tbl object which is a tibble subclass with one row per model and columns:

• 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.

Author(s)

Tim Taylor

See Also

predict.trending_model(), predict.trending_fit(), predict.trending_fit_tbl(),

Examples

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")
negbin_model <- glm_nb_model(y ~ x)
predict(list(poisson_model, negbin_model), dat)
predict(list(pm = poisson_model, nm = negbin_model), dat)

trending documentation built on April 4, 2023, 1:07 a.m.