R: Predict method for trending

predict.list

R Documentation

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

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)