feepred: Predicted Counts from One-Inflated or Truncated Count Models

In fee: Estimate the First-Exposure Effect (FEE) using Count Data Models

Predicted Counts from One-Inflated or Truncated Count Models

Description

Computes the predicted count distribution from a fitted model of class "oneinflmodel", "truncmodel", or "basicPoisson". The function returns the expected frequency for each count value from 1 up to maxpred, based on the model's parameters.

Usage

feepred(model, data, maxpred)

Arguments

model	A fitted model object of class "oneinflmodel", "truncmodel", or "basicPoisson".
data	A data frame containing the covariates used to fit the model.
maxpred	Optional integer specifying the maximum count value for which to compute predicted frequencies. If not supplied, defaults to the maximum observed count in the data.

Details

The function determines the model type based on its class and the dist attribute, and applies the appropriate density function:

• For oneinflmodel (Poisson): one-inflated positive Poisson distribution.

• For oneinflmodel (negbin): one-inflated zero-truncated negative binomial.

• For truncmodel (Poisson): truncated positive Poisson.

• For truncmodel (negbin): zero-truncated negative binomial.

Value

A numeric vector of length maxpred, giving the predicted expected frequency of each count from 1 to maxpred.

See Also

feeplot, fee, dfee

Examples

df <- data.frame(x = runif(10,0,10), d = sample(c(0,1), 10, replace=TRUE), y = rpois(10, 3) + 1)
model <- oneinfl::oneinfl(formula = y ~ x + d | x + d, df = df, dist = "Poisson")
feepred(model, data = df)

fee documentation built on Aug. 8, 2025, 7:45 p.m.