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Predictions for flexCountReg models

predict.flexCountReg.Rd

Generates predictions for the expected count (lambda) for observations.

For countreg.rp (Random Parameters) models, three methods are available:

  • Simulated: Uses Halton draws to simulate the random parameters and averages the outcomes. This is a simulation-based approximation.

  • Individual: Estimates observation-specific coefficients (conditional on observed outcomes) using Empirical Bayes. Requires the outcome variable to be present in data.

  • Exact: Uses the analytical Moment Generating Functions (MGFs) of the random parameter distributions to calculate the exact expected value. This method is faster and removes simulation error.

For countreg, poisLindRE, and RENB models, the function calculates the expected value \(\mu = \exp(X\beta)\) (with appropriate adjustments for specific families like PLN or underreporting).

Usage

# S3 method for class 'flexCountReg'
predict(object, newdata = NULL, ...)

Arguments

object

a model object estimated using this R package.

newdata

optional dataframe for which to generate predictions.

...

optional arguments passed to the function. This includes `method`.

Value

A numeric vector of predicted expected counts for each observation in the provided data. If no data is provided, the predictions for the data used in estimating the model are provided.

Note

optional parameter `newdata`: a dataframe that has all of the variables in the formula and rpar_formula.

optional parameter `method`: Only valid for random parameters models (`countreg.rp`). Options include Simulated (default), Individual, or Exact.

References

Wood, J.S., Gayah, V. (2025). Out-of-sample prediction and interpretation for random parameter generalized linear models. Accident Analysis and Prevention, 220, 108147.

Examples

# \donttest{
# Load data and create a dummy variable
data("washington_roads")
washington_roads$AADT10kplus <- ifelse(washington_roads$AADT > 10000, 1, 0)

# =========================================================================
# 1. Fixed Parameter Model (countreg)
# =========================================================================
nb2_fixed <- countreg(Total_crashes ~ lnaadt + lnlength + speed50,
                      data = washington_roads, 
                      family = "NB2")
pred_fixed <- predict(nb2_fixed, data = washington_roads)

# =========================================================================
# 2. Random Parameters Model (countreg.rp)
# =========================================================================
rp_nb2 <- countreg.rp(Total_crashes ~ lnaadt + lnlength,
                      rpar_formula = ~ -1 + speed50,
                      data = washington_roads,
                      family = "NB2",
                      rpardists = c(speed50 = "n"),
                      ndraws = 100)

# Method A: Simulated (Default)
pred_sim <- predict(rp_nb2, data = washington_roads, method = "Simulated")

# Method B: Exact (Analytical MGF)
pred_exact <- predict(rp_nb2, data = washington_roads, method = "Exact")

# =========================================================================
# 3. Random Effects Models (poisLindRE / RENB)
# =========================================================================
pl_re <- poisLind.re(Total_crashes ~ lnaadt + lnlength,
                     data = washington_roads,
                     group_var = "ID")
#> Warning: NaNs produced
pred_pl_re <- predict(pl_re, data = washington_roads)
# }