Simulate responses from a fitted underdisp model

Draws nsim replicate response vectors from the fitted model, at the estimated parameters and the estimation data, in the format of stats::simulate(). Available for every model class in the package: cpb, cpb_fe, gec, gec_fe (through inheritance), count_reg, hurdle_count, zi_count, hurdle_cpb, hurdle_gec, zi_cpb, and zi_gec. Two-part models first draw the binary stage (participation or structural zero), then the count stage from its own distribution, so the replicates carry the model's full zero structure.

Usage

# S3 method for class 'cpb'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'cpb_fe'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'gec'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'count_reg'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'hurdle_cpb'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'hurdle_gec'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'hurdle_count'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'zi_cpb'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'zi_gec'
simulate(object, nsim = 1, seed = NULL, ...)

# S3 method for class 'zi_count'
simulate(object, nsim = 1, seed = NULL, ...)

Arguments

object

A fitted model from this package.

nsim

Number of replicate response vectors.

seed

Optional seed, handled as in stats::simulate(): the caller's RNG state is restored on exit when a seed is supplied.

...

Unused.

Value

A data frame with nsim integer columns, one row per observation, with a "seed" attribute.

Details

The main consumer is simulated-residual diagnostics: DHARMa::createDHARMa(simulatedResponse = as.matrix(simulate(fit, 250)), observedResponse = y, fittedPredictedResponse = fitted(fit), integerResponse = TRUE) works for any fit in the family.

Examples

set.seed(1); x <- rnorm(200)
N <- pmax(round(exp(1.4 + 0.4 * x) / 0.5), 1); y <- rbinom(200, N, 0.5)
fit <- cpb(y ~ x, data = data.frame(y = y, x = x)[y > 0, ], se = "none")
sims <- simulate(fit, nsim = 5)
colMeans(sims)
#>    sim_1    sim_2    sim_3    sim_4    sim_5 
#> 4.808081 4.565657 4.550505 4.853535 4.479798