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Zero-inflated GEC (Katz-family) regression

Source: R/gec_variants.R
zi_gec.Rd

A structural-zero mixture with a GEC count component whose dispersion delta is estimated freely. Estimated by robust direct maximum likelihood, seeded from separate fits (a GEC on the positive counts for the count parameters and a logit of the zero indicator for the inflation), exactly as in zi_cpb(); this avoids the degenerate pi -> 0 basin that coordinate-wise EM falls into.

Usage

zi_gec(
  formula,
  data,
  zero = NULL,
  zero_fe = NULL,
  se = c("none", "bootstrap"),
  B = 500,
  cluster = NULL,
  max.support = 500,
  maxit = 200,
  tol = 1e-06,
  offset = NULL,
  weights = NULL,
  cores = 1L
)

Arguments

formula

Count formula, y ~ x.

data

A data frame.

zero

One-sided formula for the structural-zero logit; defaults to the count formula's right-hand side.

zero_fe

Optional column name for fixed effects in the zero-inflation equation, entered as factor dummies (opt-in).

se

Coefficient inference: "none" (default) or "bootstrap".

B

Bootstrap resamples.

cluster

Optional cluster (column name or vector) for the bootstrap.

max.support

Guard on the maximum evaluated support.

maxit, tol

Optimizer controls.

offset

Optional exposure offset (log scale) for the count component: a numeric vector or the name of a column in data; the bootstrap carries it through every replicate.

weights

Optional frequency weights (a numeric vector or a column name); see cpb().

cores

Worker processes for the bootstrap; see cpb().

Value

An object of class "zi_gec".

See also

zi_cpb(), gec(), hurdle_gec()

Examples

# \donttest{
set.seed(4); n <- 300; x <- rnorm(n); z <- rnorm(n)
y <- ifelse(rbinom(n, 1, plogis(-0.5 + 0.8 * z)) == 1, 0L, rpois(n, exp(1 + 0.3 * x)))
zi_gec(y ~ x, data.frame(y = y, x = x, z = z), zero = ~ z)
#> Zero-Inflated Generalized Event Count (Katz family)
#> Call:  zi_gec(formula = y ~ x, data = data.frame(y = y, x = x, z = z),     zero = ~z)
#> N: 300   logLik: -494.3
#> 
#> Count (GEC) coefficients:
#> (Intercept)           x 
#>      1.0341      0.2551 
#> Count dispersion delta: 0.914  [underdispersed (point estimate)]
#> 
#> Zero-inflation (logit link) -- positive coefficients raise P(structural zero), i.e. lower the
#> chance of a positive count (the opposite direction from a hurdle participation model):
#> (Intercept)           z 
#>     -0.5634      0.8040 
#> Mean structural-zero probability: 0.371 
# }