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Hurdle GEC (Katz-family) regression

Source: R/gec_variants.R
hurdle_gec.Rd

Joins a participation (hurdle) logit to a zero-truncated GEC intensity on the positive counts. Unlike hurdle_cpb(), whose intensity is fixed to the underdispersed CPB, the GEC intensity estimates its own dispersion direction.

Usage

hurdle_gec(
  formula,
  data,
  participation = NULL,
  part_fe = NULL,
  link = c("logit", "probit", "cloglog"),
  offset = NULL,
  cluster = NULL,
  se = c("none", "bootstrap"),
  B = 500,
  weights = NULL,
  cores = 1L,
  max.support = 500
)

Arguments

formula

Intensity formula, y ~ x.

data

A data frame.

participation

One-sided formula for the participation logit; defaults to the intensity's right-hand side.

part_fe

Optional column name for fixed effects in the participation model (added as factor dummies).

Link for the participation model: "logit" (default), "probit", or "cloglog".

offset

Optional offset (log scale) for the intensity: a numeric vector or a column name in data.

cluster

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

se

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

B

Bootstrap resamples.

weights

Optional frequency weights (a numeric vector or a column name), applied to both margins; see cpb().

cores

Worker processes for the intensity bootstrap; see cpb().

max.support

Guard on the maximum evaluated support.

Value

An object of class "hurdle_gec".

Limitation

Unlike hurdle_cpb(), hurdle_gec() has no fe argument for a concentrated fixed-effects intensity (no concentrated zero-truncated GEC is implemented). For within-unit intensities, enter the unit factor as dummies in formula (feasible for moderate unit counts), or use hurdle_cpb().

See also

hurdle_cpb(), gec(), zi_gec()

Examples

# \donttest{
set.seed(3); n <- 300; x <- rnorm(n); z <- rnorm(n)
y <- ifelse(rbinom(n, 1, plogis(0.3 + 0.8 * z)) == 1, rpois(n, exp(1 + 0.3 * x)) + 1L, 0L)
hurdle_gec(y ~ x, data.frame(y = y, x = x, z = z), participation = ~ z)
#> Hurdle Generalized Event Count (Katz family)
#> Call:  hurdle_gec(formula = y ~ x, data = data.frame(y = y, x = x, z = z),     participation = ~z)
#> Units: 300 (166 participate, 55%)
#> 
#> Participation (logit link) -- positive coefficients raise P(Y > 0), i.e. participation:
#> (Intercept)           z 
#>      0.2704      0.8885 
#> 
#> Intensity (zero-truncated GEC) coefficients:
#> (Intercept)           x 
#>      1.3504      0.2550 
#> Intensity dispersion delta: 0.683  [underdispersed (point estimate)]
# }