Skip to contents

Hurdle count regression for the matched count families

Source: R/glm_families.R
hurdle_count.Rd

Fits a participation model joined to a zero-truncated intensity from any of the package's count families (see count_reg(), Families), the count analogue of hurdle_cpb(). Returned as a "hurdle_count" object that compare_models() and score() accept, so a hurdle-CPB and a hurdle gamma-count can be compared on one footing.

Usage

hurdle_count(
  formula,
  data,
  family = c("poisson", "negbin", "compois", "mpcmp", "genpois", "gammacount",
    "doublepois"),
  participation = NULL,
  fe = NULL,
  part_fe = NULL,
  link = c("logit", "probit", "cloglog"),
  offset = NULL,
  weights = NULL,
  se = c("analytic", "robust", "cluster", "none"),
  cluster = NULL
)

Arguments

formula

Intensity formula (y ~ x).

data

A data frame.

family

The intensity family; see count_reg().

participation

Optional one-sided formula for the participation model; defaults to the intensity right-hand side.

fe, part_fe

Optional fixed-effects column names for the intensity and participation models (entered as factor dummies).

Link for the participation model: "logit" (default), "probit", or "cloglog". Positive participation coefficients raise the probability of a positive count (participation).

offset

Optional offset for the intensity, on the linear-predictor (log) scale: a numeric vector or the name of a column in data.

weights

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

se, cluster

Standard-error type and optional cluster for the intensity; see count_reg().

Value

An object of class "hurdle_count".

See also

hurdle_cpb(), zi_count(), compare_models()

Examples

set.seed(1); n <- 300; x <- rnorm(n); z <- rnorm(n)
y <- ifelse(rbinom(n, 1, plogis(0.4 + 0.8 * z)) == 1, rpois(n, exp(1 + 0.3 * x)) + 1L, 0L)
hurdle_count(y ~ x, data.frame(y = y, x = x, z = z), family = "poisson",
             participation = ~ z, se = "none")
#> Hurdle Poisson regression
#> Units: 300 (166 participate, 55%)
#> 
#> Participation (logit link) -- positive coefficients raise P(Y > 0), i.e. participation:
#> (Intercept)           z 
#>      0.2493      0.7361 
#> 
#> Intensity (zero-truncated Poisson):
#> (Intercept)           x 
#>      1.3394      0.2183