Predict from a hurdle Poisson/NB fit

Usage

# S3 method for class 'hurdle_count'
predict(
  object,
  newdata = NULL,
  type = c("response", "participation", "intensity"),
  offset = NULL,
  se.fit = FALSE,
  interval = c("none", "confidence"),
  level = 0.95,
  ...
)

Arguments

object: A "hurdle_count" object.
newdata: Optional data frame of covariate profiles. A fixed-effects unit an equation has not seen, or every row when its column is absent, enters that equation at the reference unit, and a unit at a limit at that limit (see fe,part_fe in hurdle_count()).
type: "response" (marginal E(Y), zeros included), "participation" (P(Y > 0)), or "intensity" (E(Y | Y > 0)).
offset: Optional offset (log scale) for the intensity when predicting on newdata; a numeric vector or a column name in newdata.
se.fit, interval, level: Standard errors (se.fit = TRUE) and a level confidence interval (interval = "confidence") of the predicted quantity at the rows of newdata; see predict.cpb().
...: Unused.

Value

A numeric vector. With interval = "confidence", a matrix with columns fit, lwr and upr; with se.fit = TRUE, a list of fit (the vector or the matrix), se.fit and method (each row's interval source). Without se.fit, the source is the attribute method.