confint() methods for the whole family, one interval rule per inference
type: bootstrap percentile intervals for cpb() (coefficients; the
dispersion parameter gets its profile-likelihood interval, first-order or
calibrated by calibrate_alpha()) and gec()
(coefficients and delta); normal-approximation intervals from the
bootstrap standard errors for the fixed-effects fits (cpb_fe(),
gec_fe()) and the zero-inflated mixtures (zi_cpb(), zi_gec()); Wald
intervals from the analytic covariance for count_reg() and zi_count()
(the dispersion parameter's interval is mapped from its estimation scale to
the natural scale); and, for the hurdles, the participation model's Wald
intervals (prefixed participation:) stacked over the intensity model's intervals
(prefixed intensity:); the zero-inflated classes prefix count: and zero:.
The same names label vcov(), tidy(), and the texreg tables. A fit without inference errors informatively.
Usage
# S3 method for class 'gec'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'cpb_fe'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'count_reg'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'zi_count'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'hurdle_cpb'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'hurdle_gec'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'hurdle_count'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'zi_cpb'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'zi_gec'
confint(object, parm, level = 0.95, ...)