coef(), vcov(), confint(), nobs() and summary() for hurdle fits
(panhurdle()).
The coefficients of the zero part (conditional logit of 1(y > 0)) and
of the count part (zero-truncated count model on the positive
observations) are named "zero:..." and "count:...". The two parts are
estimated from separate factors of the hurdle likelihood, so vcov() is
block diagonal. No logLik() method is provided: the zero part maximises
a conditional likelihood and the count part an unconditional likelihood
with individual intercepts, so their sum is not the log-likelihood of one
model.
Usage
# S3 method for class 'panglm_hurdle'
coef(object, ...)
# S3 method for class 'panglm_hurdle'
vcov(object, ...)
# S3 method for class 'panglm_hurdle'
nobs(object, ...)
# S3 method for class 'panglm_hurdle'
confint(object, parm, level = 0.95, ...)
# S3 method for class 'panglm_hurdle'
summary(object, ...)
# S3 method for class 'summary.panglm_hurdle'
print(x, digits = max(3, getOption("digits") - 3), ...)Value
coef(): a named vector; vcov(): a matrix; confint(): a
two-column matrix; nobs(): the number of observations; summary():
an object of class "summary.panglm_hurdle" with one coefficient table
per part (zero, count: estimate, standard error, z value and
p-value, from the inverse Hessian of each part) and the fit
information.
See also
Other panhurdle methods:
plot.panglm_hurdle()
Examples
data(copd)
fit <- panhurdle(exacerbations ~ crp, data = copd, index = c("id", "visit"))
#> 26 group(s) with no within-group outcome variation dropped (all-0 or all-1) -- they carry no information for the conditional likelihood.
#> 6 group(s) whose positive counts are all 1 dropped from the count part -- their intercept is not finite and they carry no information.
coef(fit)
#> zero:crp count:crp
#> 0.10889286 0.04246066
confint(fit)
#> 2.5 % 97.5 %
#> zero:crp -0.05129363 0.26907935
#> count:crp -0.00624235 0.09116367
summary(fit)
#>
#> Formula: exacerbations ~ crp
#>
#> Estimator: fixed-effects hurdle model (conditional logit and zero-truncated Poisson)
#> n = 300 positive = 238 vcov: model-based (inverse Hessian)
#>
#> Zero part, 1(y > 0), conditional logit (groups used = 34):
#> Estimate Std. Error z value Pr(>|z|)
#> crp 0.10889 0.08173 1.332 0.183
#>
#> Count part, y | y > 0, zero-truncated Poisson (groups used = 53):
#> Estimate Std. Error z value Pr(>|z|)
#> crp 0.04246 0.02485 1.709 0.0875 .
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>