library(bellreg)
data(cells)
# ML approach:
mle <- zibellreg(cells ~ smoker+gender|smoker+gender, data = cells, approach = "mle")
summary(mle)
#> Call:
#> zibellreg(formula = cells ~ smoker + gender | smoker + gender,
#> data = cells, approach = "mle")
#>
#> Zero-inflated regression coefficients:
#> Estimate StdErr z.value p.value
#> (Intercept) -1.95021 0.84291 -2.3137 0.020686 *
#> smoker 2.17462 0.82118 2.6482 0.008093 **
#> gender -0.49573 0.42045 -1.1791 0.238374
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#> Count regression coefficients:
#> Estimate StdErr z.value p.value
#> (Intercept) 0.716757 0.179792 3.9866 6.703e-05 ***
#> smoker -0.611758 0.183354 -3.3365 0.0008484 ***
#> gender 0.036254 0.177468 0.2043 0.8381297
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> ---
#> logLik = -610.3234 AIC = 1232.647
# Bayesian approach:
bayes <- zibellreg(cells ~ 1|smoker+gender, data = cells, approach = "bayes", refresh = FALSE)
summary(bayes)
#> Call:
#> zibellreg(formula = cells ~ 1 | smoker + gender, data = cells,
#> approach = "bayes", refresh = FALSE)
#>
#> Zero-inflated regression coefficients:
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff
#> (Intercept) -1.168 0.008 0.342 -1.937 -1.354 -1.127 -0.932 -0.636 1679.041
#> Rhat
#> (Intercept) 1
#>
#> Count regression coefficients:
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff
#> (Intercept) 0.718 0.003 0.145 0.433 0.619 0.718 0.818 1.003 2699.184
#> smoker -1.078 0.003 0.145 -1.363 -1.175 -1.077 -0.983 -0.792 2377.645
#> gender 0.173 0.003 0.142 -0.107 0.077 0.173 0.274 0.448 2809.804
#> Rhat
#> (Intercept) 1.001
#> smoker 1.000
#> gender 1.001
#> ---
#> Inference for Stan model: zibellreg.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
log_lik <- loo::extract_log_lik(bayes$fit)
loo::loo(log_lik)
#>
#> Computed from 4000 by 511 log-likelihood matrix.
#>
#> Estimate SE
#> elpd_loo -626.8 24.8
#> p_loo 4.5 0.4
#> looic 1253.6 49.5
#> ------
#> MCSE of elpd_loo is 0.0.
#> MCSE and ESS estimates assume independent draws (r_eff=1).
#>
#> All Pareto k estimates are good (k < 0.7).
#> See help('pareto-k-diagnostic') for details.