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) -1.95194 0.84468 -2.3109 0.020840 *
#> smoker 2.17615 0.82290 2.6445 0.008182 **
#> gender -0.49590 0.42061 -1.1790 0.238390
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#>
#> Count regression coefficients:
#> Estimate StdErr z.value p.value
#> (Intercept) 0.716552 0.179846 3.9843 6.769e-05 ***
#> smoker -0.611706 0.183400 -3.3354 0.0008519 ***
#> gender 0.036264 0.177481 0.2043 0.8380972
#> ---
#> 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)
#>
#> Prior specifications:
#> intercept ~ normal(0, 10)
#> psi ~ normal(mu = 0, sigma = 2.5)
#> beta ~ normal(0, 2.5)
#>
#> Zero-inflated regression coefficients:
#> mean sd 2.5% 50% 97.5% n_eff Rhat
#> -1.1520 0.3221 -1.8750 -1.1159 -0.6296 1816.8968 1.0014
#>
#> Count regression coefficients:
#> mean sd 2.5% 50% 97.5% n_eff Rhat
#> (Intercept) 0.7181 0.1458 0.4299 0.7175 1.0038 3086.932 1.0002
#> smoker -1.0741 0.1478 -1.3673 -1.0720 -0.7861 2296.083 1.0002
#> gender 0.1745 0.1418 -0.1062 0.1738 0.4459 2821.676 0.9998
#> ---
#> 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.