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The goal of bellreg is to provide a set of functions to fit regression models for count data with overdispersion using the Bell distribution. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) doi:10.1016/j.apm.2017.12.014 and Lemonte et al. (2020) doi:10.1080/02664763.2019.1636940.
You can install the development version of bellreg from GitHub with:
library(bellreg)
data(faults)
# ML approach:
mle <- bellreg(nf ~ lroll, data = faults, approach = "mle")
summary(mle)
#> Call:
#> bellreg(formula = nf ~ lroll, data = faults, approach = "mle")
#>
#> Coefficients:
#> Estimate StdErr z.value p.value
#> (Intercept) 0.98524063 0.33219476 2.9659 0.003018 **
#> lroll 0.00190934 0.00049003 3.8963 9.766e-05 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> logLik = -88.96139 AIC = 181.9228
# Bayesian approach:
bayes <- bellreg(nf ~ lroll, data = faults, approach = "bayes")
#>
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000242 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.42 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000201 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 2.01 seconds.
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#> Chain 2:
#>
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 0.000344 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 3.44 seconds.
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#> Chain 3:
#>
#> SAMPLING FOR MODEL 'bellreg' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 0.000293 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 2.93 seconds.
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summary(bayes)
#>
#> bellreg(formula = nf ~ lroll, data = faults, approach = "bayes")
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> (Intercept) 0.969 0.007 0.332 0.296 0.746 0.974 1.199 1.613 2607.463 1.001
#> lroll 0.002 0.000 0.000 0.001 0.002 0.002 0.002 0.003 2909.071 1.000
#>
#> Inference for Stan model: bellreg.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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