The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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) 0.98524996 0.33219431 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", refresh = FALSE)
summary(bayes)
#>
#> bellreg(formula = nf ~ lroll, data = faults, approach = "bayes",
#> refresh = FALSE)
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> (Intercept) 0.977 0.007 0.331 0.304 0.757 0.980 1.201 1.620 2326.404 1.002
#> lroll 0.002 0.000 0.000 0.001 0.002 0.002 0.002 0.003 2648.453 1.001
#>
#> 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.
log_lik <- loo::extract_log_lik(bayes$fit)
loo::loo(log_lik)
#> Warning: Relative effective sample sizes ('r_eff' argument) not specified.
#> For models fit with MCMC, the reported PSIS effective sample sizes and
#> MCSE estimates will be over-optimistic.
#>
#> Computed from 4000 by 32 log-likelihood matrix
#>
#> Estimate SE
#> elpd_loo -91.0 3.9
#> p_loo 2.0 0.6
#> looic 182.1 7.9
#> ------
#> Monte Carlo SE of elpd_loo is 0.0.
#>
#> All Pareto k estimates are good (k < 0.5).
#> See help('pareto-k-diagnostic') for details.
loo::waic(log_lik)
#> Warning:
#> 1 (3.1%) p_waic estimates greater than 0.4. We recommend trying loo instead.
#>
#> Computed from 4000 by 32 log-likelihood matrix
#>
#> Estimate SE
#> elpd_waic -91.0 3.9
#> p_waic 1.9 0.6
#> waic 182.0 7.9
#>
#> 1 (3.1%) p_waic estimates greater than 0.4. We recommend trying loo instead.
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.
Health stats visible at Monitor.