![[Stable]](figures/lifecycle-stable.svg)
Fits an integer adjusted exponential, gamma or lognormal distribution using
stan.
Usage
dist_fit(
values = NULL,
samples = 1000,
cores = 1,
chains = 2,
dist = "exp",
verbose = FALSE
)Arguments
- values
Numeric vector of values
- samples
Numeric, number of samples to take. Must be >= 1000. Defaults to 1000.
- cores
Numeric, defaults to 1. Number of CPU cores to use (no effect if greater than the number of chains).
- chains
Numeric, defaults to 2. Number of MCMC chains to use. More is better with the minimum being two.
- dist
Character string, which distribution to fit. Defaults to exponential (
"exp") but gamma ("gamma") and lognormal ("lognormal") are also supported.- verbose
Logical, defaults to FALSE. Should verbose progress messages be printed.
Examples
# \donttest{
# integer adjusted exponential model
dist_fit(rexp(1:100, 2),
samples = 1000, dist = "exp",
cores = ifelse(interactive(), 4, 1), verbose = TRUE
)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 5.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.58 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2: Rejecting initial value:
#> Chain 2: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2: Stan can't start sampling from this initial value.
#> Chain 2:
#> Chain 2: Gradient evaluation took 5.5e-05 seconds
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#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> lambda[1] 2.93 0.02 0.53 2.03 2.55 2.89 3.26 4.03 565 1
#> lp__ -11.68 0.03 0.75 -13.82 -11.86 -11.40 -11.21 -11.15 631 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Sep 26 15:26:16 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# integer adjusted gamma model
dist_fit(rgamma(1:100, 5, 5),
samples = 1000, dist = "gamma",
cores = ifelse(interactive(), 4, 1), verbose = TRUE
)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000262 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.62 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
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#> Chain 2:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> alpha_raw[1] 0.86 0.03 0.55 0.04 0.45 0.79 1.17 2.13 283 1.01
#> beta_raw[1] 1.02 0.03 0.56 0.11 0.58 0.96 1.40 2.23 352 1.01
#> alpha[1] 6.41 0.03 0.55 5.60 6.01 6.35 6.72 7.69 283 1.01
#> beta[1] 6.55 0.03 0.56 5.64 6.11 6.49 6.93 7.76 352 1.01
#> lp__ -12.45 0.11 1.41 -16.25 -13.04 -12.01 -11.41 -10.99 156 1.02
#>
#> Samples were drawn using NUTS(diag_e) at Tue Sep 26 15:26:25 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# integer adjusted lognormal model
dist_fit(rlnorm(1:100, log(5), 0.2),
samples = 1000, dist = "lognormal",
cores = ifelse(interactive(), 4, 1), verbose = TRUE
)
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 7.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.75 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
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#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=2000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> mu[1] 1.63 0.00 0.02 1.59 1.62 1.63 1.64 1.67 1443 1
#> sigma[1] 0.16 0.00 0.02 0.13 0.15 0.16 0.17 0.19 1286 1
#> lp__ -71.74 0.03 0.96 -74.22 -72.12 -71.44 -71.05 -70.80 831 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Sep 26 15:26:26 2023.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# }