Fit an Integer Adjusted Exponential, Gamma or Lognormal distributions
Source:R/estimate_delay.R
dist_fit.Rd![[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,
backend = "rstan"
)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.
- backend
Character string indicating the backend to use for fitting stan models. Supported arguments are "rstan" (default) or "cmdstanr".
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: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 5.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.54 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.141 seconds (Warm-up)
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#> Chain 1: 0.226 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.131 seconds (Warm-up)
#> Chain 2: 0.058 seconds (Sampling)
#> Chain 2: 0.189 seconds (Total)
#> Chain 2:
#> WARN [2026-04-14 09:06:06] dist_fit (chain: 1): Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess -
#> WARN [2026-04-14 09:06:06] dist_fit (chain: 2): Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess -
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=1500; warmup=1000; thin=1;
#> post-warmup draws per chain=500, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> lambda[1] 2.47 0.02 0.40 1.79 2.19 2.43 2.72 3.39 295 1
#> lp__ -17.12 0.05 0.77 -19.31 -17.27 -16.83 -16.64 -16.58 206 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Apr 14 09:06:06 2026.
#> 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 0.000276 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.76 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1: Elapsed Time: 2.53 seconds (Warm-up)
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#> Chain 1: 3.822 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000308 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.08 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
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#> Chain 2:
#> WARN [2026-04-14 09:06:13] dist_fit (chain: 1): 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 -
#> WARN [2026-04-14 09:06:13] dist_fit (chain: 2): 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 -
#> WARN [2026-04-14 09:06:13] dist_fit (chain: 1): Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess -
#> WARN [2026-04-14 09:06:13] dist_fit (chain: 2): Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess -
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=1500; warmup=1000; thin=1;
#> post-warmup draws per chain=500, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> alpha_raw[1] 0.94 0.04 0.56 0.06 0.52 0.86 1.29 2.22 179 1.01
#> beta_raw[1] 0.97 0.04 0.56 0.13 0.57 0.92 1.33 2.19 187 1.02
#> alpha[1] 6.29 0.04 0.56 5.41 5.87 6.22 6.64 7.57 179 1.01
#> beta[1] 6.15 0.04 0.56 5.30 5.75 6.10 6.51 7.36 187 1.02
#> lp__ -14.89 0.15 1.49 -18.80 -15.43 -14.44 -13.83 -13.44 94 1.02
#>
#> Samples were drawn using NUTS(diag_e) at Tue Apr 14 09:06:13 2026.
#> 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 5.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.56 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 5.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.52 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: Elapsed Time: 0.326 seconds (Warm-up)
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#> Chain 2:
#> Inference for Stan model: dist_fit.
#> 2 chains, each with iter=1500; warmup=1000; thin=1;
#> post-warmup draws per chain=500, total post-warmup draws=1000.
#>
#> mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
#> mu[1] 1.62 0.00 0.02 1.58 1.60 1.62 1.63 1.66 630 1
#> sigma[1] 0.17 0.00 0.02 0.14 0.15 0.16 0.18 0.20 739 1
#> lp__ -74.72 0.04 0.92 -77.17 -75.16 -74.41 -74.03 -73.76 499 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Apr 14 09:06:14 2026.
#> 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).
# }