Fit an Integer Adjusted Exponential, Gamma or Lognormal distributions
Source:R/estimate_delay.R
dist_fit.RdFits 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:
#> Chain 1: Gradient evaluation took 2.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.066 seconds (Warm-up)
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#> Chain 1: 0.105 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.064 seconds (Warm-up)
#> Chain 2: 0.038 seconds (Sampling)
#> Chain 2: 0.102 seconds (Total)
#> 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
#> lambda[1] 2.41 0.02 0.37 1.78 2.16 2.38 2.66 3.17 344 1.01
#> lp__ -17.82 0.05 0.68 -19.64 -18.02 -17.53 -17.35 -17.31 189 1.02
#>
#> Samples were drawn using NUTS(diag_e) at Fri Nov 6 12:26:42 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 0.000177 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.77 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1: Elapsed Time: 1.301 seconds (Warm-up)
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 0.000156 seconds
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#> Chain 2:
#> Chain 2: Elapsed Time: 1.239 seconds (Warm-up)
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#> Chain 2: 1.858 seconds (Total)
#> Chain 2:
#> WARN [2026-11-06 12:26:46] dist_fit (chain: 1, 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 [2026-11-06 12:26:46] dist_fit (chain: 1, 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] 0.82 0.05 0.57 0.05 0.35 0.72 1.17 2.16 128 1.02
#> beta_raw[1] 0.84 0.04 0.52 0.06 0.41 0.78 1.18 1.95 217 1.00
#> alpha[1] 7.46 0.05 0.57 6.69 6.99 7.36 7.81 8.80 128 1.02
#> beta[1] 7.32 0.04 0.52 6.53 6.89 7.26 7.66 8.43 217 1.00
#> lp__ -11.46 0.12 1.37 -14.85 -12.12 -11.17 -10.41 -9.95 142 1.01
#>
#> Samples were drawn using NUTS(diag_e) at Fri Nov 6 12:26:46 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 3.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.39 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: 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 3.1e-05 seconds
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.176 seconds (Warm-up)
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#> Chain 2: 0.262 seconds (Total)
#> 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] 1.63 0.00 0.02 1.59 1.62 1.63 1.65 1.67 664 1.00
#> sigma[1] 0.18 0.00 0.02 0.15 0.17 0.18 0.19 0.22 518 1.01
#> lp__ -79.95 0.05 1.06 -82.92 -80.32 -79.60 -79.22 -78.97 407 1.00
#>
#> Samples were drawn using NUTS(diag_e) at Fri Nov 6 12:26:46 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).
# }