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 3.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.108 seconds (Warm-up)
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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 3e-05 seconds
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#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.109 seconds (Warm-up)
#> Chain 2: 0.052 seconds (Sampling)
#> Chain 2: 0.161 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] 2.74 0.02 0.44 2.01 2.41 2.70 3.01 3.66 543 1.00
#> lp__ -12.68 0.03 0.57 -14.22 -12.91 -12.46 -12.29 -12.24 290 1.01
#>
#> Samples were drawn using NUTS(diag_e) at Mon Oct 5 08:10:47 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 0.000263 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.63 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 0.000254 seconds
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#> Chain 2:
#> Chain 2: Elapsed Time: 2.081 seconds (Warm-up)
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#> Chain 2: 3.248 seconds (Total)
#> Chain 2:
#> WARN [2026-10-05 08:10:54] 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 [2026-10-05 08:10:54] 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] 0.84 0.04 0.53 0.09 0.43 0.74 1.16 2.01 221 1.04
#> beta_raw[1] 0.90 0.04 0.54 0.03 0.49 0.81 1.26 2.09 158 1.03
#> alpha[1] 6.01 0.04 0.53 5.26 5.60 5.91 6.33 7.18 221 1.04
#> beta[1] 5.99 0.04 0.54 5.12 5.58 5.90 6.35 7.17 158 1.03
#> lp__ -15.60 0.12 1.54 -19.98 -16.24 -15.17 -14.50 -13.99 156 1.02
#>
#> Samples were drawn using NUTS(diag_e) at Mon Oct 5 08:10:54 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 4.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.47 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 4.3e-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=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 1.61 1.63 1.64 1.66 838 1
#> sigma[1] 0.15 0.00 0.02 0.12 0.14 0.15 0.16 0.18 707 1
#> lp__ -68.94 0.04 0.98 -71.72 -69.31 -68.61 -68.23 -68.01 514 1
#>
#> Samples were drawn using NUTS(diag_e) at Mon Oct 5 08:10:54 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).
# }