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 4.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.42 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.145 seconds (Warm-up)
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#> Chain 1: 0.208 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.142 seconds (Warm-up)
#> Chain 2: 0.081 seconds (Sampling)
#> Chain 2: 0.223 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] 3.34 0.04 0.67 2.28 2.87 3.27 3.68 4.90 315 1.00
#> lp__ -8.44 0.04 0.76 -10.54 -8.55 -8.16 -8.00 -7.95 290 1.01
#>
#> Samples were drawn using NUTS(diag_e) at Thu Jun 4 12:39:15 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 0.000314 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.14 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:
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#> Chain 2:
#> Chain 2: Elapsed Time: 2.572 seconds (Warm-up)
#> Chain 2: 1.092 seconds (Sampling)
#> Chain 2: 3.664 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
#> alpha_raw[1] 0.86 0.03 0.53 0.09 0.43 0.79 1.18 2.05 238 1
#> beta_raw[1] 0.92 0.03 0.51 0.14 0.53 0.86 1.24 2.06 327 1
#> alpha[1] 7.10 0.03 0.53 6.33 6.67 7.03 7.43 8.29 238 1
#> beta[1] 6.98 0.03 0.51 6.19 6.59 6.92 7.29 8.12 327 1
#> lp__ -12.33 0.08 1.15 -15.26 -12.94 -12.04 -11.44 -11.02 189 1
#>
#> Samples were drawn using NUTS(diag_e) at Thu Jun 4 12:39:22 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 6.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:
#>
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 5.6e-05 seconds
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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 1.61 1.62 1.63 1.65 529 1
#> sigma[1] 0.14 0.00 0.02 0.11 0.13 0.14 0.15 0.18 681 1
#> lp__ -67.61 0.05 1.08 -70.92 -67.98 -67.26 -66.87 -66.63 543 1
#>
#> Samples were drawn using NUTS(diag_e) at Thu Jun 4 12:39:23 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).
# }