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:
#> Chain 1: Gradient evaluation took 4.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.139 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 3.9e-05 seconds
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.14 seconds (Warm-up)
#> Chain 2: 0.08 seconds (Sampling)
#> Chain 2: 0.22 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] 2.29 0.02 0.35 1.69 2.05 2.26 2.50 3.08 324 1.01
#> lp__ -20.55 0.04 0.76 -22.87 -20.71 -20.25 -20.06 -20.01 324 1.01
#>
#> Samples were drawn using NUTS(diag_e) at Tue Aug 12 10:03:31 2025.
#> 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 0.000319 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.19 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: 2.527 seconds (Warm-up)
#> Chain 2: 1.212 seconds (Sampling)
#> Chain 2: 3.739 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] 0.84 0.04 0.54 0.06 0.40 0.76 1.17 2.09 188 1.01
#> beta_raw[1] 0.97 0.03 0.54 0.13 0.54 0.92 1.30 2.14 269 1.00
#> alpha[1] 6.70 0.04 0.54 5.92 6.25 6.62 7.03 7.95 188 1.01
#> beta[1] 6.90 0.03 0.54 6.06 6.47 6.85 7.23 8.07 269 1.00
#> lp__ -11.64 0.09 1.20 -14.66 -12.23 -11.29 -10.73 -10.31 201 1.00
#>
#> Samples were drawn using NUTS(diag_e) at Tue Aug 12 10:03:39 2025.
#> 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 6e-05 seconds
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#> 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 5.1e-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 1.57 1.60 1.61 1.63 1.66 569 1
#> sigma[1] 0.19 0.00 0.02 0.15 0.17 0.19 0.20 0.23 530 1
#> lp__ -82.80 0.05 1.02 -85.43 -83.18 -82.49 -82.10 -81.79 386 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Aug 12 10:03:40 2025.
#> 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).
# }