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Fit an Integer Adjusted Exponential, Gamma or Lognormal distributions

Source: R/estimate_delay.R
dist_fit.Rd

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".

Value

A stan fit of an interval censored distribution

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 0.000124 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.24 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
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#> Chain 1: 
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#> Chain 1:                0.207 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 6.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.142 seconds (Warm-up)
#> Chain 2:                0.08 seconds (Sampling)
#> Chain 2:                0.222 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 Wed Jun 17 08:28:28 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 0.000316 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 3.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
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#> Chain 1:                3.995 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'dist_fit' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000314 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 3.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
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#> Chain 2:                1.078 seconds (Sampling)
#> Chain 2:                3.629 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 Wed Jun 17 08:28:35 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 5.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.58 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 5.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
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#> Chain 2:                0.456 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]      1.62    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 Wed Jun 17 08:28:37 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).
# }