Fit the dynamic range model.

Fit the DRM Stan model

Usage

fit_drm(
  .data,
  y_col,
  time_col,
  site_col,
  family = "gamma",
  formula_zero = ~1,
  formula_rec = ~1,
  formula_surv = NULL,
  seed,
  init = "cmdstan_default",
  algorithm = "nuts",
  algo_args = list(),
  ...
)

Arguments

.data

A data frame containing the data for the model.

y_col

A character specifying the name of the column in .data that contains the response variable.

time_col

A character specifying the name of the column in .data that contains the time variable.

site_col

A character specifying the name of the column in .data that contains the site variable.

family

a character specifying the family of the probability distribution assumed for density. The options are:

"gamma" (default): gamma parametrized in terms of its mean;
"lognormal": log-normal parametrized in terms of its mean;
"loglogistic": log-logistic parametrized in terms of its median (usual parametrization);
"lognormal_legacy": log-normal with its usual parametrization;

formula_zero

A formula specifying the model for the zero inflation component. Defaults to ~ 1 (intercept only).

formula_rec

A formula specifying the model for the recruitment component. Defaults to ~ 1 (intercept only).

formula_surv

A formula specifying the model for the survival component. If NULL (the default), no survival component is included.

seed

An integer specifying the random number seed.

init

A scalar specifying the initialization method. The default ("cmdstan_default") lets cmdstan initialize parameters. Other options include: a scalar greater than zero, say x, which initializes all parameters uniformly between -x and x; 0, which initializes all parameters at 0; or "prior", which initializes parameters by sampling from their priors.

algorithm

a character specifying the algorithm used for inference. Default is nuts (the default MCMC in Stan). The remaining options are different flavors of variational bayes algorithms: "vb" (for ADVI), "pathfinder" (for Pathfinder), "laplace" (normal approximation centered at the mode of the posterior) or "optimize" for (penalized) MLEs.

algo_args

a list with arguments for the sampling algorithms. For instance, tol_rel_obj for variational inference.

...

Passed on to the make_data() function used to build the input list for our cmdstanr model.

Value

An object of class adrm which is a list containing the MCMC draws, the model data, the linear predictors formulas, and the (response, time, site) column names. Specifically:

stanfit: The MCMC draws from the fitted model.
data: The data used to fit the model (as a list).
formulas: The formulas used to create design matrices.
cols: Important column names.

Author

lcgodoy

Examples

if (instantiate::stan_cmdstan_exists()) {
  data(sum_fl)
  fit_drm(.data = sum_fl,
          y_col = "y",
          time_col = "year",
          site_col = "patch",
          seed = 2025)$stanfit$summary()
}
#> Running MCMC with 4 sequential chains...
#> 
#> Chain 1 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: Exception: gamma_lpdf: Inverse scale parameter[1] is 0, but must be positive finite! (in '/tmp/Rtmp77Yui3/pkg-lib1aba1e483534/drmr/bin/stan/utils/lpdfs.stan', line 97, column 4, included from
#> Chain 1 '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 2, column 0) (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 312, column 2 to line 315, column 67)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
#> Chain 1 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 1 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 1 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 1 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 1 
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#> Chain 1 finished in 3.3 seconds.
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#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
#> Chain 2 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 2 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 2 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 2 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 2 
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#> Chain 3 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: Exception: gamma_lpdf: Inverse scale parameter[1] is 0, but must be positive finite! (in '/tmp/Rtmp77Yui3/pkg-lib1aba1e483534/drmr/bin/stan/utils/lpdfs.stan', line 97, column 4, included from
#> Chain 3 '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 2, column 0) (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 312, column 2 to line 315, column 67)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3 
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: Exception: gamma_lpdf: Inverse scale parameter[1] is 0, but must be positive finite! (in '/tmp/Rtmp77Yui3/pkg-lib1aba1e483534/drmr/bin/stan/utils/lpdfs.stan', line 97, column 4, included from
#> Chain 3 '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 2, column 0) (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 312, column 2 to line 315, column 67)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3 
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3 
#> Chain 3 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 3 Exception: Exception: gamma_lpdf: Inverse scale parameter[1] is 0, but must be positive finite! (in '/tmp/Rtmp77Yui3/pkg-lib1aba1e483534/drmr/bin/stan/utils/lpdfs.stan', line 97, column 4, included from
#> Chain 3 '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 2, column 0) (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 312, column 2 to line 315, column 67)
#> Chain 3 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 3 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 3 
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#> Chain 3 finished in 4.0 seconds.
#> Chain 4 Iteration:    1 / 2000 [  0%]  (Warmup) 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: Exception: gamma_lpdf: Inverse scale parameter[1] is 0, but must be positive finite! (in '/tmp/Rtmp77Yui3/pkg-lib1aba1e483534/drmr/bin/stan/utils/lpdfs.stan', line 97, column 4, included from
#> Chain 4 '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 2, column 0) (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 312, column 2 to line 315, column 67)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
#> Chain 4 Informational Message: The current Metropolis proposal is about to be rejected because of the following issue:
#> Chain 4 Exception: gamma_lpdf: Random variable is 0, but must be positive finite! (in '/tmp/RtmpJsUOE8/model-27b4495e770c.stan', line 309, column 4 to column 54)
#> Chain 4 If this warning occurs sporadically, such as for highly constrained variable types like covariance matrices, then the sampler is fine,
#> Chain 4 but if this warning occurs often then your model may be either severely ill-conditioned or misspecified.
#> Chain 4 
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#> Chain 4 finished in 2.7 seconds.
#> 
#> All 4 chains finished successfully.
#> Mean chain execution time: 3.6 seconds.
#> Total execution time: 14.6 seconds.
#> 
#> Warning: 137 of 4000 (3.0%) transitions ended with a divergence.
#> See https://mc-stan.org/misc/warnings for details.
#> # A tibble: 1,076 × 10
#>    variable        mean    median     sd    mad       q5      q95  rhat ess_bulk
#>    <chr>          <dbl>     <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <dbl>    <dbl>
#>  1 lp__       -1363.     -1.36e+3 1.46   1.27   -1.37e+3 -1.36e+3  1.01     602.
#>  2 lxi[1]        -2.64   -2.31e+0 1.33   1.15   -5.31e+0 -1.14e+0  1.01     209.
#>  3 phi[1]         0.721   7.18e-1 0.0554 0.0546  6.32e-1  8.15e-1  1.01    1155.
#>  4 beta_r[1]      3.38    3.38e+0 0.0770 0.0777  3.26e+0  3.51e+0  1.01     753.
#>  5 beta_t[1]     -0.180  -2.63e-1 0.400  0.415  -6.96e-1  5.89e-1  1.02     194.
#>  6 xi[1]         -0.124  -9.94e-2 0.102  0.109  -3.20e-1 -4.93e-3  1.01     209.
#>  7 log_rec[1]     3.38    3.38e+0 0.0770 0.0777  3.26e+0  3.51e+0  1.01     753.
#>  8 log_rec[2]     3.38    3.38e+0 0.0770 0.0777  3.26e+0  3.51e+0  1.01     753.
#>  9 log_rec[3]     3.38    3.38e+0 0.0770 0.0777  3.26e+0  3.51e+0  1.01     753.
#> 10 log_rec[4]     3.38    3.38e+0 0.0770 0.0777  3.26e+0  3.51e+0  1.01     753.
#> # ℹ 1,066 more rows
#> # ℹ 1 more variable: ess_tail <dbl>

Usage

Arguments

Value

See also

Author

Examples