sample_tmb {adnuts} | R Documentation |

## Bayesian inference of a TMB model using the no-U-turn sampler.

### Description

Draw Bayesian posterior samples from a Template Model Builder (TMB) model using an MCMC algorithm. This function generates posterior samples from which inference can be made. Adaptation schemes are used so specification tuning parameters are not necessary, and parallel execution reduces overall run time.

### Usage

```
sample_tmb(
obj,
iter = 2000,
init,
chains = 3,
seeds = NULL,
warmup = floor(iter/2),
lower = NULL,
upper = NULL,
thin = 1,
parallel = FALSE,
cores = NULL,
path = NULL,
algorithm = "NUTS",
laplace = FALSE,
control = NULL,
...
)
```

### Arguments

`obj` |
A TMB model object. |

`iter` |
The number of samples to draw. |

`init` |
A list of lists containing the initial parameter
vectors, one for each chain or a function. It is strongly
recommended to initialize multiple chains from dispersed
points. A of NULL signifies to use the starting values
present in the model (i.e., |

`chains` |
The number of chains to run. |

`seeds` |
A vector of seeds, one for each chain. |

`warmup` |
The number of warmup iterations. |

`lower` |
A vector of lower bounds for parameters. Allowed values are -Inf and numeric. |

`upper` |
A vector of upper bounds for parameters. Allowed values are Inf and numeric. |

`thin` |
The thinning rate to apply to samples. Typically not used with NUTS. |

`parallel` |
A deprecated argument, use cores=1 for serial execution or cores>1 for parallel (default is to parallel with cores equal to the available-1) |

`cores` |
The number of cores to use for parallel
execution. Default is number available in the system minus
1. If |

`path` |
Path to model executable. Defaults to working directory. Often best to have model files in a separate subdirectory, particularly for parallel. |

`algorithm` |
The algorithm to use. NUTS is the default and recommended one, but "RWM" for the random walk Metropolis sampler and "HMC" for the static HMC sampler are available. These last two are deprecated but may be of use in some situations. These algorithms require different arguments; see their help files for more information. |

`laplace` |
Whether to use the Laplace approximation if some parameters are declared as random. Default is to turn off this functionality and integrate across all parameters with MCMC. |

`control` |
A list to control the sampler. See details for further use. |

`...` |
Further arguments to be passed to samplers |

### Details

This function implements algorithm 6 of Hoffman and Gelman (2014),
and loosely follows package `rstan`

. The step size can be
adapted or specified manually. The metric (i.e., mass matrix) can be
unit diagonal, adapted diagonal (default and recommended), or a dense
matrix specified by the user. Further control of algorithms can be
specified with the `control`

argument. Elements are:

- adapt_delta
The target acceptance rate.

- metric
The mass metric to use. Options are: "unit" for a unit diagonal matrix; "diag" to estimate a diagonal matrix during warmup; a matrix to be used directly (in untransformed space).

- adapt_engaged
Whether adaptation of step size and metric is turned on.

- max_treedepth
Maximum treedepth for the NUTS algorithm.

- stepsize
The stepsize for the NUTS algorithm. If

`NULL`

it will be adapted during warmup.

### Value

A list containing the samples, and properties of the sampler useful for diagnosing behavior and efficiency.

### Warning

This is deprecated and will cease to exist in future releases

### Author(s)

Cole Monnahan

### See Also

`extract_samples`

to extract samples and
`launch_shinytmb`

to explore the results graphically which
is a wrapper for the `launch_shinystan`

function.

### Examples

```
## Build a fake TMB object with objective & gradient functions and some
## other flags
## Not run:
f <- function(x, order=0){
if(order != 1) # negative log density
-sum(dnorm(x=x, mean=0, sd=1, log=TRUE))
else x # gradient of negative log density
}
init <- function() rnorm(2)
obj <- list(env=list(DLL='demo', last.par.best=c(x=init()), f=f,
beSilent=function() NULL))
## Run NUTS for this object
fit <- sample_tmb(obj, iter=1000, chains=3, init=init)
## Check basic diagnostics
mon <- rstan::monitor(fit$samples, print=FALSE)
Rhat <- mon[,"Rhat"]
max(Rhat)
ess <- mon[, 'n_eff']
min(ess)
## Or do it interactively with ShinyStan
launch_shinytmb(fit)
## End(Not run)
```

*adnuts*version 1.1.2 Index]