pop_pred_samp {bbmle} | R Documentation |

## generate population prediction sample from parameters

### Description

This [EXPERIMENTAL] function combines several sampling tricks to compute a version of an importance sample (based on flat priors) for the parameters.

### Usage

```
pop_pred_samp(
object,
n = 1000,
n_imp = n * 10,
return_wts = FALSE,
impsamp = FALSE,
PDify = FALSE,
PDmethod = NULL,
Sigma = vcov(object),
tol = 1e-06,
return_all = FALSE,
rmvnorm_method = c("mvtnorm", "MASS"),
fix_params = NULL,
...
)
```

### Arguments

`object` |
a fitted |

`n` |
number of samples to return |

`n_imp` |
number of total samples from which to draw, if doing importance sampling |

`return_wts` |
return a column giving the weights of the samples, for use in weighted summaries? |

`impsamp` |
subsample values (with replacement) based on their weights? |

`PDify` |
use Gill and King generalized-inverse procedure to correct non-positive-definite variance-covariance matrix if necessary? |

`PDmethod` |
method for fixing non-positive-definite covariance matrices |

`tol` |
tolerance for detecting small eigenvalues |

`return_all` |
return a matrix including all values, and weights (rather than taking a sample) |

`rmvnorm_method` |
package to use for generating MVN samples |

`fix_params` |
parameters to fix (in addition to parameters that were fixed during estimation) |

`Sigma` |
covariance matrix for sampling |

`...` |
additional parameters to pass to the negative log-likelihood function |

### References

Gill, Jeff, and Gary King. "What to Do When Your Hessian Is Not Invertible: Alternatives to Model Respecification in Nonlinear Estimation." Sociological Methods & Research 33, no. 1 (2004): 54-87. Lande, Russ and Steinar Engen and Bernt-Erik Saether, Stochastic Population Dynamics in Ecology and Conservation. Oxford University Press, 2003.

*bbmle*version 1.0.25.1 Index]