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 mle2 object 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.

[Package bbmle version 1.0.25 Index]