bbmle: pop_pred_samp – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

pop_pred_samp

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, 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)

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

Tools for General Maximum Likelihood Estimation

v1.0.23.1

GPL

Authors

Ben Bolker [aut, cre], R Development Core Team [aut], Iago Giné-Vázquez [ctb]

Initial release