pop_pred_samp
generate population prediction sample from parameters
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
mle2object- 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.