ds.mixture: A fitted Mixture Model Detection Function Object

In mmds: Mixture Model Distance Sampling (mmds)

Description

The fitted mixture model detection function object returned by fitmix. Knowledge of most of this is not useful. Use link{summary.ds.mixture} for result summaries.

Details

A ds.mixture object has the following elements:

distance	Vector of distances used in the analysis.
likelihood	Value of the log-likelihood at the maxima.
pars	Parmeter estimates. See mmds.pars for more information.
mix.terms	Number of mixture terms fit.
width	Truncation distance used.
z	List containing the matrix of covariates used. Output from model.matrix.
zdim	Number of columns of z. See mmds.pars for more information.
hessian	Hessian matrix at the maxima.
pt	Logical indicating whether the data were from a point transect survey.
data	Data frame after truncation.
ftype	Type of detection function.
ctrl.options	Options passed to optim.
showit	Debug level.
opt.method	Optimisation method used.
usegrad	Were analytic gradients used?
model.formula	Model formula.
mu	Per-observation effective trip width/effective area of detection.
pa.vec	Vector of per-observation detectabilities.
N	Estimate of N in the covered area (Horvitz-Thompson).
pa	Average detectability.
pars.se	Standard errors of the parameters.
N.se	Standard error of the Horvitz-Thompson estimate of the abundance.
pa.se	Standard error of the average detectability.
aic	AIC of the fitted model.
cvm	Cramer-von Mises GoF test results. List containing: p, the p-value and W, the test statistic.
ks	Kolmogorov-Smirnov test results. List containing: p, the p-value and Dn, the test statistic. See mmds.gof for more information.

Note

ds.mixture objects can be passed to step.ds.mixture to select number of mixture components based on AIC score.

Author(s)

David L. Miller

mmds documentation built on May 2, 2019, 8:55 a.m.