sim.mix: Simulate data from a mixture model detection function

In mmds: Mixture Model Distance Sampling (mmds)

Description

Simulate data from a (line or point transect) mixture model detection function with or without covariates using rejection sampling.

Usage

  sim.mix(pars, mix.terms, n, width, zdim = 0, z = NULL,
    pt = FALSE, showit = FALSE)

Arguments

pars	Parameters of the model to fit. See mmds.pars for details.
mix.terms	Number of mixture components.
n	Number of data to generate.
width	Truncation distance.
zdim	Number of columns of z. Defaults to 0.
z	Covariate data. Defaults to NULL. See details for more information.
pt	Should point transect data be generated? Defaults to FALSE.
showit	Print the acceptance rate. Defaults to FALSE.

Details

This routine uses rejection sampling, so may be rather slow of large sample sizes. Direct sampling will be available soon.

Value

a data.frame with the following columns:

observed	Whether the object was observed, always n 1s. Kept for mmds compatability.
object	Object identifier, numbered 1 to n. Kept for mmds compatability.
distance	Observed distances.
	Then follows as many columns as there are columns as z, named as in z.

Author(s)

David L. Miller

Examples

library(mmds)
set.seed(0)
## simulate some line transect data from a 2 point mixture
sim.dat<-sim.mix(c(-0.223,-1.897,inv.reparam.pi(0.3)),2,100,1)
hist(sim.dat$distance)

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