run_sim2: A Function to fit multistate ABM for simulation 2
In andyhoegh/moveR: Agent-Based Models for Animal Movement

Description Usage Arguments Value

View source: R/run_sim2.R

A Function to fit multistate ABM for simulation 2

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run_sim2(num_mcmc, num_particles, data, m_mu1, m_mu2, sigmasq_m1,
  sigmasq_m2, nu, sigmasq01, sigmasq02, nu_eps, sigmasq0_eps,
  mu_theta_mean, var_theta)

`num_mcmc`	The number of MCMC iterations
`num_particles`	The number of particles to use in particle filter
`data`	num_agents X time_points x 2 array of observed locations
`m_mu1`	Prior values for step size mean for state 1: # mu ~ N(m_mu, sigmasq_m)
`m_mu2`	Prior values for step size mean for state 2: # mu ~ N(m_mu, sigmasq_m)
`sigmasq_m1`	Prior values for step size variance for state 1: # mu ~ N(m_mu, sigmasq_m)
`sigmasq_m2`	Prior values for step size variance for state 2: # mu ~ N(m_mu, sigmasq_m)
`nu`	Prior values for step size standard deviation: sigmasq_u ~ IG(nu/2,sigmasq0 * nu / 2)
`sigmasq01`	Prior values for step size standard deviation for state 1: sigmasq_u ~ IG(nu/2,sigmasq0 * nu / 2)
`sigmasq02`	Prior values for step size standard deviation for state 2: sigmasq_u ~ IG(nu/2,sigmasq0 * nu / 2)
`nu_eps`	Prior values for observation error: # sigmasq_eps ~ IG(nu_eps/2,nu_eps * sigmasq0_eps / 2)
`sigmasq0_eps`	Prior values for observation error: # sigmasq_eps ~ IG(nu_eps/2,nu_eps * sigmasq0_eps / 2)
`mu_theta_mean`	Prior values for projected normal: mu_theta ~ N(mu_theta_mean, var_theta)
`var_theta`	Prior values for projected normal: mu_theta ~ N(mu_theta_mean, var_theta)