mcmc_mix1_wrapper | R Documentation |
Wrapper of mcmc_mix1
mcmc_mix1_wrapper(
df,
seed,
u_max = 2000L,
log_diff_max = 11,
a_psiu = 0.1,
b_psiu = 0.9,
m_alpha1 = 0,
s_alpha1 = 10,
a_theta1 = 1,
b_theta1 = 1,
m_alpha2 = 0,
s_alpha2 = 10,
positive = FALSE,
iter = 20000L,
thin = 1L,
burn = 10000L,
freq = 100L,
invts = 1,
mc3_or_marg = TRUE,
x_max = 1e+05
)
df |
A data frame with at least two columns, x & count |
seed |
Integer for |
u_max |
Scalar (default 2000), positive integer for the maximum threshold to be passed to |
log_diff_max |
Positive real number, the value such that thresholds with profile posterior density not less than the maximum posterior density - |
a_psiu , b_psiu , m_alpha1 , s_alpha1 , a_theta1 , b_theta1 , m_alpha2 , s_alpha2 |
Scalars, real numbers representing the hyperparameters of the prior distributions for the respective parameters. See details for the specification of the priors. |
positive |
Boolean, is alpha1 positive (TRUE) or unbounded (FALSE)? |
iter |
Positive integer representing the length of the MCMC output |
thin |
Positive integer representing the thinning in the MCMC |
burn |
Non-negative integer representing the burn-in of the MCMC |
freq |
Positive integer representing the frequency of the sampled values being printed |
invts |
Vector of the inverse temperatures for Metropolis-coupled MCMC (if mc3_or_marg = TRUE) or power posterior (if mc3_or_marg = FALSE) |
mc3_or_marg |
Boolean, is Metropolis-coupled MCMC to be used? Ignored if invts = c(1.0) |
x_max |
Scalar (default 100000), positive integer limit for computing the normalising constant |
A list returned by mcmc_mix1
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