Calculate marginal loglikelihood at high density points of a MAR model.
marg_loglik.RdThe function implements the method by Chib1995;textualmixAR and ChibJeliazkov2001;textualmixAR for calculation of the marginal loglikelihood of a mixture autoregressive model. It automatically finds high density values for model parameters, and evaluates the likelihood at such points.
Arguments
- y
a time series (currently a numeric vector).
- model
object of formal class
MixAR, containing initial values for the parameters. Currently available forMixARGaussianobjects only.- tau
tuning parameter for Metropolis-Hasting move to update autoregressive parameters.
- nsim
sample size on which to evaluate highest density values.
- prob_mod
this is currently the output from
Choose_pk: the proportion of times the "best model" was chosen.
Details
nsim is the sample size on which to evaluate highest density
values for each set of parameters. For example, choosing
nsim=1000 results in 1000*(g+3) (1000 iterations for
each autoregressive component, plus 1000 for mean and scale parameters
and mixing weights).
Value
A list containing the following elements:
- marg_loglik
value of the marginal loglikelihood.
- phi_hd
set of highest density autoregressive parameters.
- prec_hd
set of highest density precision parameters.
- mu_hd
set of highest density mean parameters.
- weig_hd
set of highest density mixing weights.
Examples
prob <- c(0.5, 0.5)
sigma <- c(1, 2)
arco <- list(-0.5, 1)
model <- new("MixARGaussian", prob = prob, scale = sigma, arcoef = arco)
set.seed(1234)
y <- mixAR_sim(model, 250, rep(0, max(model@order)), nskip = 100) # data
nsim <- 10 # 50
marg_loglik(y, model, tau = c(.15, .25), nsim = nsim, 0.5)
#> $marg_loglik
#> [1] -541.8047
#>
#> $phi_hd
#> $phi_hd[[1]]
#> [1] -0.4470663
#>
#> $phi_hd[[2]]
#> [1] 0.9891284
#>
#>
#> $prec_hd
#> [1] 0.7083760 0.2737235
#>
#> $mu_hd
#> [1] 0.04563084 -0.76579127
#>
#> $weig_hd
#> [1] 0.4996393 0.5003607
#>