sampZpi: Sampling functions for Bayesian analysis of mixture...

In mixAR: Mixture Autoregressive Models

Sampling functions for Bayesian analysis of mixture autoregressive models

Description

Sampling functions for Bayesian analysis of mixture autoregressive models. Draws observations from posterior distributions of the latent variables Z_ts and the parameters of mixture autoregressive models.

Usage

sampZpi(y, pk, prob, mu, AR, sigma, nsim, d)
sampMuShift(y, pk, prec, nk, shift, z, AR, nsim)
sampSigmaTau(y, pk, prec, nk, AR, mu, z, a, c, nsim)

Arguments

y	a time series (currently a numeric vector).
pk	numeric vector of length g. The autoregressive order of each component.
prob	numeric vector of length g. Current mixing weights.
mu	numeric vector of length g. Current mean parameters.
shift	numeric vector of length g. Current shift parameters.
AR	list of g elements. Autoregressive coefficients for each component.
sigma	numeric vector of length g. Current scale parameters
nsim	dessired sample size.
d	numeric vector of length g. Hyperparameters for Dirichlet prior. If missing, a vector of 1s is generated. If length is 1, creates a vector of length g with given number.
prec	numeric vector of length g. Current precision parameters.
nk	output from sampZpi. Component sum of latent variables Z_t.
z	output latentZ from sampZpi. A matrix containing the simulated latent variables.
a, c	hyperparameters.

Details

sampZpi draws observations from posterior distributions of the latent variables Z_ts and mixing weights of a Mixture autoregressive model.

sampSigmaTau draws observations from posterior distributions of the precisions tau_k of a Mixture autoregressive model, and obtains scales sigma_k by transformation.

sampMuShift Draws observations from posterior distributions of the means mu_k of a Mixture autoregressive model, and obtains shifts phi_k0 by transformation.

Value

for sampZpi, a list containing the following elements:

mix_weights	matrix with nrow = nsim and ncol = g: sampled mixing weights.
latentZ	matrix with nrow = n - p and ncol = g, n equal to length(y) and p equal to max(pk: the simulated latent variables Z_t at the last of nsim iterations (functional). Specifically, each row contains 1 for exactly one component, and is filled with 0.
nk	Vector of length g. Column sums of latentZ.

for sampMuShift, a list containing the following elements:

shift	matrix with nrow = nsim and ncol = g: simulated shift parameters, obtained by transformation of the means.
mu	matrix with nrow = nsim and ncol = g: simulated mean parameters.

for sampSigmaTau, a list containing the following elements:

scale	matrix with nrow = nsim and ncol = g: scale parameters, obtained by transformation of precisions.
precision	matrix with nrow = nsim and ncol = g: precision parameters.
lambda	numeric vector of length nsim simulated values of hyperparameter lambda, due to hierarchical setup.

Author(s)

Davide Ravagli

Examples

model <- new("MixARGaussian",
             prob   = exampleModels$WL_At@prob,      # c(0.5, 0.5)
             scale  = exampleModels$WL_At@scale,     # c(1, 2)        
             arcoef = exampleModels$WL_At@arcoef@a ) # list(-0.5, 1.1)
             
prob  <- model@prob
sigma <- model@scale
prec  <- 1 / sigma ^ 2
g     <- length(model@prob)
d     <- rep(1, g)
pk    <- model@arcoef@p
p     <- max(pk)
shift <- mu <- model@shift
         
AR <- model@arcoef@a       

model

set.seed(1234)
n <- 50 # 500
nsim <- 50

y <- mixAR_sim(model, n = n, init = 0)
x  <- sampZpi(y, pk, prob, shift, AR, sigma, nsim = nsim, d)
x1 <- sampMuShift(y, pk, prec, nk = x$nk, shift, z = x$latentZ, AR, nsim = nsim)
x2 <- sampSigmaTau(y, pk, prec, nk = x$nk, AR, mu = x1$mu, z = x$latentZ,
                   a = 0.2, c = 2, nsim = nsim)

mixAR documentation built on May 29, 2024, 6:21 a.m.