warpDLM: Posterior Inference for warpDLM model with latent structural...

In countSTAR: Flexible Modeling of Count Data

Posterior Inference for warpDLM model with latent structural DLM

Description

This function outputs posterior quantities and forecasts from a univariate warpDLM model. Currently two latent DLM specifications are supported: local level and the local linear trend.

Usage

warpDLM(
  y,
  type = c("level", "trend"),
  transformation = c("np", "identity", "log", "sqrt", "pois", "neg-bin"),
  y_max = Inf,
  R0 = 10,
  nsave = 5000,
  nburn = 5000,
  nskip = 1,
  n.ahead = 1
)

Arguments

y	the count-valued time series
type	the type of latent DLM (must be either level or trend)
transformation	transformation to use for the latent process (default is np); must be one of "identity" (identity transformation) "log" (log transformation) "sqrt" (square root transformation) "np" (nonparametric transformation estimated from empirical CDF) "pois" (transformation for moment-matched marginal Poisson CDF) "neg-bin" (transformation for moment-matched marginal Negative Binomial CDF)
y_max	a fixed and known upper bound for all observations; default is Inf
R0	the variance for the initial state theta_0; default is 10
nsave	number of MCMC iterations to save
nburn	number of MCMC iterations to discard
nskip	number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
n.ahead	number of steps to forecast ahead

Value

A list with the following elements:

• V_post: posterior draws of the observation variance

• W_post: posterior draws of the state update variance(s)

• fc_post: draws from the forecast distribution (of length n.ahead)

• post_pred: draws from the posterior predictive distribution of y

• g_func: transformation function

• g_inv_func: inverse transformation function

• KFAS_mod: the final KFAS model representing the latent DLM

countSTAR documentation built on July 9, 2023, 5:12 p.m.