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Posterior Inference for warpDLM model with latent structural DLM

Source: R/warpDLM.R
warpDLM.Rd

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