summary.dsp: Summarize DSP MCMC chains

In dsp: Dynamic Shrinkage Process and Change Point Detection

Summarize DSP MCMC chains

Description

Summarize DSP MCMC chains

Usage

## S3 method for class 'dsp'
summary(object, pars, probs = c(0.025, 0.25, 0.5, 0.75, 0.975), ...)

Arguments

object	object of class dsp from dsp_fit()
pars	parameter names specified for summaries; currently defaults to all parameters named in object$mcmc_output
probs	numeric vector of quantile()s requested for posterior summary of pars. Defaults to c(0.025, 0.25, 0.50, 0.75, 0.975)
...	currently not being used

Value

Returns a named list of the same length as pars where within each element of the list is a numeric matrix (vector parameters) or vector (scalar parameters). For matrices, each row is a time point (or dimension) of the parameter and each column is a named summary. The names are accessible with colnames. For vectors (scalar parameters), each element is a named summary.

Examples

set.seed(200)
signal = c(rep(0, 50), rep(10, 50))
noise = rep(1, 100)
noise_var = rep(1, 100)
for (k in 2:100){
  noise_var[k] = exp(0.9*log(noise_var[k-1]) + rnorm(1, 0, 0.5))
  noise[k] = rnorm(1, 0, sqrt(noise_var[k])) }

y = signal + noise
model_spec = dsp_spec(family = "gaussian", model = "changepoint",
                      D = 1, useAnom = TRUE, obsSV = "SV")
mcmc_output = dsp_fit(y, model_spec = model_spec, nsave = 500, nburn = 500)

summary_fit <- summary(mcmc_output)
summary_fit$mu[,"mean"]
summary_fit$evol_sigma_t2[,"mean"]

dsp documentation built on Aug. 21, 2025, 5:29 p.m.