read_hobbs_mean: Read hobbs posterior-mean output

In hobbs: High Dimensional Bayesian Omnibus Sampler

Read hobbs posterior-mean output

Description

For declaration-level save=mean, reads the one-record standard hobbs binary written beside the ordinary chain. The saved field is the number of retained draws used in each posterior mean. The older global hobbs(save = "mean") CSV format remains readable for compatibility.

Usage

read_hobbs_mean(file, param_names = NULL)

Arguments

file	An hobbs_run object or path to a mean output file.
param_names	Optional parameter names. Usually supplied automatically.

Value

A one-row data frame with saved, logp, and posterior mean columns.

Examples

library(hobbs)

set.seed(1)
n <- 200L
p <- 4L
X <- cbind(1, matrix(rnorm(n * (p - 1L)), nrow = n))
beta_true <- c(0.5, 1, -0.75, 0.25)
sigma_true <- 0.75
y <- as.numeric(X %*% beta_true + rnorm(n, sd = sigma_true))

dat <- list(n = n, p = p, X = X, y = y)

model <- '
param beta(p) save=mean;
param logsigma(1);

func llk() {
  double sigma = exp(logsigma(1));
  for (i = 1:n) {
    y(i) ~ dnorm(mu(i), sigma);
  }
}

block beta(j) {
  beta(j) ~ dnorm(0, 10);
  llk();
} cache mu(n) {
  for (i = 1:n) {
    for (k = 1:p) {
      mu(i) += beta(k) * X(i, k);
    }
  }
} update mu(n) {
  for (i = 1:n) {
    mu(i) += (proposal(beta(j)) - current(beta(j))) * X(i, j);
  }
}

block logsigma(1) {
  logsigma(1) ~ dnorm(0, 2);
  llk();
}
'

out <- tempfile("regression", fileext = ".bin")

fit <- hobbs(
    model = model,
    data = dat,
    samples = 2000,
    burnin = 1000,
    seed = 123,
    out = out
)

draws <- read_hobbs(fit)
draws_mean <- read_hobbs_mean(fit)

hobbs documentation built on Oct. 6, 2026, 1:07 a.m.