Description Usage Arguments Value References Examples
summary method for class hmclearn
1 2 3 4 5 6 7  | 
object | 
 an object of class   | 
burnin | 
 optional numeric parameter for the number of initial MCMC samples to omit from the summary  | 
probs | 
 quantiles to summarize the posterior distribution  | 
... | 
 additional arguments to pass to   | 
Returns a matrix with posterior quantiles and the posterior scale reduction factor statistic for each parameter.
Gelman, A., et. al. (2013) Bayesian Data Analysis. Chapman and Hall/CRC.
Gelman, A. and Rubin, D. (1992) Inference from Iterative Simulation Using Multiple Sequences. Statistical Science 7(4) 457-472.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  | # Linear regression example
set.seed(521)
X <- cbind(1, matrix(rnorm(300), ncol=3))
betavals <- c(0.5, -1, 2, -3)
y <- X%*%betavals + rnorm(100, sd=.2)
f1 <- hmc(N = 500,
          theta.init = c(rep(0, 4), 1),
          epsilon = 0.01,
          L = 10,
          logPOSTERIOR = linear_posterior,
          glogPOSTERIOR = g_linear_posterior,
          varnames = c(paste0("beta", 0:3), "log_sigma_sq"),
          param=list(y=y, X=X), parallel=FALSE, chains=1)
summary(f1)
 | 
Summary of MCMC simulation
                   2.5%         5%        25%        50%        75%        95%
beta0         0.3761942  0.4648189  0.5155872  0.5325537  0.5497568  0.5781873
beta1        -1.0739748 -1.0488209 -1.0281105 -1.0118904 -0.9965476 -0.9627243
beta2         0.9617633  1.8882191  1.9997614  2.0164956  2.0314843  2.0521531
beta3        -3.0255286 -3.0154147 -2.9970442 -2.9807522 -2.9646554 -2.8305029
log_sigma_sq -3.2893269 -3.2522864 -3.1503169 -3.0392322 -2.9052983 -0.4878351
                  97.5%
beta0         0.5854330
beta1        -0.9602134
beta2         2.0591168
beta3        -1.8103326
log_sigma_sq  1.5343091
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