Extract Model Coefficients

Method for hmclearn objects created by mh and hmc functions. Extracts the specified quantile of the posterior.

# S3 method for hmclearn
coef(object, burnin = NULL, prob = 0.5, ...)

Arguments

object	an object of class hmclearn, usually a result of a call to mh or hmc
burnin	optional numeric parameter for the number of initial MCMC samples to omit from the summary
prob	quantile to extract coefficients
...	additional arguments to pass to quantile

Value

Numeric vector of parameter point estimates based on the given prob, with a default of the median estimate.

Examples

# 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
coef(f1)
#>        beta0        beta1        beta2        beta3 log_sigma_sq 
#>    0.5325537   -1.0118904    2.0164956   -2.9807522   -3.0392322