bulletcp (version 1.0.0)

runmcmc_cp0: Estimate a posterior distribution of data conditional on zero changepoints.

Description

This function runs a random walk Metropolis algorithm to estimate the posterior distribution of a zero mean multivariate normal distribution with an covariance matrix generated by the exponential covariance function. This functions assumes equally spaced locations ("x" values in the "data" argument).

Usage

runmcmc_cp0(data, iter, start.vals, prop_var, warmup = 500,
  verbose = FALSE)

Arguments

data

Data frame with columns "x" and "y." "x" is a column of the locations of the observed residual values, y.

iter

Number of interations after warmup.

start.vals

List with elements "sigma" and "l" for the standard deviation and length scale which parameterize the covariance matrix.

prop_var

The proposal variance-covariance matrix for the random walk metropolis algorithm.

warmup

The number of initial iterations which serves two purposes: the first is to allow the algorithm to wander to the area of most mass, and the second is to tune the proposal variance.

verbose

Logical value indicating whether to print the iteration number and the parameter proposals.

Value

A named list. "parameters" is a list of named parameter values each of which is a vector of length "iter". "accept" gives the proportion of accepted proposals after warmup. "lp" is a vector of values of the log data pdf at each sampled parameter value.

Examples

# NOT RUN {
# Fake data
sim_groove <- function(beta = c(-0.28,0.28), a = 125)
{
    x <- seq(from = 0, to = 2158, by = 20)
    med <- median(x)
    y <- 1*(x <= a)*(beta[1]*(x - med) - beta[1]*(a - med)) +
    1*(x >= 2158 - a)*(beta[2]*(x - med) - beta[2]*(2158 - a - med))
    return(data.frame("x" = x, "y" = y))
}

fake_groove <- sim_groove()
# define starting values
start.vals <- list("sigma" = c(1), "l" = c(10))

# proposal variance for the MH step
prop_var <- diag(c(1/2,1/2))

set.seed(1111)
m0cp <- runmcmc_cp0(data = fake_groove, iter = 500,
    start.vals = start.vals,
    prop_var = prop_var, warmup = 100, verbose = FALSE)
# }