bulletcp (version 1.0.0)

runmcmc_cp2: Estimate a posterior distribution of data conditional that there are two grooves.

Description

This function runs a random walk metropolis within Gibbs algorithm to estimate the posterior distribution of the value of the changepoints as well as the parameters fit in each multivariate normal distribution on either side of each changepoint. The covariance matrices are based on the exponential covariance function. This functions assumes equally spaced locations ("x" values in the "data" argument). The distribution to the right of the right most changepoint and to the left of the left most changepoint have means that are a linear function of the distance from the center of the data. The slope is constrained to be negative in the left case and positive in the right case. The models fit to the groove engraved areas are exactly the same as in the one changepoint case. Thus, this algorithm only differs in that there are three segments of data to deal with as opposed to two.

Usage

runmcmc_cp2(data, iter, start.vals, prop_var, cp_prop_var, tol_edge = 50,
  tol_cp = 1000, 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

Starting values for the changepoint algorithm. List with elements "sigma", "l", "cp", "beta", and "intercept." "sigma" and "l" are 3 element vectors where the first element is for the data on the left groove. The second element is for the land engraved area, and the third element is for the right groove. "cp" is the vector of changepoint starting values. "beta" and "intercept" are two element vectors of the slope and intercept for the left and right groove engraved area respectively.

prop_var

A three element list of the proposal variance-covariance matrices for the random walk Metropolis algorithm(s). The first element is for the left groove engraved area. The second element is for the land engraved area, and the third element is for the right engraved area.

cp_prop_var

The proposal variance-covariance matrix for the changepoints.

tol_edge

This parameter controls how close changepoint proposals can be to the edge of the data before getting automatically rejected. For example, a value of 10 means that the changepoint will be automatically rejected if either of the proposal changepoints is within a distance of 10 x-values from either edge.

tol_cp

This parameter controls how close changepoint proposals can be to each other before getting automatically rejected. For example, a value of 10 means that the changepoint will be automatically rejected if either of the proposal changepoints is within a distance of 10 x-values from either each other.

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 containing the sampled parameters, acceptance rates for the Metropolis steps, log likelihood values, and proposal variance for the changepoints.

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 for the changepoints
cp_start_left <- min(fake_groove$x) + 60
cp_start_right <- max(fake_groove$x) - 60

# define starting values
start.vals <- list("sigma" = c(1,1,1),
                "l" = c(10,10,10),
                "cp" = c(cp_start_left, cp_start_right),
                "beta" = c(-2,2),
                "intercept" = c(0,0))

# define proposal variances (not for changepoints)
prop_var <- list(diag(c(1/2,1/2,1/2,1/2)),
              diag(c(1/2,1/2)),
              diag(c(1/2,1/2,1/2,1/2)))

# define proposal variance for changepoints
cp_prop_var <- diag(c(10^2, 10^2))


# run Gibbs MCMC for both the right only and left only GEA models
set.seed(1111)
m2cp <- runmcmc_cp2(data = fake_groove,
                 iter = 500,
                 start.vals = start.vals,
                 prop_var = prop_var,
                 cp_prop_var = cp_prop_var,
                 tol_edge = 50, tol_cp = 1000,
                 warmup = 100,
                 verbose = FALSE)
# }