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Inner part of EM-algorithm for QR with measurement error

Source: R/em.R
em.algo.inner.Rd

Does the heavy-lifting of the EM-algorithm for QR with measurment error

Usage

em.algo.inner(
  formula,
  data,
  betmat,
  tau,
  me_distribution = "gaussian",
  m = 1,
  pi = 1,
  mu = 0,
  sig = 1,
  mcmc_method = "MH",
  mcmc_draws = 200,
  mcmc_burn_in = 100,
  proposal_sd = NULL,
  n_cores = 1,
  verbose = FALSE
)

Arguments

formula

y ~ x

data

a data.frame that contains y and x

betmat

LxK matrix of parameter values with L the number of quantiles and K the dimension of the covariates

tau

An L-vector indicating which quantiles have been estimated

me_distribution

The distribution of the measurement error. "gaussian" is the default and supports a mixture of normals. "laplace" is also supported.

m

The number of components of the mixture distribution for the measurement error

pi

The probability of each mixture component (should have length equal to m)

mu

The mean of each mixture component (should have length equal to m)

sig

The standard deviation of each mixture component (should have length equal to m)

mcmc_method

Simulation step to use in the EM algorithm. Currently only "MH" is supported.

mcmc_draws

Total number of MCMC draws per EM step (default 200)

mcmc_burn_in

Number of MCMC draws to discard as burnin (default 100)

proposal_sd

Standard deviation of the Metropolis-Hastings proposal (random-walk step size). When NULL (default), initialised to the SD of the ME mixture from the starting parameters and updated after each M-step to track the current ME scale: sqrt(sum(pi_k * (sig_k^2 + mu_k^2))). Pass a positive numeric to fix the proposal at that value for all iterations.

n_cores

Number of cores for parallel bootstrap computation (default 1)

verbose

Logical or nonnegative integer. If FALSE (default), suppresses progress output. If TRUE or 1, reports major computational stages, EM iteration numbers, and convergence diagnostics. If 2, also reports qrme-native details such as pi, mu, sigma, tolerance, and finite-mixture summaries. If 3 or larger, also reports raw normalmixEM() output, which uses lambda for mixture probabilities.

Value

A list of QR parameters and parameters for mixture of normals for the measurement error term