chandwich (version 1.1.2)

confint.chandwich: Confidence intervals for model parameters

Description

confint method for objects of class "chandwich". Computes confidence intervals for one or more model parameters based on an object returned from adjust_loglik.

Usage

# S3 method for chandwich
confint(object, parm, level = 0.95,
  type = c("vertical", "cholesky", "spectral", "none"), ...)

Arguments

object

An object of class "chandwich", returned by adjust_loglik.

parm

A vector specifying the (unfixed) parameters for which confidence intervals are required. If missing, all parameters are included.

Can be either a numeric vector, specifying indices of the components of the full parameter vector, or a character vector of parameter names, which must be a subset of those supplied in par_names in the call to adjust_loglik that produced object.

which_pars must not have any parameters in common with attr(object, "fixed_pars"). which_pars must not contain all of the unfixed parameters, i.e. there is no point in profiling over all the unfixed parameters.

level

The confidence level required. A numeric scalar in (0, 1).

type

A character scalar. The argument type to the function returned by adjust_loglik, that is, the type of adjustment made to the independence loglikelihood function.

...

Further arguments to be passed to conf_intervals.

Value

A matrix with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1 - level)/2 and 1 - (1 - level)/2 in % (by default 2.5% and 97.5%). The row names are the names of the model parameters, if these are available.

Details

For details see the documentation for the function conf_intervals, on which confint.chandwich is based.

See Also

conf_intervals.

conf_region for a confidence region for pairs of parameters.

compare_models for an adjusted likelihood ratio test of two models.

adjust_loglik to adjust a user-supplied loglikelihood function.

Examples

# NOT RUN {
# -------------------------- GEV model, owtemps data -----------------------
# ------------ following Section 5.2 of Chandler and Bate (2007) -----------

gev_loglik <- function(pars, data) {
  o_pars <- pars[c(1, 3, 5)] + pars[c(2, 4, 6)]
  w_pars <- pars[c(1, 3, 5)] - pars[c(2, 4, 6)]
  if (o_pars[2] <= 0 | w_pars[2] <= 0) return(-Inf)
  o_data <- data[, "Oxford"]
  w_data <- data[, "Worthing"]
  check <- 1 + o_pars[3] * (o_data - o_pars[1]) / o_pars[2]
  if (any(check <= 0)) return(-Inf)
  check <- 1 + w_pars[3] * (w_data - w_pars[1]) / w_pars[2]
  if (any(check <= 0)) return(-Inf)
  o_loglik <- log_gev(o_data, o_pars[1], o_pars[2], o_pars[3])
  w_loglik <- log_gev(w_data, w_pars[1], w_pars[2], w_pars[3])
  return(o_loglik + w_loglik)
}

# Initial estimates (method of moments for the Gumbel case)
sigma <- as.numeric(sqrt(6 * diag(var(owtemps))) / pi)
mu <- as.numeric(colMeans(owtemps) - 0.57722 * sigma)
init <- c(mean(mu), -diff(mu) / 2, mean(sigma), -diff(sigma) / 2, 0, 0)

# Log-likelihood adjustment of the full model
par_names <- c("mu[0]", "mu[1]", "sigma[0]", "sigma[1]", "xi[0]", "xi[1]")
large <- adjust_loglik(gev_loglik, data = owtemps, init = init,
                       par_names = par_names)
confint(large)
# }