R: Loglikelihood adjustment using the sandwich estimator

adjust_loglik {chandwich}

R Documentation

Loglikelihood adjustment using the sandwich estimator

Description

Performs adjustments of a user-supplied independence loglikelihood for the presence of cluster dependence, following Chandler and Bate (2007). The user provides a function that returns a vector of observation-specific loglikelihood contributions and a vector that indicates cluster membership. The loglikelihood of a sub-model can be adjusted by fixing a set of parameters at particular values.

Usage

adjust_loglik(
  loglik = NULL,
  ...,
  cluster = NULL,
  p = NULL,
  init = NULL,
  par_names = NULL,
  fixed_pars = NULL,
  fixed_at = 0,
  name = NULL,
  larger = NULL,
  alg_deriv = NULL,
  alg_hess = NULL,
  mle = NULL,
  H = NULL,
  V = NULL
)

Arguments

`loglik`	A named function. Returns a vector of the loglikelihood contributions of individual observations. The first argument must be the vector of model parameter(s). If any of the model parameters are out-of-bounds then `loglik` should return either `-Inf` or a vector with at least one element equal to `-Inf`. The number of parameters in the full model must be specified using (at least) one of the arguments `p`, `init` or `par_names`.
`...`	Further arguments to be passed either to `loglik` (and to `alg_deriv` and `alg_hess` if these are supplied) or to `optim`. The latter may include `gr`, `method`, `lower`, `upper` or `control`. In the call to `optim`, `hessian = TRUE` will be used regardless of any value supplied. The function `loglik` must not have arguments with names that match any of these arguments to `optim`.
`cluster`	A vector or factor indicating from which cluster the respective loglikelihood contributions from `loglik` originate. Must have the same length as the vector returned by `loglik`. If `cluster` is not supplied then it is set inside `adjust_loglik` under the assumption that each observation forms its own cluster.
`p`	A numeric scalar. The dimension of the full parameter vector, i.e. the number of parameters in the full model. Must be consistent with the lengths of `init` and `par_names`, if these are also supplied.
`init`	A numeric vector of initial values. Must have length equal to the number of parameters in the full model. If `init` is supplied then `p` is set to `length(init)`, provided that this is consistent with the the value given by `p` or implied by `length(par_names)`. If `fixed_pars` is not `NULL` then `init[-fixed_pars]` is used in the search for the MLE. If `init` is not supplied then `rep(0.1, p)` is used.
`par_names`	A character vector. Names of the `p` parameters in the full model. Must be consistent with the lengths of `init` and `p`, if these are also supplied.
`fixed_pars`	A vector specifying which parameters are to be restricted to be equal to the value(s) in `fixed_at`. 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` or stored in the object `larger`.
`fixed_at`	A numeric vector of length 1 or `length(fixed_pars)`. If `length(fixed_at) = 1` then the components `fixed_pars` of the parameter vector are all fixed at `fixed_at`. If `length(fixed_at) = length(fixed_pars)` then the component `fixed_pars[i]` is fixed at `fixed_at[i]` for each `i`.
`name`	A character scalar. A name for the model that gives rise to `loglik`. If this is not supplied then the name in `larger` is used, if this has been supplied, and the name of the function `loglik` otherwise.
`larger`	Only relevant if `fixed_pars` is not `NULL`. If `larger` is supplied but `fixed_pars` is not then an error will result. if `larger` is supplied then information about the model in `larger`, e.g. about `p` and `par_names` will override any attempt to set these arguments in the call to `adjust_loglik`. An object of class `"chandwich"` returned by `adjust_loglik`, corresponding to a model in which the smaller model implied by `fixed_pars` is nested. If `larger` is supplied then all the arguments to `adjust_loglik` apart from `fixed_pars` and `fixed_at` are extracted from `larger`. If `init` is not supplied in the current call to `adjust_loglik` then `init` is set to `attr(larger, "MLE")`, with the elements in `fixed_pars` set to `fixed_at`.
`alg_deriv`	A function with the vector of model parameter(s) as its first argument. Returns a `length(cluster)` by `p` numeric matrix. Column i contains the derivatives of each of the loglikelihood contributions in `loglik` with respect to model parameter i.
`alg_hess`	A function with the vector of model parameter(s) as its first argument. Returns a `p` by `p` numeric matrix equal to the Hessian of `loglik`, i.e. the matrix of second derivatives of the function `loglik`. Supplying both `V` and `alg_deriv` or both `H` and `alg_hess` will produce an error.
`mle`	A numeric vector. Can only be used if `fixed_pars = NULL`. Provides the maximum likelihood estimate of the model parameters, that is, the value of the parameter vector at which the independence loglikelihood `loglik` is maximized. Must have length equal to the number of parameters in the full model. If `mle` is supplied then `p` is set to `length(mle)`, provided that this is consistent with the the value given by `p` or implied by `length(par_names)`. If `mle` is supplied then it overrides `init`.
`H`, `V`	p by p numeric matrices. Only used if `mle` is supplied. Provide estimates of the Hessian of the independence loglikelihood (H) and the variance of the vector of cluster-specific contributions to the score vector (first derivatives with respect to the parameters) of the independence loglikelihood, each evaluated at the MLE `mle`. See the Introducing chandwich vignette and/or Chandler and Bate (2007). Supplying both `V` and `alg_deriv` or both `H` and `alg_hess` will produce an error.

Details

Three adjustments to the independence loglikelihood described in Chandler and Bate (2007) are available. The vertical adjustment is described in Section 6 and two horizontal adjustments are described in Sections 3.2 to 3.4. See the descriptions of type and, for the horizontal adjustments, the descriptions of C_cholesky and C_spectral, in Value.

The adjustments involve first and second derivatives of the loglikelihood with respect to the model parameters. These are estimated using jacobian and optimHess unless alg_deriv and/or alg_hess are supplied.

Value

A function of class "chandwich" to evaluate an adjusted loglikelihood, or the independence loglikelihood, at one or more sets of model parameters, with arguments

x

A numeric vector or matrix giving values of the p_current (see below) parameters in the model to which the returned adjusted loglikelihood applies. If p_current = 1 this may be a numeric vector or a matrix with 1 column. If p_current > 1 this may be a numeric vector of length p (one set of model parameters) or a numeric matrix with p columns (nrow(x) sets of model parameters), one set in each row of x.

type

A character scalar. The type of adjustment to use. One of "vertical", "cholesky", "spectral" or "none".

The latter results in the evaluation of the (unadjusted) independence loglikelihood. The function has (additional) attributes

`p_full`, `p_current`	The number of parameters in the full model and current models, respectively.
`free_pars`	A numeric vector giving the indices of the free parameters in the current model, with names inferred from `par_names` if this was supplied.
`MLE`, `res_MLE`	Numeric vectors, with names inferred from `par_names` if this was supplied. Maximum likelihood estimates of free parameters under the current model (`mle`) and all parameters in the full model, including any parameters with fixed values (`res_MLE`).
`SE`, `adjSE`	The unadjusted and adjusted estimated standard errors, of the free parameters, respectively.
`VC`, `adjVC`	The unadjusted and adjusted estimated variance-covariance matrix of the free parameters, respectively.
`HI`, `HA`	The Hessians of the independence and adjusted loglikelihood, respectively.
`C_cholesky`, `C_spectral`	The matrix C in equation (14) of Chandler and Bate (2007), calculated using Cholesky decomposition and spectral decomposition, respectively.
`full_par_names`, `par_names`	The names of the parameters in the full and current models, respectively, if these were supplied in this call or a previous call.
`max_loglik`	The common maximised value of the independence and adjusted loglikelihoods.
`loglik`, `cluster`	The arguments `loglik` and `cluster` supplied in this call, or a previous call.
`loglik_args`	A list containing the further arguments passed to `loglik` via ... in this call, or a previous call.
`loglikVecMLE`	a vector containing the contributions of individual observations to the independence log-likelihood evaluated at the MLE.
`name`	The argument `name`, or the name of the function `loglik` if `name` isn't supplied.
`nobs`	The number of observations.
`call`	The call to `adjust_loglik`.

If fixed_pars is not NULL then there are further attributes

`fixed_pars`	The argument `fixed_pars`, with names inferred from `par_names` if this was supplied.
`fixed_at`	The argument `fixed_at`, with names inferred from `par_names` if this was supplied.

If alg_deriv and/or alg_hess were supplied then these are returned as further attributes.

To view an individual attribute called att_name use attr(x, "att_name") or attributes(x)$att_name.

References

Chandler, R. E. and Bate, S. (2007). Inference for clustered data using the independence loglikelihood. Biometrika, 94(1), 167-183. doi:10.1093/biomet/asm015

Examples

# ------------------------- Binomial model, rats data ----------------------

# Contributions to the independence loglikelihood
binom_loglik <- function(prob, data) {
  if (prob < 0 || prob > 1) {
    return(-Inf)
  }
  return(dbinom(data[, "y"], data[, "n"], prob, log = TRUE))
}
rat_res <- adjust_loglik(loglik = binom_loglik, data = rats, par_names = "p")

# Plot the loglikelihoods
plot(rat_res, type = 1:4, legend_pos = "bottom", lwd = 2, col = 1:4)
# MLE, SEs and adjusted SEs
summary(rat_res)

# -------------------------- GEV model, owtemps data -----------------------
# ------------ following Section 5.2 of Chandler and Bate (2007) -----------

# Contributions to the independence loglikelihood
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 (isTRUE(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 (isTRUE(any(check <= 0))) return(-Inf)
  check <- 1 + w_pars[3] * (w_data - w_pars[1]) / w_pars[2]
  if (isTRUE(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)

# Loglikelihood adjustment for 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)
# Rows 1, 3 and 4 of Table 2 of Chandler and Bate (2007)
t(summary(large))

# Loglikelihood adjustment of some smaller models: xi[1] = 0 etc

# Starting from a larger model
medium <- adjust_loglik(larger = large, fixed_pars = "xi[1]")
small <- adjust_loglik(larger = large, fixed_pars = c("sigma[1]", "xi[1]"))
small <- adjust_loglik(larger = medium, fixed_pars = c("sigma[1]", "xi[1]"))

# Starting from scratch
medium <- adjust_loglik(gev_loglik, data = owtemps, init = init,
          par_names = par_names, fixed_pars = "xi[1]")
small <- adjust_loglik(gev_loglik, data = owtemps, init = init,
         par_names = par_names, fixed_pars = c("sigma[1]", "xi[1]"))

# --------- Misspecified Poisson model for negative binomial data ----------

# ... following Section 5.1 of the "Object-Oriented Computation of Sandwich
# Estimators" vignette of the sandwich package
# https://cran.r-project.org/web/packages/sandwich/vignettes/sandwich-OOP.pdf

# Simulate data
set.seed(123)
x <- rnorm(250)
y <- rnbinom(250, mu = exp(1 + x), size = 1)
# Fit misspecified Poisson model
fm_pois <- glm(y ~ x + I(x^2), family = poisson)
summary(fm_pois)$coefficients

# Contributions to the independence loglikelihood
pois_glm_loglik <- function(pars, y, x) {
  log_mu <- pars[1] + pars[2] * x + pars[3] * x ^ 2
  return(dpois(y, lambda = exp(log_mu), log = TRUE))
}
pars <- c("alpha", "beta", "gamma")
pois_quad <- adjust_loglik(pois_glm_loglik, y = y, x = x, par_names = pars)
summary(pois_quad)

# Providing algebraic derivatives and Hessian
pois_alg_deriv <- function(pars, y, x) {
  mu <- exp(pars[1] + pars[2] * x + pars[3] * x ^ 2)
  return(cbind(y - mu, x * (y - mu), x ^2 * (y - mu)))
}
pois_alg_hess <- function(pars, y, x) {
  mu <- exp(pars[1] + pars[2] * x + pars[3] * x ^ 2)
  alg_hess <- matrix(0, 3, 3)
  alg_hess[1, ] <- -c(sum(mu), sum(x * mu), sum(x ^ 2 * mu))
  alg_hess[2, ] <- -c(sum(x * mu), sum(x ^ 2 * mu), sum(x ^ 3 * mu))
  alg_hess[3, ] <- -c(sum(x ^ 2 * mu), sum(x ^ 3 * mu), sum(x ^ 4 * mu))
  return(alg_hess)
}
pois_quad <- adjust_loglik(pois_glm_loglik, y = y, x = x, p = 3,
                           alg_deriv = pois_alg_deriv, alg_hess = pois_alg_hess)
summary(pois_quad)

got_sandwich <- requireNamespace("sandwich", quietly = TRUE)
if (got_sandwich) {
  # Providing MLE, H and V
  # H and V calculated using bread() and meat() from sandwich package
  n_obs <- stats::nobs(fm_pois)
  pois_quad <- adjust_loglik(pois_glm_loglik, y = y, x = x, p = 3,
                             mle = fm_pois$coefficients,
                             H = -solve(sandwich::bread(fm_pois) / n_obs),
                             V = sandwich::meat(fm_pois) * n_obs)
}

[Package chandwich version 1.1.6 Index]