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#-------------------------------------
# vcovCR with defaults
#-------------------------------------
#' Cluster-robust variance-covariance matrix for a glm object.
#'
#' \code{vcovCR} returns a sandwich estimate of the variance-covariance matrix
#' of a set of regression coefficient estimates from an \code{\link{glm}} object.
#'
#' @param cluster Expression or vector indicating which observations belong to
#' the same cluster. Required for \code{glm} objects.
#' @param target Optional matrix or vector describing the working
#' variance-covariance model used to calculate the \code{CR2} and \code{CR4}
#' adjustment matrices. If a vector, the target matrix is assumed to be
#' diagonal. If not specified, the target is taken to be the estimated variance function.
#' @inheritParams vcovCR
#'
#' @return An object of class \code{c("vcovCR","clubSandwich")}, which consists
#' of a matrix of the estimated variance of and covariances between the
#' regression coefficient estimates.
#'
#' @seealso \code{\link{vcovCR}}
#'
#' @examples
#'
#' data(dietox, package = "geepack")
#' dietox$Cu <- as.factor(dietox$Cu)
#' weight_fit <- glm(Weight ~ Cu * poly(Time, 3), data=dietox, family = "quasipoisson")
#' V_CR <- vcovCR(weight_fit, cluster = dietox$Pig, type = "CR2")
#' coef_test(weight_fit, vcov = V_CR, test = "Satterthwaite")
#'
#' @export
vcovCR.glm <- function(obj, cluster, type, target = NULL, inverse_var = NULL, form = "sandwich", ...) {
if (missing(cluster)) stop("You must specify a clustering variable.")
if (is.null(inverse_var)) inverse_var <- is.null(target)
vcov_CR(obj, cluster = cluster, type = type,
target = target, inverse_var = inverse_var, form = form)
}
# coef()
# nobs()
#-----------------------------------------------
# Model matrix
#-----------------------------------------------
model_matrix.glm <- function(obj) {
X <- model.matrix(obj)
eta <- obj$linear.predictors
dmu_deta <- obj$family$mu.eta
d <- dmu_deta(eta)
d * X
}
#-------------------------------------
# residuals
#-------------------------------------
residuals_CS.glm <- function(obj) {
residuals(obj, type = "response")
}
#-----------------------------------------------
# Get (model-based) working variance matrix
#-----------------------------------------------
targetVariance.glm <- function(obj, cluster) {
mu <- fitted.values(obj)
var_fun <- obj$family$variance
v <- var_fun(mu)
w <- weights(obj, type = "prior")
matrix_list(v / w, cluster, "both")
}
#-------------------------------------
# Get weighting matrix
#-------------------------------------
weightMatrix.glm <- function(obj, cluster) {
mu <- fitted.values(obj)
var_fun <- obj$family$variance
v <- var_fun(mu)
w <- weights(obj, type = "prior")
matrix_list(w / v, cluster, "both")
}
#---------------------------------------
# Get bread matrix and scaling constant
#---------------------------------------
# bread.glm() is in sandwich package
v_scale.glm <- function(obj) {
if (substr(obj$family$family, 1, 17) %in% c("poisson", "binomial", "Negative Binomial")) {
dispersion <- 1
} else {
wres <- as.vector(residuals(obj, "working")) * weights(obj, "working")
dispersion <- sum(wres^2)/sum(weights(obj, "working"))
}
as.vector(sum(summary(obj)$df[1:2])) * dispersion
}
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