#' Negative Binomial Regression for Event Count Dependent Variables
#'@param formula a symbolic representation of the model to be
#' estimated, in the form \code{y ~ x1 + x2}, where \code{y} is the
#' dependent variable and \code{x1} and \code{x2} are the explanatory
#' variables, and \code{y}, \code{x1}, and \code{x2} are contained in the
#' same dataset. (You may include more than two explanatory variables,
#' of course.) The \code{+} symbol means ``inclusion'' not
#' ``addition.'' You may also include interaction terms and main
#' effects in the form \code{x1*x2} without computing them in prior
#' steps; \code{I(x1*x2)} to include only the interaction term and
#' exclude the main effects; and quadratic terms in the form
#' \code{I(x1^2)}.
#' @param model the name of a statistical model to estimate.
#' For a list of other supported models and their documentation see:
#' \url{http://docs.zeligproject.org/articles/}.
#' @param data the name of a data frame containing the variables
#' referenced in the formula or a list of multiply imputed data frames
#' each having the same variable names and row numbers (created by
#' \code{Amelia} or \code{\link{to_zelig_mi}}).
#' @param ... additional arguments passed to \code{zelig},
#' relevant for the model to be estimated.
#' @param by a factor variable contained in \code{data}. If supplied,
#' \code{zelig} will subset
#' the data frame based on the levels in the \code{by} variable, and
#' estimate a model for each subset. This can save a considerable amount of
#' effort. You may also use \code{by} to run models using MatchIt
#' subclasses.
#' @param cite If is set to 'TRUE' (default), the model citation will be printed
#' to the console.
#'
#' @details
#' Additional parameters avaialable to this model include:
#' \itemize{
#' \item \code{weights}: vector of weight values or a name of a variable in the dataset
#' by which to weight the model. For more information see:
#' \url{http://docs.zeligproject.org/articles/weights.html}.
#' \item \code{bootstrap}: logical or numeric. If \code{FALSE} don't use bootstraps to
#' robustly estimate uncertainty around model parameters due to sampling error.
#' If an integer is supplied, the number of boostraps to run.
#' For more information see:
#' \url{http://docs.zeligproject.org/articles/bootstraps.html}.
#' }
#' @return Depending on the class of model selected, \code{zelig} will return
#' an object with elements including \code{coefficients}, \code{residuals},
#' and \code{formula} which may be summarized using
#' \code{summary(z.out)} or individually extracted using, for example,
#' \code{coef(z.out)}. See
#' \url{http://docs.zeligproject.org/articles/getters.html} for a list of
#' functions to extract model components. You can also extract whole fitted
#' model objects using \code{\link{from_zelig_model}}.
#'
#'@examples
#' library(Zelig)
#' data(sanction)
#' z.out <- zelig(num ~ target + coop, model = "negbin", data = sanction)
#' summary(z.out)
#'
#' @seealso Vignette: \url{http://docs.zeligproject.org/articles/zelig_negbin.html}
#' @import methods
#' @export Zelig-negbin
#' @exportClass Zelig-negbin
#'
#' @include model-zelig.R
znegbin <- setRefClass("Zelig-negbin",
contains = "Zelig",
field = list(simalpha = "list" # ancillary parameters
))
znegbin$methods(
initialize = function() {
callSuper()
.self$fn <- quote(MASS::glm.nb)
.self$name <- "negbin"
.self$authors <- "Kosuke Imai, Gary King, Olivia Lau"
.self$packageauthors <- "William N. Venables, and Brian D. Ripley"
.self$year <- 2008
.self$category <- "count"
.self$description <- "Negative Binomial Regression for Event Count Dependent Variables"
# JSON
.self$outcome <- "discrete"
.self$wrapper <- "negbin"
.self$acceptweights <- TRUE
}
)
znegbin$methods(
zelig = function(formula, data, ..., weights=NULL, by = NULL, bootstrap = FALSE) {
.self$zelig.call <- match.call(expand.dots = TRUE)
.self$model.call <- .self$zelig.call
callSuper(formula=formula, data=data, ..., weights=weights, by = by, bootstrap = bootstrap)
rse <- lapply(.self$zelig.out$z.out, (function(x) vcovHC(x, type = "HC0")))
.self$test.statistics<- list(robust.se = rse)
}
)
znegbin$methods(
param = function(z.out, method="mvn") {
simalpha.local <- z.out$theta
if(identical(method,"mvn")){
simparam.local <- mvrnorm(n = .self$num, mu = coef(z.out),
Sigma = vcov(z.out))
simparam.local <- list(simparam = simparam.local, simalpha = simalpha.local)
return(simparam.local)
} else if(identical(method,"point")){
return(list(simparam = t(as.matrix(coef(z.out))), simalpha = simalpha.local))
}
}
)
znegbin$methods(
qi = function(simparam, mm) {
coeff <- simparam$simparam
alpha <- simparam$simalpha
inverse <- family(.self$zelig.out$z.out[[1]])$linkinv
eta <- coeff %*% t(mm)
theta <- matrix(inverse(eta), nrow=nrow(coeff))
ev <- theta
pv <- matrix(NA, nrow=nrow(theta), ncol=ncol(theta))
#
for (i in 1:ncol(ev))
pv[, i] <- rnegbin(nrow(ev), mu = ev[i, ], theta = alpha[i])
return(list(ev = ev, pv = pv))
}
)
znegbin$methods(
mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){
mu <- exp(b0 + b1 * x)
if(sim){
y <- rnbinom(n=length(x), 1, mu=mu)
return(y)
}else{
return(mu)
}
}
)
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