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#' Poisson Regression with Survey Weights
#' @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(api, package="survey")
#' z.out1 <- zelig(enroll ~ api99 + yr.rnd , model = "poisson.survey", data = apistrat)
#' summary(z.out1)
#'
#' @seealso Vignette: \url{http://docs.zeligproject.org/articles/zelig_poissonsurvey.html}
#' @import methods
#' @export Zelig-poisson-gee
#' @exportClass Zelig-poisson-gee
#'
#' @include model-zelig.R
#' @include model-survey.R
#' @include model-poisson.R
zpoissonsurvey <- setRefClass("Zelig-poisson-survey",
contains = c("Zelig-survey", "Zelig-poisson"))
zpoissonsurvey$methods(
initialize = function() {
callSuper()
.self$name <- "poisson-survey"
.self$family <- "poisson"
.self$link <- "log"
.self$linkinv <- eval(call(.self$family, .self$link))$linkinv
.self$category <- "continuous"
.self$description = "Poisson Regression with Survey Weights"
# JSON from parent
.self$wrapper <- "poisson.survey"
}
)
zpoissonsurvey$methods(
qi = function(simparam, mm) {
eta <- simparam %*% t(mm)
theta.local <- matrix(.self$linkinv(eta), nrow = nrow(simparam))
ev <- theta.local
pv <- matrix(NA, nrow = nrow(theta.local), ncol = ncol(theta.local))
for (i in 1:ncol(theta.local))
pv[, i] <- rpois(nrow(theta.local), lambda = theta.local[, i])
return(list(ev = ev, pv = pv))
}
)
zpoissonsurvey$methods(
mcfun = function(x, b0=0, b1=1, ..., sim=TRUE){
lambda <- exp(b0 + b1 * x)
if(sim){
y <- rpois(n=length(x), lambda=lambda)
return(y)
}else{
return(lambda)
}
}
)
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