R/TwoStepCLogit.R

In TwoStepCLogit: Conditional Logistic Regression: A Two-Step Estimation Method

#' Conditional Logistic Regression: A Two-Step Estimation Method
#' 
#' Conditional logistic regression with longitudinal follow up and
#' individual-level random coefficients: A stable and efficient two-step
#' estimation method (see \code{\link{Ts.estim}}).
#' 
#' \tabular{ll}{ 
#' Package: \tab TwoStepCLogit\cr 
#' Type: \tab Package\cr 
#' Version: \tab 1.2.5\cr 
#' Date: \tab 2016-03-19\cr 
#' License: \tab GPL-2\cr 
#' }
#' 
#' @name TwoStepCLogit-package
#' @aliases TwoStepCLogit-package TwoStepCLogit
#' @docType package
#' @author Radu V. Craiu, Thierry Duchesne, Daniel Fortin and Sophie
#' Baillargeon
#' 
#' Maintainer: Thierry Duchesne <thierry.duchesne@@mat.ulaval.ca>
#' @references Craiu, R.V., Duchesne, T., Fortin, D. and Baillargeon, S.
#' (2011), Conditional Logistic Regression with Longitudinal Follow-up and
#' Individual-Level Random Coefficients: A Stable and Efficient Two-Step
#' Estimation Method, \emph{Journal of Computational and Graphical Statistics}. \bold{20}(3), 767-784.
#' @keywords package
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#' Bison Dataset
#' 
#' Bison data collected in Prince Albert National Park, Saskatchewan, Canada 
#' (Craiu et al. 2011).
#' 
#' This data set was collected in order to study habitat selection by groups
#' of free-ranging bison. For each observed group, two individuals (dyad) equipped with GPS 
#' radio-collars were followed simultaneously. A cluster is defined here as a pair of bison. 
#' This data set contains 20 clusters. The number of strata per cluster varies
#' between 13 and 345 for a total of 1410 strata. A stratum is composed of two visited GPS
#' locations (one for each individual) gathered at the same time, together with 10 
#' random locations (five drawn within 700 m of each of the two focal bison). Therefore,
#' there are 12 observations per stratum, with 2 cases (Y=1) and 10 controls (Y=0).
#' However, due to problems in the data collection, 17 of the 1410 strata have only 6 
#' observations (1 case and 5 controls).
#' 
#' @name bison
#' @docType data
#' @format A data frame with 16818 observations on the following 10 variables.
#' \describe{ 
#'   \item{Cluster}{pair of animals (dyad) ID}
#'   \item{Strata}{stratum ID} 
#'   \item{Y}{response variable: 1 for visited locations, 0 otherwise} 
#'   \item{water}{land cover indicator covariate: 1 for water, 0 otherwise} 
#'   \item{agric}{land cover indicator covariate: 1 for agricultural locations , 0 otherwise} 
#'   \item{forest}{land cover indicator covariate: 1 for forests, 0 otherwise} 
#'   \item{meadow}{land cover indicator covariate: 1 for meadows, 0 otherwise}
#'   \item{biomass}{continuous covariate: above-ground vegetation biomass index measured
#'                  (in \eqn{kg/m^2}) only at locations within meadows, 0 otherwise}
#'   \item{pmeadow}{continuous covariate: the proportion of meadow in a circular plot 
#'                  (700 m in radius) centered at the bison's location}
#' }
#' @references Craiu, R.V., Duchesne, T., Fortin, D. and Baillargeon, S.
#' (2011), Conditional Logistic Regression with Longitudinal Follow-up and
#' Individual-Level Random Coefficients: A Stable and Efficient Two-Step
#' Estimation Method, \emph{Journal of Computational and Graphical Statistics}. \bold{20}(3), 767-784.
#' @keywords datasets
#' @example inst\\tests\\bison_example.R  
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