Nothing
################################################################################
##
## R package sgee by Gregory Vaughan, Kun Chen, and Jun Yan
## Copyright (C) 2016-2018
##
## This file is part of the R package sgee.
##
## The R package sgee is free software: You can redistribute it and/or
## modify it under the terms of the GNU General Public License as published
## by the Free Software Foundation, either version 3 of the License, or
## any later version (at your option). See the GNU General Public License
## at <http://www.gnu.org/licenses/> for details.
##
## The R package sgee is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
##
#################################################################################' sgee: Stagewise Generalized Estimating Equations
#'
#' Provides functions to perform Boosting / Functional Gradient Descent /
#' Forward Stagewise regression with grouped covariates setting using
#' Generalized Estimating Equations.
#'
#' \tabular{ll}{ Package: \tab sgee\cr Type: \tab Package\cr
#' Version: \tab 0.6-0\cr Date: \tab 2018-01-08\cr License: \tab GPL (>= 3)
#' \cr } sgee provides several stagewise regression approaches
#' that are designed to address variable selection with grouped covariates
#' in the context of
#' Generalized Estimating Equations. Given a response and design matrix
#' stagewise techniques perform a sequence of small learning steps
#' wherein a subset of the covariates are selected as being the
#' most important at that iteration and are then subsequently updated
#' by a small amount, epsilon. different techniques this optimal update
#' in different ways that achieve different structural goals (i.e.
#' groups of covariates are fully included or not).
#'
#' The resulting path can then be analyzed to determine an optimal
#' model along the path of coefficient estimates. The
#' \code{analyzeCoefficientPath} function provides such
#' functionality based on various
#' possible metrics, primarily focused on the Mean Squared Error.
#' Furthermore, the \code{plot.sgee} function can be used to examine the
#' path of coefficient estimates versus the iteration number, or some
#' desired penalty.
#'
#' @name sgee-package
#' @aliases sgee-package sgee
#' @docType package
#' @author Gregory Vaughan [aut, cre],
#' Kun Chen [ctb], Jun Yan [ctb]
#'
#' Maintainer: Gregory Vaughan <gregory.vaughan@uconn.edu>
#' @references Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017). Stagewise
#' Generalized Estimating Equations with Grouped Variables. Biometrics 73,
#' 1332-1342. URL: http://dx.doi.org/10.1111/biom.12669,
#' doi:10.1111/biom.12669.
#'
#' Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017). Efficient
#' interaction selection for clustered data via stagewise generalized
#' estimating equations. Department of Statistics, University of
#' Connecticut. Technical Report.
#'
#' Wolfson, J. (2011). EEBoost: A general method for prediction
#' and variable selection based on estimating equations. Journal of the
#' American Statistical Association 106, 296--305.
#'
#' Tibshirani, R. J. (2015). A general framework for fast stagewise
#' algorithms. Journal of Machine Learning Research 16, 2543--2588.
#'
#' Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2013). A
#' sparse-group lasso. Journal of Computational and Graphical
#' Statistics 22, 231--245.
#'
#' Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements
#' of Statistical Learning: Data Mining, Inference, and Prediction.
#' Springer, New York.
#'
#' Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis
#' using generalized linear models. Biometrika 73, 13--22.
#' @examples
#'
#'
#'
#' #####################
#' ## Generate test data
#' #####################
#'
#' ## Initialize covariate values
#' p <- 50
#' beta <- c(rep(2.4,5),
#' c(1.2, 0, 1.6, 0, .4),
#' rep(0.5,5),
#' rep(0,p-15))
#' groupSize <- 5
#' numGroups <- length(beta)/groupSize
#'
#'
#' generatedData <- genData(numClusters = 50,
#' clusterSize = 4,
#' clusterRho = 0.6,
#' clusterCorstr = "exchangeable",
#' yVariance = 1,
#' xVariance = 1,
#' numGroups = numGroups,
#' groupSize = groupSize,
#' groupRho = 0.3,
#' beta = beta,
#' family = gaussian(),
#' intercept = 0)
#'
#'
#' coefMat1 <- hisee(y = generatedData$y, x = generatedData$x,
#' family = gaussian(),
#' clusterID = generatedData$clusterID,
#' groupID = generatedData$groupID,
#' corstr="exchangeable",
#' control = sgee.control(maxIt = 100, epsilon = 0.2))
#'
#' ## interceptLimit allows for compatibility with older R versions
#' coefMat2 <- bisee(y = generatedData$y, x = generatedData$x,
#' family = gaussian(),
#' clusterID = generatedData$clusterID,
#' groupID = generatedData$groupID,
#' corstr="exchangeable",
#' control = sgee.control(maxIt = 100, epsilon = 0.2,
#' interceptLimit = 10),
#' lambda1 = .5,
#' lambda2 = .5)
#'
#'
#' par(mfrow = c(2,1))
#' plot(coefMat1)
#' plot(coefMat2)
#'
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.