R/nspmix-package.R

In nspmix: Nonparametric and Semiparametric Mixture Estimation

##' Beta-blockers Data
##' 
##' Contains the data of the 22-center clinical trial of beta-blockers for
##' reducing mortality after myocardial infarction.
##' 
##' 
##' @name betablockers
##' @docType data
##' @format A numeric matrix with four columns:
##' 
##' center: center identification code.
##' 
##' deaths: the number of deaths in the center.
##' 
##' total: the number of patients taking beta-blockers in the center.
##' 
##' treatment: 0 for control, and 1 for treatment.
##' @seealso \code{\link{mlogit}},\code{\link{cnmms}}.
##' @references
##' 
##' Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric
##' mixture models. \emph{Statistics and Computing}, \bold{20}, 75-86.
##' @source
##' 
##' Aitkin, M. (1999). A general maximum likelihood analysis of variance
##' components in generalized linear models. \emph{Biometrics}, \bold{55},
##' 117-128.
##' @examples
##' 
##' 
##' data(betablockers)
##' x = mlogit(betablockers)
##' cnmms(x)
##'
##'
NULL





##' Z-values of BRCA Data
##' 
##' 
##' Contains 3226 \eqn{z}-values computed by Efron (2004) from the data obtained
##' in a well-known microarray experiment concerning two types of genetic
##' mutations causing increased breast cancer risk, BRCA1 and BRCA2.
##' 
##' 
##' @name brca
##' @docType data
##' @format A numeric vector containing 3226 \eqn{z}-values.
##' @seealso \code{\link{npnorm}},\code{\link{cnm}}.
##' @references
##' 
##' Efron, B. (2004). Large-scale simultaneous hypothesis testing: the choice of
##' a null hypothesis. \emph{Journal of the American Statistical Association},
##' \bold{99}, 96-104.
##' 
##' Wang, Y. (2007). On fast computation of the non-parametric maximum
##' likelihood estimate of a mixing distribution. \emph{Journal of the Royal
##' Statistical Society, Ser. B}, \bold{69}, 185-198.
##' 
##' Wang, Y. and C.-S. Chee (2012). Density estimation using nonparametric and
##' semiparametric mixtures. \emph{Statistical Modelling: An International
##' Journal}, \bold{12}, 67-92.
##' @examples
##' 
##' 
##' data(brca)
##' x = npnorm(brca)
##' plot(cnm(x), x)
##'
##'
NULL





##' Lung Cancer Data
##' 
##' 
##' Contains the data of 14 studies of the effect of smoking on lung cancer.
##' 
##' 
##' @name lungcancer
##' @docType data
##' @format A numeric matrix with four columns:
##' 
##' study: study identification code.
##' 
##' lungcancer: the number of people diagnosed with lung cancer.
##' 
##' size: the number of people in the study.
##' 
##' smoker: 0 for smoker, and 1 for non-smoker.
##' @seealso \code{\link{mlogit}},\code{\link{cnmms}}.
##' @references
##' 
##' Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric
##' mixture models. \emph{Statistics and Computing}, \bold{20}, 75-86.
##' @source
##' 
##' Booth, J. G. and Hobert, J. P. (1999). Maximizing generalized linear mixed
##' model likelihoods with an automated Monte Carlo EM algorithm. \emph{Journal
##' of the Royal Statistical Society, Ser. B}, \bold{61}, 265-285.
##' @examples
##' 
##' 
##' data(lungcancer)
##' x = mlogit(lungcancer)
##' cnmms(x)
##'
##'
NULL





##' Class 'nspmix'
##' 
##' 
##' Class \code{nspmix} is an object returned by function \code{cnm},
##' \code{cnmms}, \code{cnmpl} or \code{cnmap}.
##' 
##' Function \code{plot.nspmix} plots either the mixture model, if the family of
##' the mixture provides an implementation of the generic \code{plot} function,
##' or the gradient function.
##' 
##' 
##' \code{data} must belong to a mixture family, as specified by its class.
##' 
##' @name plot.nspmix
##' @aliases nspmix plot.nspmix
##' @param x an object of a mixture model class
##' @param data a data set from the mixture model
##' @param type the type of function to be plotted: the probability model of the
##' mixture family (\code{probability}), or the gradient function
##' (\code{gradient}).
##' @param ... arguments passed on to the \code{plot} function called.
##' @author Yong Wang <yongwang@@auckland.ac.nz>
##' @seealso \code{\link[lsei]{nnls}}, \code{\link{cnm}}, \code{\link{cnmms}},
##' \code{\link{cnmpl}}, \code{\link{cnmap}}, \code{\link{npnorm}},
##' \code{\link{nppois}}.
##' @references
##' 
##' Wang, Y. (2007). On fast computation of the non-parametric maximum
##' likelihood estimate of a mixing distribution. \emph{Journal of the Royal
##' Statistical Society, Ser. B}, \bold{69}, 185-198.
##' 
##' Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric
##' mixture models. \emph{Statistics and Computing}, \bold{20}, 75-86
##' @examples
##' 
##' ## Poisson mixture
##' x = rnppois(200, disc(c(1,4), c(0.7,0.3)))
##' r = cnm(x)
##' plot(r, x, "p")
##' plot(r, x, "g")
##' 
##' ## Normal mixture
##' x = rnpnorm(200, mix=disc(c(0,4), c(0.3,0.7)), sd=1)
##' r = cnm(x, init=list(beta=0.5))   # sd = 0.5
##' plot(r, x, "p")
##' plot(r, x, "g")
##' 
##' @usage
##' \method{plot}{nspmix}(x, data, type=c("probability","gradient"), ...)
##' 
##' @export plot.nspmix
NULL





##' Illness Spells and Frequencies of Thai Preschool Children
##' 
##' 
##' Contains the results of a cohort study in north-east Thailand in which 602
##' preschool children participated. For each child, the number of illness
##' spells \eqn{x}, such as fever, cough or running nose, is recorded for all
##' 2-week periods from June 1982 to September 1985. The frequency for each
##' value of \eqn{x} is saved in the data set.
##' 
##' 
##' @name thai
##' @docType data
##' @format A data frame with 24 rows and 2 variables:
##' 
##' x: values of \eqn{x}.
##' 
##' freq: frequencies for each value of \eqn{x}.
##' @seealso \code{\link{nppois}},\code{\link{cnm}}.
##' @references
##' 
##' Wang, Y. (2007). On fast computation of the non-parametric maximum
##' likelihood estimate of a mixing distribution. \emph{Journal of the Royal
##' Statistical Society, Ser. B}, \bold{69}, 185-198.
##' @source
##' 
##' Bohning, D. (2000). \emph{Computer-assisted Analysis of Mixtures and
##' Applications: Meta-analysis, Disease Mapping, and Others}. Boca Raton:
##' Chapman and Hall-CRC.
##' 
##' @examples
##' 
##' 
##' data(thai)
##' x = nppois(thai)
##' plot(cnm(x), x)
##'
##'
NULL





##' Toxoplasmosis Data
##' 
##' 
##' Contains the number of subjects testing positively for toxoplasmosis in 34
##' cities of El Salvador, with various rainfalls.
##' 
##' 
##' @name toxo
##' @docType data
##' @format A numeric matrix with four columns:
##' 
##' city: city identification code.
##' 
##' y: the number of subjects testing positively for toxoplasmosis.
##' 
##' n: the number of subjects tested.
##' 
##' rainfall: the annual rainfall of the city, in meters.
##' @seealso \code{\link{mlogit}},\code{\link{cnmms}}.
##' @references
##' 
##' Efron, B. (1986). Double exponential families and their use in generalized
##' linear regression. \emph{Journal of the American Statistical Association},
##' \bold{81}, 709-721.
##' 
##' Aitkin, M. (1996). A general maximum likelihood analysis of overdispersion
##' in generalised linear models. \emph{Statistics and Computing}, \bold{6},
##' 251-262.
##' 
##' Wang, Y. (2010). Maximum likelihood computation for fitting semiparametric
##' mixture models. \emph{Statistics and Computing}, \bold{20}, 75-86.
##' 
##' @examples
##' 
##' 
##' data(toxo)
##' x = mlogit(toxo)
##' cnmms(x)
##'
##'
NULL