https://github.com/cran/RecordLinkage
Tip revision: 91236cabdeb855d9c01a2714a751518c47acdc6b authored by Andreas Borg on 04 March 2019, 14:20:44 UTC
version 0.4-10.2
version 0.4-10.2
Tip revision: 91236ca
mygllm.Rd
\name{mygllm}
\alias{mygllm}
\title{Generalized Log-Linear Fitting}
\description{
Fits a log-linear model for collapsed contingency tables.
}
\usage{
mygllm(y, s, X, maxit = 1000, tol = 1e-05, E = rep(1, length(s)))
}
\arguments{
\item{y}{Vector of observed cell frequencies.}
\item{s}{Scatter matrix. s[i] is the cell in the observed array that
corresponds to cell i in the full array.}
\item{X}{Design matrix.}
\item{maxit}{Maximum number of iterations.}
\item{tol}{Convergence parameter.}
\item{E}{Full contingency table. Should be initialized with either ones or
a priori estimates.}
}
\details{This is an implementation and extension of the algorithm published by
Haber (1984). It also incorporates ideas of David Duffy (see references).
A priori estimates of the full contingency table can be given as
start values by argument \code{E}. This can reduce
execution time significantly.
}
\value{Estimated full contingency table.}
\references{Michael Haber, Algorithm AS 207: Fitting a General Log-Linear
Model, in: Applied Statistics 33 (1984) No. 3, 358--362.
David Duffy: gllm: Generalised log-linear model. R package
version 0.31. see \url{http://www.qimr.edu.au/davidD/#loglin}}
\author{Andreas Borg, Murat Sariyar}
\seealso{\code{\link{emWeights}}, which makes use of log-linear fitting for
weight calculation.}