\name{CNV.fitModel} \alias{CNV.fitModel} %- Also NEED an '\alias' for EACH other topic documented here. \title{Fits a mixture of Gaussian to a set of one dimensional points.} \description{ This is the workhorse function, essentially an R wrapper around a lot of C code. It fits GLM models to the data. } \usage{ CNV.fitModel(ncomp, nind, hyp = "H0", data, logit.offset, design.matrix.mean, design.matrix.variance, design.matrix.disease, pi.model = 0, mix.model = 10, control = list(tol = 1e-05, max.iter = 3000, min.freq= 4)) } \arguments{ \item{ncomp}{integer, number of components to fit to the data} \item{nind}{integer, total number of data points} \item{hyp}{Hypothesis, can be either H0 or H1} \item{data}{The data frame containing the data, in an expanded form (one point per individual and copy number)} \item{logit.offset}{An option most users will not use. It sets an offset when fitting the logit model for the disease status. This is used to obtain a profile likelihood when the disease parameter beta varies.} \item{design.matrix.mean}{The design matrix that relate mean cluster locations with batch.copy numbers.} \item{design.matrix.variance}{The design matrix for the cluster variances.} \item{design.matrix.disease}{The design matrix for the disease model.} \item{pi.model}{0,1,2 fit disease, hetero and quantitative models respectively.} \item{mix.model}{Specifies model for the components. } \item{control}{A list of parameters that control the behavior of the fitting.} } \details{ The user is very unlikely to actually use that function which is meant as an internal routine, a wrapper around the C code of the package. This function is called by the more user friendly function CNVtest.binary. } \value{ \item{data}{The input expanded data frame, but with the posterior probabilities estimated.} \item{status}{A marker of convergence} } \author{Vincent Plagnol \email{vincent.plagnol@cimr.cam.ac.uk} and Chris Barnes \email{christopher.barnes@imperial.ac.uk} } \seealso{CNVtest.binary} \keyword{cluster}