CNV.fitModel {CNVtools} | R Documentation

## 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

`ncomp` | integer, number of components to fit to the data
`nind` | integer, total number of data points
`hyp` | Hypothesis, can be either H0 or H1
`data` | The data frame containing the data, in an expanded form (one point per individual and copy number)
`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.
`design.matrix.mean` | The design matrix that relate mean cluster locations with batch.copy numbers.
`design.matrix.variance` | The design matrix for the cluster variances.
`design.matrix.disease` | The design matrix for the disease model.
`pi.model` | 0,1,2 fit disease, hetero and quantitative models respectively.
`mix.model` | Specifies model for the components.
`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

`data` | The input expanded data frame, but with the posterior probabilities estimated.
`status` | A marker of convergence

### Author(s)

Vincent Plagnol vincent.plagnol@cimr.cam.ac.uk and Chris Barnes christopher.barnes@imperial.ac.uk

### See Also

CNVtest.binary

[Package CNVtools version 1.76.0 Index]