CNV.fitModel {CNVtools} | R Documentation |
This is the workhorse function, essentially an R wrapper around a lot of C code. It fits GLM models to the data.
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))
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. |
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.
data |
The input expanded data frame, but with the posterior probabilities estimated. |
status |
A marker of convergence |
Vincent Plagnol vincent.plagnol@cimr.cam.ac.uk and Chris Barnes christopher.barnes@imperial.ac.uk
CNVtest.binary