estimates: Extract estimates of the fitted MOSAiCS model

Description

Extract estimates from MosaicsFit class object, which is a fitted MOSAiCS model.

Usage

estimates( object, ... )
"estimates"( object )

Arguments

object

Object of class MosaicsFit, which represents a fitted MOSAiCS model obtained using method mosaicsFit.

...

Other parameters to be passed through to generic estimates.

Value

pi0: Mixing proportion of background component.
a: Parameter for background component.
betaEst: Parameter for background component (coefficient estimates).
muEst: Parameter for background component.
b: Parameter for one-signal-component model.
c: Parameter for one-signal-component model.
p1: Parameter for two-signal-component model (mixing proportion of signal components).
b1: Parameter for two-signal-component model (the first signal component).
c1: Parameter for two-signal-component model (the first signal component).
b2: Parameter for two-signal-component model (the second signal component).
c2: Parameter for two-signal-component model (the second signal component).
analysisType: Analysis type. Possible values are "OS" (one-sample analysis), "TS" (two-sample analysis using mappability and GC content), and "IO" (two-sample analysis without using mappability and GC content).

References

Kuan, PF, D Chung, G Pan, JA Thomson, R Stewart, and S Keles (2011), "A Statistical Framework for the Analysis of ChIP-Seq Data", Journal of the American Statistical Association, Vol. 106, pp. 891-903.

Chung, D, Zhang Q, and Keles S (2014), "MOSAiCS-HMM: A model-based approach for detecting regions of histone modifications from ChIP-seq data", Datta S and Nettleton D (eds.), Statistical Analysis of Next Generation Sequencing Data, Springer.

Examples

Run this code

## Not run: 
# library(mosaicsExample)
# data(exampleBinData)
# exampleFit <- mosaicsFit( exampleBinData, analysisType="IO" )
# estimates(exampleFit)
# ## End(Not run)