estimates {mosaics} | R Documentation |
Extract estimates from MosaicsFit
class object, which is a fitted MOSAiCS model.
estimates( object, ... ) ## S4 method for signature 'MosaicsFit' estimates( object )
object |
Object of class |
... |
Other parameters to be passed through to generic |
Returns a list with components:
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). |
Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles
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.
## Not run: library(mosaicsExample) data(exampleBinData) exampleFit <- mosaicsFit( exampleBinData, analysisType="IO" ) estimates(exampleFit) ## End(Not run)