Accessor for the theta parameter in the hierarchical mixture model

Description

The interpretation of theta depends on whether object is a MarginalModel or a BatchModel. For BatchModel, theta is a matrix of size B x K, where B is the number of batches and K is the number of components. Each column of the theta matrix can be interpreted as the batch means for a particular component. For objects of class MarginalModel (assumes no batch effect), theta is a vector of length K. Each element of theta can be interpreted as the mean for a component. See the following examples for accessing the current value of theta from a MixtureModel-derived object, and for plotting the chain of theta values.

Usage

theta(object)

## S4 method for signature 'BatchModel'
theta(object)

## S4 method for signature 'MarginalModel'
theta(object)

## S4 method for signature 'McmcChains'
theta(object)

Arguments

Value

A vector of length number of components or a matrix of size number of batches x number of components

Examples

## MarginalModel
k(MarginalModelExample)
theta(MarginalModelExample)
plot.ts(theta(chains(MarginalModelExample)))
## BatchModel
k(BatchModelExample)
length(unique(batch(BatchModelExample)))
theta(BatchModelExample)
## Plot means for batches in one component
plot.ts(theta(chains(BatchModelExample))[, 1:3])

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(CNPBayes)
Loading required package: GenomicRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb

Attaching package: 'CNPBayes'

The following object is masked from 'package:stats':

    sigma

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/CNPBayes/theta-method.Rd_%03d_medium.png", width=480, height=480)
> ### Name: theta
> ### Title: Accessor for the theta parameter in the hierarchical mixture
> ###   model
> ### Aliases: theta theta,BatchModel-method theta,MarginalModel-method
> ###   theta,McmcChains-method
> 
> ### ** Examples
> 
> ## MarginalModel
> k(MarginalModelExample)
[1] 3
> theta(MarginalModelExample)
[1] -1.002325098  0.009333966  0.996938101
> plot.ts(theta(chains(MarginalModelExample)))
> ## BatchModel
> k(BatchModelExample)
[1] 3
> length(unique(batch(BatchModelExample)))
[1] 3
> theta(BatchModelExample)
         [,1]      [,2]      [,3]
1 -0.21685023 0.8081400 -1.193065
2  0.01065644 0.9827103 -0.986529
3  0.19187989 1.2026677 -0.809950
> ## Plot means for batches in one component
> plot.ts(theta(chains(BatchModelExample))[, 1:3])
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>