Plot the densities estimated from a mixture model for a copy number polymorphism

Description

Plot estimates of the posterior density for each component and the overall, marginal density. For batch models, one can additionally plot batch-specific density estimates.

Usage

plot(x, y, ...)

## S4 method for signature 'DensityModel,ANY'
plot(x, y, ...)

## S4 method for signature 'MarginalModel,ANY'
plot(x, y, ...)

## S4 method for signature 'BatchModel,ANY'
plot(x, y, show.batch = TRUE, ...)

## S4 method for signature 'DensityBatchModel,ANY'
plot(x, show.batch = TRUE, ...)

Arguments

Value

A plot showing the density estimate

Examples

  set.seed(100)
  truth <- simulateData(N=2500,
                        theta=c(-2, -0.4, 0),
                        sds=c(0.3, 0.15, 0.15),
                        p=c(0.05, 0.1, 0.8))

  mcmcp <- McmcParams(iter=500, burnin=500, thin=2)
  model <- MarginalModel(y(truth), k=3, mcmc.params=mcmcp)
  model <- CNPBayes:::startAtTrueValues(model, truth)
  model <- posteriorSimulation(model)
  par(mfrow=c(1,2), las=1)
  plot(truth)
  plot(model)

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> 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/plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot
> ### Title: Plot the densities estimated from a mixture model for a copy
> ###   number polymorphism
> ### Aliases: plot plot,BatchModel,ANY-method
> ###   plot,DensityBatchModel,ANY-method plot,DensityModel,ANY-method
> ###   plot,DensityModel,numeric-method plot,MarginalModel,ANY-method
> 
> ### ** Examples
> 
>   set.seed(100)
>   truth <- simulateData(N=2500,
+                         theta=c(-2, -0.4, 0),
+                         sds=c(0.3, 0.15, 0.15),
+                         p=c(0.05, 0.1, 0.8))
> 
>   mcmcp <- McmcParams(iter=500, burnin=500, thin=2)
>   model <- MarginalModel(y(truth), k=3, mcmc.params=mcmcp)
>   model <- CNPBayes:::startAtTrueValues(model, truth)
>   model <- posteriorSimulation(model)
>   par(mfrow=c(1,2), las=1)
>   plot(truth)
An object of class 'DensityModel'
   component densities:  list of 3 vectors 
   overall density:  vector of length 250 
   modes:  -2.04, 0 
>   plot(model)
An object of class 'DensityModel'
   component densities:  list of 3 vectors 
   overall density:  vector of length 250 
   modes:  -2.07, 0 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>