cnv.plot {CNVtools} | R Documentation

## Plots posterior probabilty distributions

### Description

Makes formatted density plots from the posterior data frame(s) returned by CNVtest.binary

### Usage

    cnv.plot(posterior, hist.or.dens='histogram', batch = NULL, freq = NULL, ...)

### Arguments

`posterior` | The posterior distribution obtained from the CNVtools fitting algorithm, for example using CNVtest.binary
`hist.or.dens` | Either 'histogram' or 'density' to plot the data as an histogram or using a kernel density estimator
`batch` | character vector (usually of length 1, but not always), designing the batches one wants to plot.
`freq` | This argument is only relevant when hist.or.dens='histogram' (the default). It matches the argument freq of the hist function. With freq = FALSE frequencies, and not raw counts, are shown in the histogram.
`...` | Usual arguments passed to the hist function, including main or breaks for example.

### Author(s)

Vincent Plagnol vincent.plagnol@cimr.cam.ac.uk and Chris Barnes christopher.barnes@imperial.ac.uk

### Examples

    	#Load data for CNV for two control cohorts
    	data(A112)
    	raw.signal <- as.matrix(A112[, -c(1,2)])
    	dimnames(raw.signal)[[1]] <- A112$subject

    	#Extract CNV signal using principal components
    	pca.signal <- apply.pca(raw.signal)

    	#Extract batch, sample and trait information
     	batches <- factor(A112$cohort)
    	sample <- factor(A112$subject)
    	trait <- ifelse( A112$cohort == '58C', 0, 1)

    	#Fit the CNV with a three component model
    	fit.pca <- CNVtest.binary(signal = pca.signal, sample = sample, batch = batches,
    		   	          disease.status = trait, ncomp = 3, n.H0=3, n.H1=3,
    				  model.disease = "~ cn")

    	cnv.plot(fit.pca[['posterior.H0']], batch = '58C', breaks = 30)

[Package CNVtools version 1.74.0 Index]