prcp.ca: Perform a Principal Component Analysis

In infeR: A pedagogical tool to teach the basics of inference in population genetics

Description

Perform a principal component analysis (PCA) based on the simulated summary statistics, together with those measured on the observed data.

Usage

	prcp.ca(stats,target,...)

Arguments

stats	a data frame containing a set of summary statistics measured on simulated data
target	a data frame containing a set of summary statistics measured on the observed data
...	options for plotting

Details

Perform a principal component analysis (PCA) based on the simulated summary statistics, together with those measured on the observed data.

Value

an object of the class pca dudi, and a graphical representation of the PCA.

Examples

	
## This is to generate a pseudo-observed dataset.

prior <- generate.prior(number.of.simulations = 1,prior.theta = 'UNI',min.theta = 1.0,max.theta = 1.0,prior.M = 'UNI',min.M = 1.0,max.M = 1.0)

target <- sim.island.model(number.of.simulations = 1,mutation.model = 'SMM',total.number.of.demes = 10,number.of.loci = 20,number.of.sampled.demes = 10,sample.sizes = 50)

## This is to generate a prior distribution of the model parameters.

prior <- generate.prior(number.of.simulations = 1e3,prior.theta = 'UNI',min.theta = 0.1, max.theta = 5,prior.M = 'UNI',min.M = 0.1,max.M = 5)

## This is to generate summary statistics from simulated data.

stats <- sim.island.model(number.of.simulations = 1e3,mutation.model = 'SMM',total.number.of.demes = 10,number.of.loci = 20,number.of.sampled.demes = 10,sample.sizes = 50)

## Perform a principal component analysis (PCA) 

prcp.ca(stats,target)

infeR documentation built on May 2, 2019, 4:45 p.m.