# Perform a Principal Component Analysis

### Description

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

### Usage

1 |

### 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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
## 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)
``` |

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