View source: R/plot_joint_marginal.R

plot_joint_marginal | R Documentation |

For each sampled MCMC generation, the values estimated for a pair of parameters are logged and plotted against one another. Points are color coded by when in the analysis they were sampled, so that users can visually assess mixing.

plot_joint_marginal(parameter1, parameter2, percent.burnin = 0, thinning = 1, param.name1 = deparse(substitute(parameter1)), param.name2 = deparse(substitute(parameter2)))

`parameter1` |
One of the two parameters for which the joint marginal is being plotted. |

`parameter2` |
The other of the two parameters for which the joint marginal is being plotted. |

`percent.burnin` |
The percent of the sampled MCMC generations to be discarded as "burn-in." If the
MCMC is run for 1,000,000 generations, and sampled every 1,000 generations, there
will be 1,000 sampled generations. A |

`thinning` |
The multiple by which the sampled MCMC generations are thinned. A |

`param.name1` |
The name of one of the two parameters for which the joint marginal is being plotted. |

`param.name2` |
The name of the other of the two parameters for which the joint marginal is being plotted. |

Visualizations of the joint marginal distribution allow users to (1) assess how well the MCMC is mixing, and (2) potentially diagnose instances of non-identifiability in the model. Strong linear trends in the joint marginal, or visible "ridges" in the likelihood surface, may be indicative of parameter non-identifiability, in which multiple combinations of values of these two parameters provide equally reasonable fits to the data.

Gideon Bradburd

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