ggs_pairs {ggmcmc}

Create a plot matrix of posterior simulations

Description

Pairs style plots to evaluate posterior correlations among parameters.

Usage

ggs_pairs(D, family = NA, greek = FALSE, ...)

Arguments

D

Data frame with the simulations.

family

Name of the family of parameters to plot, as given by a character vector or a regular expression. A family of parameters is considered to be any group of parameters with the same name but different numerical value between square brackets (as beta[1], beta[2], etc).

greek

Logical value indicating whether parameter labels have to be parsed to get Greek letters. Defaults to false.

...

Arguments to be passed to ggpairs, including geom's aes (see examples)

Value

A ggpairs object that creates a plot matrix consisting of univariate density plots on the diagonal, correlation estimates in upper triangular elements, and scatterplots in lower triangular elements.

References

Fernández-i-Marín, Xavier (2016) ggmcmc: Analysis of MCMC Samples and Bayesian Inference. Journal of Statistical Software, 70(9), 1-20. doi:10.18637/jss.v070.i09

Examples

## Not run: 
library(GGally)
data(linear)

# default ggpairs plot
ggs_pairs(ggs(s))

# change alpha transparency of points
ggs_pairs(ggs(s), lower=list(continuous = wrap("points", alpha = 0.2)))

# with too many points, try contours instead
ggs_pairs(ggs(s), lower=list(continuous="density"))

# histograms instead of univariate densities on diagonal
ggs_pairs(ggs(s), diag=list(continuous="barDiag"))

# coloring results according to chains
ggs_pairs(ggs(s), mapping = aes(color = Chain))

# custom points on lower panels, black contours on upper panels
ggs_pairs(ggs(s),
  upper=list(continuous = wrap("density", color = "black")),
  lower=list(continuous = wrap("points", alpha = 0.2, shape = 1)))

## End(Not run)