Create a plot matrix of posterior simulations
Pairs style plots to evaluate posterior correlations among parameters.
ggs_pairs(D, family = NA, greek = FALSE, ...)
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 |
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.
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
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.