# pdensityContour: Bivariate Posterior Contour In rmsb: Bayesian Regression Modeling Strategies

 pdensityContour R Documentation

## Bivariate Posterior Contour

### Description

Computes coordinates of a highest density contour containing a given probability volume given a sample from a continuous bivariate distribution, and optionally plots. The default method assumes an elliptical shape, but one can optionally use a kernel density estimator. Code adapted from `embbook::HPDregionplot`. See https://www.sumsar.net/blog/2014/11/how-to-summarize-a-2d-posterior-using-a-highest-density-ellipse/.

### Usage

``````pdensityContour(
x,
y,
method = c("ellipse", "kernel"),
prob = 0.95,
otherprob = c(0.01, 0.1, 0.25, 0.5, 0.75, 0.9),
h = c(1.3 * MASS::bandwidth.nrd(x), 1.3 * MASS::bandwidth.nrd(y)),
n = 70,
pl = FALSE
)
``````

### Arguments

 `x` a numeric vector `y` a numeric vector the same length of x `method` defaults to `'ellipse'`, can be set to `'kernel'` `prob` main probability coverage (the only one for `method='ellipse'`) `otherprob` vector of other probability coverages for `method='kernel'` `h` vector of bandwidths for x and y. See `MASS::kde2d()`. `n` number of grid points in each direction, defaulting to normal reference bandwidth (see `bandwidth.nrd`). `pl` set to `TRUE` to plot contours

### Value

a 2-column matrix with x and y coordinates unless `pl=TRUE` in which case a `ggplot2` graphic is returned

### Author(s)

Ben Bolker and Frank Harrell

rmsb documentation built on Sept. 26, 2023, 5:11 p.m.