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

## 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 http://www.sumsar.net/blog/2014/11/how-to-summarize-a-2d-posterior-using-a-highest-density-ellipse/.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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 Feb. 28, 2021, 1:06 a.m.