# plotComb: Display a posterior probability distribution from the comb... In wiqid: Quick and Dirty Estimates for Wildlife Populations

## Description

Plot the posterior probability distribution for a single parameter calculated using the comb method described by Kruschke (2015).

## Usage

 `1` ```plotComb(x, y, credMass = 0.95, plot = TRUE, showMode = FALSE, shadeHDI = NULL, ...) ```

## Arguments

 `x` A vector of equally-spaced possible values for the parameter. The range should cover all values of the parameter with non-negligible probability. (To restrict the range displayed in the plot, use `xlim`.) `y` A vector of probabilities corresponding to the values in `x`. `credMass` the probability mass to include in credible intervals; set to NULL to suppress plotting of credible intervals. `plot` logical: if TRUE, the posterior is plotted. `showMode` logical: if TRUE, the mode is displayed instead of the mean. `shadeHDI` specifies a colour to shade the area under the curve corresponding to the HDI; NULL for no shading. Use`colours()` to see a list of possible colours. `...` additional graphical parameters.

## Details

The function calculates the Highest Density Interval (HDI). A multi-modal distribution may have a disjoint HDI, in which case the ends of each segment are calculated. No interpolation is done, and the end points correspond to values of the parameter in `x`; precision will be determined by the resolution of `x`.

If `plot = TRUE`, the probability density is plotted together with either the mean or the mode and the HDI.

## Value

Returns a matrix with the upper and lower limits of the HDI. If the HDI is disjoint, this matrix will have more than 1 row. It has attributes `credMass` and `height`, giving the height of the probability curve corresponding to the ends of the HDI.

## Author(s)

Mike Meredith

For details of the HDI calculation, see `hdi`.
 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# Generate some data: N <- 0:100 post <- dpois(N, 25) # Do the plots: plotComb(N, post) plotComb(N, post, showMode=TRUE, shadeHDI='pink', xlim=c(10, 50)) # A bimodal distribution: post2 <- (dnorm(N, 28, 8) + dnorm(N, 70, 11)) / 2 plotComb(N, post2, credMass=0.99, shade='pink') plotComb(N, post2, credMass=0.80, shade='grey') ```