dbDiversityRegression: Simple linear regression on distance-based diversity indices
In stevencarlislewalker/subscript: Subscript

Description Usage Arguments Details Value Note

Simple linear regression on distance-based diversity indices

Find points on a grid within a 100p% highest posterior density region for the tuning parameter, a

dbDiversityProfile(slist, sdist1, sdist2, aGrid, ...)

dbDiversityRegression(slist, sdist1, sdist2, resp, diversities, aGrid, ...)

## S3 method for class 'dbDiversityRegression'
logLik(object, ...)

posterior(object, ...)

## S3 method for class 'dbDiversityRegression'
mean(x, ...)

## S3 method for class 'dbDiversityRegression'
coef(object, ...)

aHpd(object, level = 0.95)

`slist`	A `speciesList` object
`sdist1`	An object that is coercible to `longDist`
`sdist2`	A second object that is coercible to `longDist`
`resp`	A response variable
`diversities`	[optional] Output of `dbDiversityProfile`
`aGrid`	Optional grid of weighting parameters (see `combineDists`)
`...`	Passed to `subscript.longDist`
`object`	A `dbDiversityRegression` object
`x`	`dbDiversityRegression` object
`level`	Size of the highest posterior density region.

The 100p% highest posterior density region for 'a' is the subset of the interval between 0 and 1, which contains 100p% of the probability.

TODO

A data frame with two columns: the values of the grid within the hpd region and the value of the posterior at each point in this grid.

There are two ways to provide diversity inputs: (1) slist, sdist1, and sdist2, or (2) diversities. If the former, dbDiversityProfile is called to compute diversities from slist, sdist1, and sdist2. Also note that the coercion of sdist1 and sdist2 forces normalization of the distances (see norm argument of longDist).