summarise.lincomb: Compute the posterior distribution for a linear combination...

In duncanplee/CARBayes: Spatial Generalised Linear Mixed Models for Areal Unit Data

Description

This function takes in a CARBayes model object and computes the posterior distribution and posterior quantiles of a linear combination of the covariates from the linear predictor. For example, if a quadratic effect of a covariate on the response was specified, then this function allows you to compute the posterior distribution of the quadratic relationship.

Usage

summarise.lincomb(model, columns=NULL, quantiles=0.5, distribution=FALSE)

Arguments

model	A CARBayes model object from fitting one of the models in this package.
columns	A vector of column numbers stating which columns in the design matrix of covariates the posterior distribution should be computed for.
quantiles	The vector of posterior quantiles required.
distribution	A logical value stating whether the entire posterior distribution should be returned or just the specified quantiles.

Value

quantiles	A 2 dimensional array containing the requied posterior quantiles. Each row relates to a data value, and each column to a different requested quantile.
posterior	A 2 dimensional array containing the requied posterior distribution. Each column relates to a different data value.

Author(s)

Duncan Lee

Examples

## See the vignette accompanying this package for an example of its use.

duncanplee/CARBayes documentation built on Oct. 3, 2021, 4:10 p.m.