Description Usage Arguments Value Author(s) Examples
View source: R/summarise.lincomb.R
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.
1 | summarise.lincomb(model, columns=NULL, quantiles=0.5, distribution=FALSE)
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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. |
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. |
Duncan Lee
1 | ## See the vignette accompanying this package for an example of its use.
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