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)
``` |

`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.
``` |

duncanplee/CARBayes documentation built on June 5, 2017, 3:20 a.m.

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