ppc.pibblefit: Visualization of Posterior Predictive Check of fit model

In jsilve24/mongrel: Bayesian Multinomial Logistic Normal Regression

Description

Visualization of Posterior Predictive Check of fit model

Usage

## S3 method for class 'pibblefit'
ppc(m, ...)

Arguments

m	an object of class pibblefit
...	other options passed to ppc (see details)

Details

ppc.pibblefit accepts the following additional arguments:

• "type" type of plot (options "lines", "points", "bounds")

• "iter" number of samples from posterior predictive distribution to plot (currently must be <= m$iter) if type=="lines" default is 50, if type=="ribbon" default is to use all available iterations.

• "from_scratch" should predictions of Y come from fitted Eta or from predictions of Eta from posterior of Lambda? (default: false)

Value

ggplot object

Examples

## Not run: 
fit <- pibble(Y, X)
ppc(fit)

## End(Not run)

jsilve24/mongrel documentation built on Jan. 27, 2022, 9:54 p.m.