plot: Additional plotting for MCMC visualization and diagnostics.

In bayesGAM: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo

Additional plotting for MCMC visualization and diagnostics.

Description

Marginal response smooth plot functions for parametric and nonparametric associations.

Usage

## S4 method for signature 'bayesGAMfit,missing'
plot(x, y, applylink = TRUE, ...)

## S4 method for signature 'predictPlotObject,missing'
plot(x, y, ...)

## S4 method for signature 'posteriorPredictObject,missing'
plot(x, y, ...)

Arguments

x	an object of class hmclearn, usually a result of a call to mh or hmc
y	unused
applylink	logical to indicate whether the inverse link function should be applied to the plots
...	optional additional arguments to pass to the ggplot2

Value

A list of univariate and bivariate plots generated by plot functions based on ggplot2

References

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

See Also

mcmc_plots

Examples

f <- bayesGAM(weight ~ np(height), data = women, 
              family = gaussian, iter=500, chains = 1)
plot(f)

bayesGAM documentation built on March 18, 2022, 6:29 p.m.