visbeta: Format scores output for visualization

In DrBats: Data Representation: Bayesian Approach That's Sparse

Format scores output for visualization

Description

Format scores output for visualization

Usage

visbeta(mcmc.output, Y, D, chain = 1, axes = c(1, 2), quant = NULL)

Arguments

mcmc.output	an mcmc list as produced by clean.mcmc
Y	the matrix of data
D	the number of latent factors
chain	the chain to use (default = 1)
axes	the axes to use (default = c(1, 2))
quant	a vector of quantiles to retain (default = NULL)

Value

mean.df are the MCMC estimates for the parmeters

points.df contains all of the estimates of the chain

contour.df contains the exterior points of the convex hull of the cloud of estimates

Author(s)

Gabrielle Weinrott

Examples

data("toydata")
data("stanfit")
codafit <- coda.obj(stanfit) ## convert to mcmc.list
beta.res <- visbeta(codafit, Y = toydata$Y.simul$Y, D = toydata$wlu$D, chain = 1,
axes = c(1, 2), quant = c(0.05, 0.95))

ggplot2::ggplot() +
  ggplot2::geom_path(data = beta.res$contour.df, ggplot2::aes(x = x, y = y, colour = ind)) +
  ggplot2::geom_point(data = beta.res$mean.df, ggplot2::aes(x = x, y = y, colour = ind))

DrBats documentation built on March 18, 2022, 5:15 p.m.