visW: Plot the estimates for the latent factors

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

Plot the estimates for the latent factors

Description

Plot the estimates for the latent factors

Usage

visW(mcmc.output, Y, D, chain = 1, factors = c(1, 2))

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 plot (default = 1)
factors	a vector indicating the factors to plot (default = c(1, 2))

Value

res.W a data frame containing the estimates for the factors, and their lower and upper bounds

Inertia the percentage of total inertia captured by each of the factors

Author(s)

Gabrielle Weinrott

Examples

data("toydata")
data("stanfit")
codafit <- coda.obj(stanfit) ## convert to mcmc.list
W.res <- visW(codafit, Y = toydata$Y.simul$Y, D = toydata$wlu$D,
chain = 1, factors = c(1, 2))

## plot the results

data <- data.frame(time = rep(1:9, 2), W.res$res.W)
ggplot2::ggplot() +
  ggplot2::geom_step(data = data, ggplot2::aes(x = time, y = Estimation, colour = Factor)) +
  ggplot2::geom_step(data = data, ggplot2::aes(x = time, y = Lower.est, colour = Factor),
  linetype = "longdash") +
  ggplot2::geom_step(data = data, ggplot2::aes(x = time, y = Upper.est, colour = Factor),
  linetype = "longdash")

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