DrBats (version 0.1.3)

visbeta: 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

Examples

require(DrBats)
data("toydata")
data("stanfit")
codafit <- coda.obj(stanfit)
Y <- toydata$Y.simul$Y
N = nrow(Y)
D = toydata$wlu$D
P = ncol(Y)
## PCA in the histogram basis
obs <- toydata$X
times <- toydata$t
pca.data <- pca.Deville(obs, times, t.range = c(min(times), max(times)), breaks = 15)
## Post-processing landmark information
rotation <- toydata$wlu$Q # rotation matrix
real.W <- toydata$wlu$W # PCA-determined latent factors
real.B <- t(pca.data$Cp[, 1:(toydata$wlu$D)]) # PCA-determined scores
mcmc.output <- clean.mcmc(N, P, D, codafit, rotation, real.W, real.B)
beta.res <- visbeta(mcmc.output, Y, 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))