diagnostic_plot: Check the convergence of a data set computed by 'compute_GVA'

In VBel: Variational Bayes for Fast and Accurate Empirical Likelihood Inference

Check the convergence of a data set computed by compute_GVA

Description

Plots mu and variance in a time series plot to check for convergence of the computed data (i.e. Full-Covariance Gaussian VB Empirical Likelihood Posterior)

Usage

diagnostic_plot(dataList, muList, cList)

Arguments

dataList	Named list of data generated from compute_GVA
muList	Array of indices of mu_arr to plot. (default:all)
cList	Matrix of indices of variance to plot, 2xn matrix, each row is (col,row) of variance matrix

Value

Matrix of variance of C_FC

Examples

# Generate toy variables
seedNum <- 100
set.seed(seedNum)
n       <- 100
p       <- 10
lam0    <- matrix(0, nrow = p)

# Calculate z
mean    <- rep(1, p)
sigStar <- matrix(0.4, p, p) + diag(0.6, p)
z       <- mvtnorm::rmvnorm(n = n-1, mean = mean, sigma = sigStar)

# Calculate intermediate variables
zbar    <- 1/(n-1) * matrix(colSums(z), nrow = p)
sumVal  <- matrix(0, nrow = p, ncol = p)
for (i in 1:p) {
zi      <- matrix(z[i,], nrow = p)
sumVal  <- sumVal + (zi - zbar) %*% matrix(zi - zbar, ncol = p)
}
sigHat  <- 1/(n-2) * sumVal

# Initial values for GVA
mu_0    <- matrix(zbar, p, 1)
C_0     <- 1/sqrt(n) * t(chol(sigHat))

# Define h-function
h       <- function(zi, th) { matrix(zi - th, nrow = 10) }

# Define h-gradient function
delthh  <- function(z, th) { -diag(p) }

# Set other initial values
delth_logpi <- function(theta) {-0.0001 * theta}
elip    <- 10^-5
T       <- 5 # Number of iterations for GVA
T2      <- 5 # Number of iterations for AEL
rho     <- 0.9
a       <- 0.00001

ansGVA <-compute_GVA(mu_0, C_0, h, delthh, delth_logpi, z, lam0, rho, elip, 
a, T, T2, fullCpp = TRUE)

diagnostic_plot(ansGVA)
diagnostic_plot(ansGVA, muList = c(1,4))
diagnostic_plot(ansGVA, cList = matrix(c(1,1, 5,6, 3,3), ncol = 2))

VBel documentation built on June 22, 2024, 10:55 a.m.