Type:	Package
Title:	Variational Bayes for Fast and Accurate Empirical Likelihood Inference
Version:	1.1.0
Date:	2024-12-05
Description:	Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.
License:	GPL (≥ 3)
Imports:	Rcpp (≥ 1.0.12), stats
LinkingTo:	Rcpp, RcppEigen
Encoding:	UTF-8
RoxygenNote:	7.3.2
URL:	https://github.com/jlimrasc/VBel
BugReports:	https://github.com/jlimrasc/VBel/issues
Suggests:	mvtnorm, testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2024-12-05 02:29:50 UTC; jerem
Author:	Weichang Yu [aut], Jeremy Lim [cre, aut]
Maintainer:	Jeremy Lim <jeremy.lim@unimelb.edu.au>
Repository:	CRAN
Date/Publication:	2024-12-05 05:20:02 UTC

Variational Bayes for Fast and Accurate Empirical Likelihood Inference

Description

Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.

Details

The DESCRIPTION file:

Index of help topics:

VBel-package            Variational Bayes for Fast and Accurate
                        Empirical Likelihood Inference
compute_AEL             Compute the Adjusted Empirical Likelihood
compute_GVA             Compute the Full-Covariance Gaussian VB
                        Empirical Likelihood Posterior
diagnostic_plot         Check the convergence of a data set computed by
                        'compute_GVA'

Author(s)

Weichang Yu [aut] (<https://orcid.org/0000-0002-0399-3779>), Jeremy Lim [cre, aut]

Maintainer: Jeremy Lim <jeremy.lim@unimelb.edu.au>

References

https://www.tandfonline.com/doi/abs/10.1080/01621459.2023.2169701

See Also

compute_AEL() for choice of R and/or C++ computation of AEL

compute_GVA() for choice of R and/or C++ computation of GVA

diagnostic_plot() for verifying convergence of computed GVA data

Examples

#ansGVARcppPure <- compute_GVA(mu, C_0, h, delthh, delth_logpi, z, lam0, rho, 
#elip, a, iters, iters2, fullCpp = TRUE)
#diagnostic_plot(ansGVARcppPure)

Compute the Adjusted Empirical Likelihood

Description

Evaluates the Log-Adjusted Empirical Likelihood (AEL) (Chen, Variyath, and Abraham 2008) for a given data set, moment conditions and parameter values. The AEL function is formulated as

\log \text{AEL}(\boldsymbol{\theta}) = \max_{\mathbf{w}'} \sum\limits_{i=1}^{n+1} \log(w_i'),

where \mathbf{z}_{n+1} is a pseudo-observation that satisfies

h(\mathbf{z}_{n+1}, \boldsymbol{\theta}) = -\frac{a_n}{n} \sum\limits_{i=1}^n h(\mathbf{z}_i, \boldsymbol{\theta})

for some constant a_n > 0 that may (but not necessarily) depend on n, and \mathbf{w}' = (w_1', \ldots, w_n', w_{n+1}') is a vector of probability weights that define a discrete distribution on \{\mathbf{z}_1, \ldots, \mathbf{z}_n, \mathbf{z}_{n+1}\}, and are subject to the constraints

\sum\limits_{i=1}^{n+1} w_i' h(\mathbf{z}_i, \boldsymbol{\theta}) = 0, \quad \text{and} \quad \sum\limits_{i=1}^{n+1} w_i' = 1.

Here, the maximizer \tilde{\mathbf{w}} is of the form

\tilde{w}_i = \frac{1}{n+1} \frac{1}{1 + \lambda_{\text{AEL}}^\top h(\mathbf{z}_i, \boldsymbol{\theta})},

where \lambda_{\text{AEL}} satisfies the constraints

\frac{1}{n+1} \sum\limits_{i=1}^{n+1} \frac{h(\mathbf{z}_i, \boldsymbol{\theta})}{1 + \lambda_{\text{AEL}}^\top h(\mathbf{z}_i, \boldsymbol{\theta})} = 0, \quad \text{and} \quad \frac{1}{n+1} \sum\limits_{i=1}^{n+1} \frac{1}{1 + \lambda_{\text{AEL}}^\top h(\mathbf{z}_i, \boldsymbol{\theta})} = 1.

Usage

compute_AEL(th, h, lam0, a, z, iters = 500, returnH = FALSE)

Arguments

Details

Note that theta (th) is a p-dimensional vector, h is a K-dimensional vector and K \geq p

Value

A numeric value for the Adjusted Empirical Likelihood function computed evaluated at a given theta value

Author(s)

Weichang Yu, Jeremy Lim

References

Chen, J., Variyath, A. M., and Abraham, B. (2008), “Adjusted Empirical Likelihood and its Properties,” Journal of Computational and Graphical Statistics, 17, 426–443. Pages 2,3,4,5,6,7 doi:10.1198/106186008X321068

Examples

# Generating 30 data points from a simple linear-regression model
set.seed(1)
x     <- runif(30, min = -5, max = 5)
vari  <- rnorm(30, mean = 0, sd = 1)
y     <- 0.75 - x + vari
z <- cbind(x, y)

lam0  <- matrix(c(0,0), nrow = 2)
th    <- matrix(c(0.8277, -1.0050), nrow = 2)

# Specify AEL constant and Newton-Rhapson iteration
a     <- 0.00001
iters <- 10

# Specify moment condition functions for linear regression
h <- function(z, th) {
    xi      <- z[1]
    yi      <- z[2]
    h_zith  <- c(yi - th[1] - th[2] * xi, xi*(yi - th[1] - th[2] * xi))
    matrix(h_zith, nrow = 2)
}
result <- compute_AEL(th, h, lam0, a, z, iters)

Compute the Full-Covariance Gaussian VB Empirical Likelihood Posterior

Description

Requires a given data set, moment conditions and parameter values and returns a list of the final mean and variance-covariance along with an array of the in-between calculations at each iteration for analysis of convergence

Usage

compute_GVA(
  mu0,
  C0,
  h,
  delthh,
  delth_logpi,
  z,
  lam0,
  rho,
  epsil,
  a,
  SDG_iters = 10000,
  AEL_iters = 500,
  verbosity = 500
)

Arguments

Value

A list containing:

1. mu_FC: VB Posterior Mean at final iteration. A vector of size p x 1

2. C_FC: VB Posterior Variance-Covariance (Cholesky) at final iteration. A lower-triangular matrix of size p x p

3. mu_FC_arr: VB Posterior Mean for each iteration. A matrix of size p x (SDG_iters + 1)

4. C_FC_arr: VB Posterior Variance-Covariance (Cholesky) for each iteration. An array of size p x p x (SDG_iters + 1)

Author(s)

Weichang Yu, Jeremy Lim

References

Yu, W., & Bondell, H. D. (2023). Variational Bayes for Fast and Accurate Empirical Likelihood Inference. Journal of the American Statistical Association, 1–13. doi:10.1080/01621459.2023.2169701

Examples

# -----------------------------
# Initialise Inputs
# -----------------------------
# Generating 30 data points from a simple linear-regression model
set.seed(1)
x    <- runif(30, min = -5, max = 5)
vari <- rnorm(30, mean = 0, sd = 1)
y    <- 0.75 - x + vari
lam0 <- matrix(c(0,0), nrow = 2)
z    <- cbind(x, y)

# Specify moment condition functions for linear regression and its corresponding derivative
h    <- function(z, th) {
    xi     <- z[1]
    yi     <- z[2]
    h_zith <- c(yi - th[1] - th[2] * xi, xi*(yi - th[1] - th[2] * xi))
    matrix(h_zith, nrow = 2)
}

delthh <- function(z, th) {
    xi <- z[1]
    matrix(c(-1, -xi, -xi, -xi^2), 2, 2)
}

# Specify derivative of log prior
delth_logpi <- function(theta) { -0.0001 * mu0 }

# Specify AEL constant and Newton-Rhapson iteration
a         <- 0.00001
AEL_iters <- 10

# Specify initial values for GVA mean vector and Cholesky
reslm <- lm(y ~ x)
mu0   <- matrix(unname(reslm$coefficients),2,1)
C0    <- unname(t(chol(vcov(reslm))))

# Specify details for ADADELTA (Stochastic Gradient-Descent)
SDG_iters <- 50
epsil     <- 10^-5
rho       <- 0.9

# -----------------------------
# Main
# -----------------------------
result <-compute_GVA(mu0, C0, h, delthh, delth_logpi, z, lam0, 
rho, epsil, a, SDG_iters, AEL_iters)

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

Value

Matrix of variance of C_FC

Examples

# -----------------------------
# Initialise Inputs
# -----------------------------
# Generating 30 data points from a simple linear-regression model
seedNum <- 100
set.seed(seedNum)
n       <- 100
p       <- 10
lam0    <- matrix(0, nrow = p)
mean    <- rep(1, p)
sigStar <- matrix(0.4, p, p) + diag(0.6, p)
z       <- mvtnorm::rmvnorm(n = n-1, mean = mean, sigma = sigStar)

# Specify moment condition functions for linear regression and its corresponding derivative
h       <- function(zi, th) { matrix(zi - th, nrow = 10) }
delthh  <- function(z, th) { -diag(p) }

# Specify derivative of log prior
delth_logpi <- function(theta) {-0.0001 * theta}

# Specify AEL constant and Newton-Rhapson iteration
AEL_iters <- 5 # Number of iterations for AEL
a         <- 0.00001

# Specify initial values for GVA mean vector and Cholesky
zbar    <- 1/(n-1) * matrix(colSums(z), nrow = p)
mu_0    <- matrix(zbar, p, 1)

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
C_0     <- 1/sqrt(n) * t(chol(sigHat))

# Specify details for ADADELTA (Stochastic Gradient-Descent)
SDG_iters   <- 5 # Number of iterations for GVA
epsil       <- 10^-5
rho         <- 0.9

# -----------------------------
# Main
# -----------------------------
# Compute GVA
ansGVA <-compute_GVA(mu_0, C_0, h, delthh, delth_logpi, z, lam0, rho, epsil, 
a, SDG_iters, AEL_iters)

# Plot graphs
diagnostic_plot(ansGVA) # Plot all graphs
diagnostic_plot(ansGVA, muList = c(1,4)) # Limit number of graphs
diagnostic_plot(ansGVA, cList = matrix(c(1,1, 5,6, 3,3), ncol = 2)) # Limit number of graphs