Title: Covariate Dependent Graph Estimation
Version: 1.0.1
Date: 2022-09-16
Language: en-US
BugReports: https://github.com/JacobHelwig/covdepGE/issues
URL: https://github.com/JacobHelwig/covdepGE
Description: A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.
License: GPL (≥ 3)
Encoding: UTF-8
RoxygenNote: 7.2.1
LinkingTo: Rcpp, RcppArmadillo
Imports: doParallel, foreach, ggplot2, glmnet, latex2exp, MASS, parallel, Rcpp, reshape2, stats
Suggests: testthat (≥ 3.0.0), covr, vdiffr
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2022-09-16 15:25:55 UTC; jacob.a.helwig
Author: Jacob Helwig [cre, aut], Sutanoy Dasgupta [aut], Peng Zhao [aut], Bani Mallick [aut], Debdeep Pati [aut]
Maintainer: Jacob Helwig <jacob.a.helwig@tamu.edu>
Repository: CRAN
Date/Publication: 2022-09-16 15:56:08 UTC

covdepGE: Covariate Dependent Graph Estimation

Description

A covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.

Author(s)

Maintainer: Jacob Helwig jacob.a.helwig@tamu.edu

Authors:

References

(1) Sutanoy Dasgupta, Peng Zhao, Prasenjit Ghosh, Debdeep Pati, and Bani Mallick. An approximate Bayesian approach to covariate-dependent graphical modeling. pages 1–59, 2022.

See Also

Useful links:

Covariate Dependent Graph Estimation

Description

Model the conditional dependence structure of X as a function of Z as described in (1)

Usage

covdepGE(
  X,
  Z = NULL,
  hp_method = "hybrid",
  ssq = NULL,
  sbsq = NULL,
  pip = NULL,
  nssq = 5,
  nsbsq = 5,
  npip = 5,
  ssq_mult = 1.5,
  ssq_lower = 1e-05,
  snr_upper = 25,
  sbsq_lower = 1e-05,
  pip_lower = 1e-05,
  pip_upper = NULL,
  tau = NULL,
  norm = 2,
  center_X = TRUE,
  scale_Z = TRUE,
  alpha_tol = 1e-05,
  max_iter_grid = 10,
  max_iter = 100,
  edge_threshold = 0.5,
  sym_method = "mean",
  parallel = FALSE,
  num_workers = NULL,
  prog_bar = TRUE
)

Arguments

X

n \times p numeric matrix; data matrix. For best results, n should be greater than p

Z

NULL OR n \times q numeric matrix; extraneous covariates. If NULL, Z will be treated as constant for all observations, i.e.: 

Z <- rep(0, nrow(X))

If Z is constant, the estimated graph will be homogeneous throughout the data. NULL by default

hp_method

character in c("grid_search","model_average","hybrid"); method for selecting hyperparameters from the the hyperparameter grid. The grid will be generated as the Cartesian product of ssq, sbsq, and pip. Fix X_j, the j-th column of X, as the response; then, the hyperparameters will be selected as follows:

  • If "grid_search", the point in the hyperparameter grid that maximizes the total ELBO summed across all n regressions will be selected

  • If "model_average", then all posterior quantities will be an average of the variational estimates resulting from the model fit for each point in the hyperparameter grid. The unnormalized averaging weights for each of the n regressions are the exponentiated ELBO

  • If "hybrid", then models will be averaged over pip as in "model_average", with \sigma^2 and \sigma_\beta^2 chosen for each \pi in pip by maximizing the total ELBO over the grid defined by the Cartesian product of ssq and sbsq as in "grid_search"

"hybrid" by default

ssq

NULL OR numeric vector with positive entries; candidate values of the hyperparameter \sigma^2 (prior residual variance). If NULL, ssq will be generated for each variable X_j fixed as the response as: 

ssq <- seq(ssq_lower, ssq_upper, length.out = nssq)

NULL by default

sbsq

NULL OR numeric vector with positive entries; candidate values of the hyperparameter \sigma_\beta^2 (prior slab variance). If NULL, sbsq will be generated for each variable X_j fixed as the response as: 

sbsq <- seq(sbsq_lower, sbsq_upper, length.out = nsbsq)

NULL by default

pip

NULL OR numeric vector with entries in (0, 1); candidate values of the hyperparameter \pi (prior inclusion probability). If NULL, pip will be generated for each variable X_j fixed as the response as: 

pip <- seq(pip_lower, pi_upper, length.out = npip)

NULL by default

nssq

positive integer; number of points to generate for ssq if ssq is NULL. 5 by default

nsbsq

positive integer; number of points to generate for sbsq if sbsq is NULL. 5 by default

npip

positive integer; number of points to generate for pip if pip is NULL. 5 by default

ssq_mult

positive numeric; if ssq is NULL, then for each variable X_j fixed as the response: 

ssq_upper <- ssq_mult * stats::var(X_j)

Then, ssq_upper will be the greatest value in ssq for variable X_j. 1.5 by default

ssq_lower

positive numeric; if ssq is NULL, then ssq_lower will be the least value in ssq. 1e-5 by default

snr_upper

positive numeric; upper bound on the signal-to-noise ratio. If sbsq is NULL, then for each variable X_j fixed as the response: 

s2_sum <- sum(apply(X, 2, stats::var))
sbsq_upper <- snr_upper / (pip_upper * s2_sum)

Then, sbsq_upper will be the greatest value in sbsq. 25 by default

sbsq_lower

positive numeric; if sbsq is NULL, then sbsq_lower will be the least value in sbsq. 1e-5 by default

pip_lower

numeric in (0, 1); if pip is NULL, then pip_lower will be the least value in pip. 1e-5 by default

pip_upper

NULL OR numeric in (0, 1); if pip is NULL, then pip_upper will be the greatest value in pip. If sbsq is NULL, pip_upper will be used to calculate sbsq_upper. If NULL, pip_upper will be calculated for each variable X_j fixed as the response as: 

lasso <- glmnet::cv.glmnet(X, X_j)
non0 <- sum(glmnet::coef.glmnet(lasso, s = "lambda.1se")[-1] != 0)
non0 <- min(max(non0, 1), p - 1)
pip_upper <- non0 / p

NULL by default

tau

NULL OR positive numeric OR numeric vector of length n with positive entries; bandwidth parameter. Greater values allow for more information to be shared between observations. Allows for global or observation-specific specification. If NULL, use 2-step KDE methodology as described in (2) to calculate observation-specific bandwidths. NULL by default

norm

numeric in [1, \infty]; norm to use when calculating weights. Inf results in infinity norm. 2 by default

center_X

logical; if TRUE, center X column-wise to mean 0. TRUE by default

scale_Z

logical; if TRUE, center and scale Z column-wise to mean 0, standard deviation 1 prior to calculating the weights. TRUE by default

alpha_tol

positive numeric; end CAVI when the Frobenius norm of the change in the alpha matrix is within alpha_tol. 1e-5 by default

max_iter_grid

positive integer; if tolerance criteria has not been met by max_iter_grid iterations during grid search, end CAVI. After grid search has completed, CAVI is performed with the final hyperparameters selected by grid search for at most max_iter iterations. Does not apply to hp_method = "model_average". 10 by default

max_iter

positive integer; if tolerance criteria has not been met by max_iter iterations, end CAVI. 100 by default

edge_threshold

numeric in (0, 1); a graph for each observation will be constructed by including an edge between variable i and variable j if, and only if, the (i, j) entry of the symmetrized posterior inclusion probability matrix corresponding to the observation is greater than edge_threshold. 0.5 by default

sym_method

character in c("mean","max","min"); to symmetrize the posterior inclusion probability matrix for each observation, the (i, j) and (j, i) entries will be post-processed as sym_method applied to the (i, j) and (j, i) entries. "mean" by default

parallel

logical; if TRUE, hyperparameter selection and CAVI for each of the p variables will be performed in parallel using foreach. Parallel backend may be registered prior to making a call to covdepGE. If no active parallel backend can be detected, then parallel backend will be automatically registered using: 

doParallel::registerDoParallel(num_workers)

FALSE by default

num_workers

NULL OR positive integer less than or equal to parallel::detectCores(); argument to doParallel::registerDoParallel if parallel = TRUE and no parallel backend is detected. If NULL, then: 

num_workers <- floor(parallel::detectCores() / 2)

NULL by default

prog_bar

logical; if TRUE, then a progress bar will be displayed denoting the number of remaining variables to fix as the response and perform CAVI. If parallel, no progress bar will be displayed. TRUE by default

Value

Returns object of class covdepGE with the following values:

graphs

list with the following values:

  • graphs: list of n numeric matrices of dimension p \times p; the l-th matrix is the adjacency matrix for the l-th observation

  • unique_graphs: list; the l-th element is a list containing the l-th unique graph and the indices of the observation(s) corresponding to this graph

  • inclusion_probs_sym: list of n numeric matrices of dimension p \times p; the l-th matrix is the symmetrized posterior inclusion probability matrix for the l-th observation

  • inclusion_probs_asym: list of n numeric matrices of dimension p \times p; the l-th matrix is the posterior inclusion probability matrix for the l-th observation prior to symmetrization

variational_params

list with the following values:

  • alpha: list of p numeric matrices of dimension n \times (p - 1); the (i, j) entry of the k-th matrix is the variational approximation to the posterior inclusion probability of the j-th variable in a weighted regression with variable k fixed as the response, where the weights are taken with respect to observation i

  • mu: list of p numeric matrices of dimension n \times (p - 1); the (i, j) entry of the k-th matrix is the variational approximation to the posterior slab mean for the j-th variable in a weighted regression with variable k fixed as the response, where the weights are taken with respect to observation i

  • ssq_var: list of p numeric matrices of dimension n \times (p - 1); the (i, j) entry of the k-th matrix is the variational approximation to the posterior slab variance for the j-th variable in a weighted regression with variable k fixed as the response, where the weights are taken with respect to observation i

hyperparameters

list of p lists; the j-th list has the following values for variable j fixed as the response:

  • grid: matrix of candidate hyperparameter values, corresponding ELBO, and iterations to converge

  • final: the final hyperparameters chosen by grid search and the ELBO and iterations to converge for these hyperparameters

model_details

list with the following values:

  • elapsed: amount of time to fit the model

  • n: number of observations

  • p: number of variables

  • ELBO: ELBO summed across all observations and variables. If hp_method is "model_average" or "hybrid", this ELBO is averaged across the hyperparameter grid using the model averaging weights for each variable

  • num_unique: number of unique graphs

  • grid_size: number of points in the hyperparameter grid

  • args: list containing all passed arguments of length 1

weights

list with the following values:

  • weights: n\times n numeric matrix. The (i, j) entry is the similarity weight of the i-th observation with respect to the j-th observation using the j-th observation's bandwidth

  • bandwidths: numeric vector of length n. The i-th entry is the bandwidth for the i-th observation

References

(1) Sutanoy Dasgupta, Peng Zhao, Prasenjit Ghosh, Debdeep Pati, and Bani Mallick. An approximate Bayesian approach to covariate-dependent graphical modeling. pages 1–59, 2022.

(2) Sutanoy Dasgupta, Debdeep Pati, and Anuj Srivastava. A Two-Step Geometric Framework For Density Modeling. Statistica Sinica, 30(4):2155–2177, 2020.

Examples

## Not run: 
library(ggplot2)

# get the data
set.seed(12)
data <- generateData()
X <- data$X
Z <- data$Z
interval <- data$interval
prec <- data$true_precision

# get overall and within interval sample sizes
n <- nrow(X)
n1 <- sum(interval == 1)
n2 <- sum(interval == 2)
n3 <- sum(interval == 3)

# visualize the distribution of the extraneous covariate
ggplot(data.frame(Z = Z, interval = as.factor(interval))) +
  geom_histogram(aes(Z, fill = interval), color = "black", bins = n %/% 5)

# visualize the true precision matrices in each of the intervals

# interval 1
matViz(prec[[1]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 1, observations 1,...,", n1))

# interval 2 (varies continuously with Z)
cat("\nInterval 2, observations ", n1 + 1, ",...,", n1 + n2, sep = "")
int2_mats <- prec[interval == 2]
int2_inds <- c(5, n2 %/% 2, n2 - 5)
lapply(int2_inds, function(j) matViz(int2_mats[[j]], incl_val = TRUE) +
         ggtitle(paste("True precision matrix, interval 2, observation", j + n1)))

# interval 3
matViz(prec[[length(prec)]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 3, observations ",
                 n1 + n2 + 1, ",...,", n1 + n2 + n3))

# fit the model and visualize the estimated graphs
(out <- covdepGE(X, Z))
plot(out)

# visualize the posterior inclusion probabilities for variables (1, 3) and (1, 2)
inclusionCurve(out, 1, 2)
inclusionCurve(out, 1, 3)

## End(Not run)

Generate Covariate-Dependent Data

Description

Generate a 1-dimensional extraneous covariate and p-dimensional Gaussian data with a precision matrix that varies as a continuous function of the extraneous covariate. This data is distributed similar to that used in the simulation study from (1)

Usage

generateData(p = 5, n1 = 60, n2 = 60, n3 = 60, Z = NULL, true_precision = NULL)

Arguments

p

positive integer; number of variables in the data matrix. 5 by default

n1

positive integer; number of observations in the first interval. 60 by default

n2

positive integer; number of observations in the second interval. 60 by default

n3

positive integer; number of observations in the third interval. 60 by default

Z

NULL or numeric vector; extraneous covariate values for each observation. If NULL, Z will be generated from a uniform distribution on each of the intervals

true_precision

NULL OR list of matrices of dimension p \times p; true precision matrix for each observation. If NULL, the true precision matrices will be generated dependent on Z. NULL by default

Value

Returns list with the following values:

X

a (n1 + n2 + n3) \times p numeric matrix, where the i-th row is drawn from a p-dimensional Gaussian with mean 0 and precision matrix true_precision[[i]]

Z

a (n1 + n2 + n3) \times 1 numeric matrix, where the i-th entry is the extraneous covariate z_i for observation i

true_precision

list of n1 + n2 + n3 matrices of dimension p \times p; the i-th matrix is the precision matrix for the i-th observation

interval

vector of length n1 + n2 + n3; interval assignments for each of the observations, where the i-th entry is the interval assignment for the i-th observation

Extraneous Covariate

If Z = NULL, then the generation of Z is as follows:

The first n1 observations have z_i from from a uniform distribution on the interval (-3, -1) (the first interval).

Observations n1 + 1 to n1 + n2 have z_i from from a uniform distribution on the interval (-1, 1) (the second interval).

Observations n1 + n2 + 1 to n1 + n2 + n3 have z_i from a uniform distribution on the interval (1, 3) (the third interval).

Precision Matrices

If true_precision = NULL, then the generation of the true precision matrices is as follows:

All precision matrices have 2 on the diagonal and 1 in the (2, 3)/ (3, 2) positions.

Observations in the first interval have a 1 in the (1, 2) / (1, 2) positions, while observations in the third interval have a 1 in the (1, 3)/ (3, 1) positions.

Observations in the second interval have 2 entries that vary as a linear function of their extraneous covariate. Let \beta = 1/2. Then, the (1, 2)/(2, 1) positions for the i-th observation in the second interval are \beta\cdot(1 - z_i), while the (1, 3)/ (3, 1) entries are \beta\cdot(1 + z_i).

Thus, as z_i approaches -1 from the right, the associated precision matrix becomes more similar to the matrix for observations in the first interval. Similarly, as z_i approaches 1 from the left, the matrix becomes more similar to the matrix for observations in the third interval.

Examples

## Not run: 
library(ggplot2)

# get the data
set.seed(12)
data <- generateData()
X <- data$X
Z <- data$Z
interval <- data$interval
prec <- data$true_precision

# get overall and within interval sample sizes
n <- nrow(X)
n1 <- sum(interval == 1)
n2 <- sum(interval == 2)
n3 <- sum(interval == 3)

# visualize the distribution of the extraneous covariate
ggplot(data.frame(Z = Z, interval = as.factor(interval))) +
  geom_histogram(aes(Z, fill = interval), color = "black", bins = n %/% 5)

# visualize the true precision matrices in each of the intervals

# interval 1
matViz(prec[[1]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 1, observations 1,...,", n1))

# interval 2 (varies continuously with Z)
cat("\nInterval 2, observations ", n1 + 1, ",...,", n1 + n2, sep = "")
int2_mats <- prec[interval == 2]
int2_inds <- c(5, n2 %/% 2, n2 - 5)
lapply(int2_inds, function(j) matViz(int2_mats[[j]], incl_val = TRUE) +
         ggtitle(paste("True precision matrix, interval 2, observation", j + n1)))

# interval 3
matViz(prec[[length(prec)]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 3, observations ",
                 n1 + n2 + 1, ",...,", n1 + n2 + n3))

# fit the model and visualize the estimated graphs
(out <- covdepGE(X, Z))
plot(out)

# visualize the posterior inclusion probabilities for variables (1, 3) and (1, 2)
inclusionCurve(out, 1, 2)
inclusionCurve(out, 1, 3)

## End(Not run)

Plot PIP as a Function of Index

Description

Plot the posterior inclusion probability of an edge between two variables as a function of observation index

Usage

inclusionCurve(
  out,
  col_idx1,
  col_idx2,
  line_type = "solid",
  line_size = 0.5,
  line_color = "black",
  point_shape = 21,
  point_size = 1.5,
  point_color = "#500000",
  point_fill = "white"
)

Arguments

out

object of class covdepGE; return of covdepGE function

col_idx1

integer in [1, p]; column index of the first variable

col_idx2

integer in [1, p]; column index of the second variable

line_type

linetype; ggplot2 line type to interpolate the probabilities. "solid" by default

line_size

positive numeric; thickness of the interpolating line. 0.5 by default

line_color

color; color of interpolating line. "black" by default

point_shape

shape; shape of the points denoting observation-specific inclusion probabilities; 21 by default

point_size

positive numeric; size of probability points. 1.5 by default

point_color

color; color of probability points. "#500000" by default

point_fill

color; fill of probability points. Only applies to select shapes. "white" by default

Value

Returns ggplot2 visualization of inclusion probability curve

Examples

## Not run: 
library(ggplot2)

# get the data
set.seed(12)
data <- generateData()
X <- data$X
Z <- data$Z
interval <- data$interval
prec <- data$true_precision

# get overall and within interval sample sizes
n <- nrow(X)
n1 <- sum(interval == 1)
n2 <- sum(interval == 2)
n3 <- sum(interval == 3)

# visualize the distribution of the extraneous covariate
ggplot(data.frame(Z = Z, interval = as.factor(interval))) +
  geom_histogram(aes(Z, fill = interval), color = "black", bins = n %/% 5)

# visualize the true precision matrices in each of the intervals

# interval 1
matViz(prec[[1]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 1, observations 1,...,", n1))

# interval 2 (varies continuously with Z)
cat("\nInterval 2, observations ", n1 + 1, ",...,", n1 + n2, sep = "")
int2_mats <- prec[interval == 2]
int2_inds <- c(5, n2 %/% 2, n2 - 5)
lapply(int2_inds, function(j) matViz(int2_mats[[j]], incl_val = TRUE) +
         ggtitle(paste("True precision matrix, interval 2, observation", j + n1)))

# interval 3
matViz(prec[[length(prec)]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 3, observations ",
                 n1 + n2 + 1, ",...,", n1 + n2 + n3))

# fit the model and visualize the estimated graphs
(out <- covdepGE(X, Z))
plot(out)

# visualize the posterior inclusion probabilities for variables (1, 3) and (1, 2)
inclusionCurve(out, 1, 2)
inclusionCurve(out, 1, 3)

## End(Not run)

Visualize a matrix

Description

Create a visualization of a matrix

Usage

matViz(
  x,
  color1 = "white",
  color2 = "#500000",
  grid_color = "black",
  incl_val = FALSE,
  prec = 2,
  font_size = 3,
  font_color1 = "black",
  font_color2 = "white",
  font_thres = mean(x)
)

Arguments

x

matrix; matrix to be visualized

color1

color; color for low entries. "white" by default

color2

color; color for high entries. "#500000" by default

grid_color

color; color of grid lines. "black" by default

incl_val

logical; if TRUE, the value for each entry will be displayed. FALSE by default

prec

positive integer; number of decimal places to round entries to if incl_val is TRUE. 2 by default

font_size

positive numeric; size of font if incl_val is TRUE. 3 by default

font_color1

color; color of font for low entries if incl_val is TRUE. "black" by default

font_color2

color; color of font for high entries if incl_val is TRUE. "white" by default

font_thres

numeric; values less than font_thres will be displayed in font_color1 if incl_val is TRUE. mean(x) by default

Value

Returns ggplot2 visualization of matrix

Examples

## Not run: 
library(ggplot2)

# get the data
set.seed(12)
data <- generateData()
X <- data$X
Z <- data$Z
interval <- data$interval
prec <- data$true_precision

# get overall and within interval sample sizes
n <- nrow(X)
n1 <- sum(interval == 1)
n2 <- sum(interval == 2)
n3 <- sum(interval == 3)

# visualize the distribution of the extraneous covariate
ggplot(data.frame(Z = Z, interval = as.factor(interval))) +
  geom_histogram(aes(Z, fill = interval), color = "black", bins = n %/% 5)

# visualize the true precision matrices in each of the intervals

# interval 1
matViz(prec[[1]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 1, observations 1,...,", n1))

# interval 2 (varies continuously with Z)
cat("\nInterval 2, observations ", n1 + 1, ",...,", n1 + n2, sep = "")
int2_mats <- prec[interval == 2]
int2_inds <- c(5, n2 %/% 2, n2 - 5)
lapply(int2_inds, function(j) matViz(int2_mats[[j]], incl_val = TRUE) +
         ggtitle(paste("True precision matrix, interval 2, observation", j + n1)))

# interval 3
matViz(prec[[length(prec)]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 3, observations ",
                 n1 + n2 + 1, ",...,", n1 + n2 + n3))

# fit the model and visualize the estimated graphs
(out <- covdepGE(X, Z))
plot(out)

# visualize the posterior inclusion probabilities for variables (1, 3) and (1, 2)
inclusionCurve(out, 1, 2)
inclusionCurve(out, 1, 3)

## End(Not run)

Plot the Graphs Estimated by covdepGE

Description

Create a list of the unique graphs estimated by covdepGE

Usage

## S3 method for class 'covdepGE'
plot(x, graph_colors = NULL, title_sum = TRUE, ...)

Arguments

x

object of class covdepGE; return of covdepGE function

graph_colors

NULL OR vector; the j-th element is the color for the j-th graph. If NULL, all graphs will be colored with "#500000". NULL by default

title_sum

logical; if TRUE the indices of the observations corresponding to the graph will be included in the title. TRUE by default

...

additional arguments will be ignored

Value

Returns list of ggplot2 visualizations of unique graphs estimated by covdepGE

Examples

## Not run: 
library(ggplot2)

# get the data
set.seed(12)
data <- generateData()
X <- data$X
Z <- data$Z
interval <- data$interval
prec <- data$true_precision

# get overall and within interval sample sizes
n <- nrow(X)
n1 <- sum(interval == 1)
n2 <- sum(interval == 2)
n3 <- sum(interval == 3)

# visualize the distribution of the extraneous covariate
ggplot(data.frame(Z = Z, interval = as.factor(interval))) +
  geom_histogram(aes(Z, fill = interval), color = "black", bins = n %/% 5)

# visualize the true precision matrices in each of the intervals

# interval 1
matViz(prec[[1]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 1, observations 1,...,", n1))

# interval 2 (varies continuously with Z)
cat("\nInterval 2, observations ", n1 + 1, ",...,", n1 + n2, sep = "")
int2_mats <- prec[interval == 2]
int2_inds <- c(5, n2 %/% 2, n2 - 5)
lapply(int2_inds, function(j) matViz(int2_mats[[j]], incl_val = TRUE) +
         ggtitle(paste("True precision matrix, interval 2, observation", j + n1)))

# interval 3
matViz(prec[[length(prec)]], incl_val = TRUE) +
  ggtitle(paste0("True precision matrix, interval 3, observations ",
                 n1 + n2 + 1, ",...,", n1 + n2 + n3))

# fit the model and visualize the estimated graphs
(out <- covdepGE(X, Z))
plot(out)

# visualize the posterior inclusion probabilities for variables (1, 3) and (1, 2)
inclusionCurve(out, 1, 2)
inclusionCurve(out, 1, 3)

## End(Not run)

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