Cond_Gaussian {PAGE} | R Documentation |
Estimation of network structure and variable selection in the linear model via the conditional likelihood function.
Description
This function focuses on multivariate linear regression models Y = XB + \epsilon
subject to measurement error in responses and covariates, where with B is a matrix of parameters and \epsilon
is a noise term with zero expectation. We aim to detect the network structure of responses and select informative covaraites. The estimation method is motivated by the conditional likelihood function and uses the conditional expectation to correct for measurement error.
Usage
Cond_Gaussian(
W,
Z,
sigma_eta,
sigma_delta,
alpha_1,
alpha_2,
alpha_1_list = NULL,
alpha_2_list = NULL,
max_iter = 30,
tol = 1e-06,
label_name = TRUE
)
Arguments
W |
A n × m response matrix, the variables can be error-prone or precisely measured. |
Z |
A n × p matrix of continuous covariates. The variables can be error-prone or precisely measured. |
sigma_eta |
A p × p covariance matrix of the noise term |
sigma_delta |
A m × m covariance matrix of the noise term |
alpha_1 |
A tuning parameter associated with parameter B. |
alpha_2 |
A tuning parameter associated with parameter, denoted as |
alpha_1_list |
A list of tuning parameters for the model averaging estimator of B. The default value is NULL. |
alpha_2_list |
A list of tuning parameters for the model averaging estimator of |
max_iter |
A maximum number for iterations for updated values of B and |
tol |
A prespecified tolerance |
label_name |
The name of the response variables. The default value is TRUE, which reflects the labels from the input data. Else, users can input the required labels manually. |
Value
B |
An estimator of B. |
gamma |
An estimator of the network in Y. |
graph |
A visualization of the estimated network structure by gamma. |
Beta_BICs |
A vector of Bayesian Information Criterion (BIC) weights for the model averaging estimator of B under candidate models alpha_1_list. |
Gamma_BICs |
A vector of Bayesian Information Criterion (BIC) weights for the model averaging estimator of |
Author(s)
Wan-Yi Chang and Li-Pang Chen
Maintainer: Wan-Yi Chang jessica306a@gmail.com
Examples
n <- 100
Z <- matrix(rnorm(n * 5), n, 5)
W <- matrix(rnorm(n * 5), n, 5)
sigma_eta <- diag(0.15, ncol(Z))
sigma_delta <- diag(0.3, ncol(W))
Cond_Gaussian(W, Z, sigma_eta, sigma_delta,
alpha_1 = 0.1, alpha_2 = 0.1,
alpha_1_list = NULL,
alpha_2_list = NULL,
max_iter = 1, tol = 1e-6, label_name = TRUE)