zilgm_negbin: Neiborhood selection under the zero-inflated negative...

In bbeomjin/ZILGM: Implementations of local Markov random fields for count data with zero-inflation and overdispersion

Neiborhood selection under the zero-inflated negative binomial I distribution for Zero-inflated Local Graphical Model

Description

Neiborhood selection under the zero-inflated negative binomial distribution I by zero-inflated negative binomial regression with l_1-regularization.

Usage

zilgm_negbin(y, x, lambda, weights = NULL, update_type = c("IRLS", "MM"), 
             penalty.factor = NULL, thresh = 1e-6, EM_tol = 1e-5, EM_iter = 3e+2,
 			 tol = 1e-6, maxit = 3e+2, theta = NULL)

Arguments

y	A numeric vector. A response variable y.
x	A design matrix x.
lambda	A regularization parameter to control a level of l_1-penalty.
weights	Weights vector for observations. A default value for each observation is 1.
update_type	Types of algorithm for estimating coefficients.
penalty.factor	Weights vector for coefficients of each variable. A default value for each variable is 1.
thresh	Threshold value for the estimated coefficients.
EM_tol	Convergence tolerance for EM algorithm.
EM_iter	A integer value. Maximum number of EM algorithm iterations.
tol	Convergence tolerance for coordinate descent algorithm.
maxit	A integer value. Maximum number of coordinate descent iterations.
theta	A numeric value. If the argument is given value, the neiborhood selection is implemented using this dispersion value without estimating the dispersion parameter.

Value

An S3 object with the following slots

beta	Estimated coefficients vector.
theta	Estimated dispersion parameter theta.
prob	Estimated probability of structural zero.
pos_zero	Indices of zero values.
iteration	Iteration numbers until convergence.
loglik	l_1-penalized negative log-likelihood value.
call	The matched call.

References

Wang, Z., S. Ma, M. Zappitelli, C. Parikh, C.-Y. Wang, and P. Devarajan, 2016: Penalized count data regression with application to hospital stay after pediatric cardiac surgery. Stat. Methods Med. Res., 25, no.6, 2685-2703.
Choi, H., J. Gim, S. Won, Y. J. Kim, S. Kwon, and C. Park, 2017: Network analysis for count data with excess zeros. BMC genetics, 18, no. 1, 1-10.
Park, B., H. Choi, C. Park, 2021: Negative binomial graphical model with excess zeros.

Examples

# Zero-inflated negative binomial I regression with l_1 penalty
require(ZILGM)
set.seed(1)
n = 100; p = 10; prob = 2 / p;
A = generate_network(p, prob, type = "random")
simul_dat = zilgm_sim(A = A, n = n, p = p, zlvs = 0.1, 
					  family = "negbin", signal = 1.5, theta = 0.5, noise = 0.0)
y = simul_dat$X[, 1]
X = simul_dat$X[, -1]
negbin_fit = zilgm_negbin(y = y, x = X, lambda = 1, update_type = "IRLS")

bbeomjin/ZILGM documentation built on Aug. 5, 2023, 5:52 a.m.