minb {minb}

Multiple-Inflated Negative Binomial Model

Description

The minb is the main function to achieve data-driven inflated values selection and identification of important predictors for multiple inflated count data modeling.

Usage

minb(X,y,pars_init=NULL,lambda1_set=NULL,lambda2_set=NULL,ntune=10,
maxiter=200,tol=1e-03,vrbs=FALSE)

Arguments

X

The design matrix, without an intercept, minb includes an intercept by default.

y

The response count vector.

pars_init

an optional list containing the initial values for the corresponding component. See details.

lambda1_set

A user supplied tuning sequence for inflated values. Typical usage is to have the program compute its own sequence based on ntune. Supplying a value of lambda1_set overrides this.

lambda2_set

A user supplied tuning sequence for regression predictors. Typical usage is to have the program compute its own sequence based on ntune. Supplying a value of lambda2_set overrides this.

ntune

The number of the tuning parameter values, default to 10.

maxiter

defines the maximum number of iterations, default to 200.

tol

specifies the iteration convergency tolerance, default to 1e-03.

vrbs

a boolean variable which defines whether to print the iteration details, default to FALSE.

Details

Initialization values can be supplied or estimated by glm.fit (the default). To supply initial values of the parameters, start should be a list with elements "kappa", "omega","beta", and "phi" containing the starting values for the coefficients of the corresponding component of the model.

Value

The minb returns a list containing the following components:

omega

The vector of the estimated mixing proportions of the selected inflated values in the multiple-inflated negative binomial model.

kappa

The vector of selected inflated values.

phi

The value of the dispersion parameter of Negative Binomial distribution.

beta

The vector of estimated non-zero regression coefficients of the Negative Binomial distribution.

Examples

# This is an example of minb with simulated data
data(minb_SimuData)
X = minb_SimuData$X
y = minb_SimuData$y
result = minb(X=X,y=y,ntune=2)
result$beta
result$omega