LoglikPen: Compute the penalized log likelihood for GLM with MIC penalty...

In liqun730/gMIC: Group Sparsity via Approximated Information Criteria

Description

Compute the penalized log likelihood for GLM with MIC penalty (Self-Written)

Usage

LoglikPen(gamma, group, X, y, lambda, a, family = "gaussian")

Arguments

group	The group structure of the model. For example, assume that X has 4 columns and group=c(1,1,2,2). It means the first 2 features form a group of variables and the last 2 features form another group of variables.
X	An n by p design matrix.
y	The n by 1 response vector
lambda	The penalty parameter euqals either 2 in AIC or ln(n) in BIC (by default). It can be specified as any value of the user's own choice.
a	The scale parameter in the hyperbolic tangent function of the MIC penalty. By default, a = 50.
family	a description of the error distribution. To use this function, family needs to be one of "gaussian",
beta	A p-dimensional vector containing the regression ceofficients.

Details

This function is much faster than LoglikPenGLM, but it is only applicable for Gaussian linear regression, logistic regression, and loglinear or Poisson regression models. To take advantage, sepcify family as family="gaussian", family="binomial", or family="poisson" only.

Value

The value of the penalized log likelihood function evaluated at beta.

See Also

glm, family

liqun730/gMIC documentation built on May 22, 2019, 11:52 p.m.