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

In xgsu/glmMIC: Sparse Estimation of Generalized Linear Models (GLM) via Minimum approximated Inforamtion Criterion (MIC)

Description

Compute the penalized log likelihood for GLM with MIC penalty via R function glm

Usage

LoglikPenGLM(beta, I.preselect = NULL, preselect.beta0 = FALSE, X, y,
  lambda, a = 20, family = gaussian(link = "identity"))

Arguments

beta	A p-dimensional vector containing the regression ceofficients.
I.preselect	A 0-1 indicator vector of same length as beta showing preselected variables (value 0 for no penalty) that will not be penalized.
preselect.beta0	Indicator of whether or not the intercept is pre-selected. Default is FALSE.
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 and link function to be used in the model. It needs to be the result of a call to a family function since glm.fit is used here. In other words, it can NOT be specified as a character string naming a family function nor a family function. See glm and family for details of family functions.

Value

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

References

• Su, X. (2015). Variable selection via subtle uprooting. Journal of Computational and Graphical Statistics, 24(4): 1092–1113. URL http://www.tandfonline.com/doi/pdf/10.1080/10618600.2014.955176

• Su, X., Fan, J., Levine, R. A., Nunn, M. E., and Tsai, C.-L. (2016+). Sparse estimation of generalized linear models via approximated information criteria. Submitted, Statistica Sinica.

See Also

glm, family

xgsu/glmMIC documentation built on May 4, 2019, 1:06 p.m.