sandwichReg function

Making Sandwiches with Bread and Meat for Regularized Estimators

Constructing sandwich covariance matrix estimators by multiplying bread and meat matrices for regularized regression parameters.

sandwichReg(x, breadreg.=breadReg, meatreg.=meatReg, which, log=FALSE, ...)

Arguments

• x: a fitted model object.
• breadreg.: either a breadReg matrix or a function for computing this via breadreg.(x).
• meatreg.: either a breadReg matrix or a function for computing this via meatreg.(x, ...).
• which: which penalty parameters(s) to compute?
• log: if TRUE, the corresponding element is with respect to log(theta) in negative binomial regression. Otherwise, for theta
• ...: arguments passed to the meatReg function.

Details

sandwichReg is a function to compute an estimator for the covariance of the non-zero parameters. It takes a breadReg matrix (i.e., estimator of the expectation of the negative derivative of the penalized estimating functions) and a meatReg matrix (i.e., estimator of the variance of the log-likelihood function) and multiplies them to a sandwich with meat between two slices of bread. By default breadReg and meatReg are called. Implemented only for zipath object with family="negbin" in the current version.

Returns

A matrix containing the sandwich covariance matrix estimate for the non-zero parameters.

See Also

breadReg, meatReg

References

Zhu Wang, Shuangge Ma and Ching-Yun Wang (2015) Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany, Biometrical Journal. 57(5):867-84.

Author(s)

Zhu Wang zwang145@uthsc.edu

Examples

data("bioChemists", package = "pscl")
fm_zinb <- zipath(art ~ . | ., data = bioChemists, family = "negbin", nlambda=10, maxit.em=1)
sandwichReg(fm_zinb, which=which.min(fm_zinb$bic))