tuning_zipath function

find optimal path for penalized zero-inflated model

Fit penalized zero-inflated models, generate multiple paths with varying penalty parameters, therefore determine optimal path with respect to a particular penalty parameter

tuning.zipath(formula, data, weights, subset, na.action, offset, standardize=TRUE,
              family = c("poisson", "negbin", "geometric"), 
              penalty = c("enet", "mnet", "snet"), lambdaCountRatio = .0001, 
              lambdaZeroRatio = c(.1, .01, .001), maxit.theta=1, gamma.count=3, 
              gamma.zero=3, ...)

Arguments

• formula: symbolic description of the model, see details.

• data: argument controlling formula processing via model.frame.

• weights: optional numeric vector of weights. If standardize=TRUE, weights are renormalized to weights/sum(weights). If standardize=FALSE, weights are kept as original input

• subset: subset of data

• na.action: how to deal with missing data

• offset: Not implemented yet

• standardize: logical value, should variables be standardized?

• family: family to fit

• penalty: penalty considered as one of enet, mnet, snet.

• lambdaCountRatio, lambdaZeroRatio: Smallest value for lambda.count

  and lambda.zero, respectively, as a fraction of lambda.max, the (data derived) entry value (i.e. the smallest value for which all coefficients are zero except the intercepts). This lambda.max can be a surrogate value for penalty="mnet" or "snet"

• maxit.theta: For family="negbin", the maximum iteration allowed for estimating scale parameter theta. Note, the default value 1 is for computing speed purposes, and is typically too small and less desirable in real data analysis

• gamma.count: The tuning parameter of the snet or mnet penalty for the count part of model.

• gamma.zero: The tuning parameter of the snet or mnet penalty for the zero part of model.

• ...: Other arguments passing to zipath

Details

From the default lambdaZeroRatio = c(.1, .01, .001) values, find optimal lambdaZeroRatio for penalized zero-inflated Poisson, negative binomial and geometric model

Returns

An object of class zipath with the optimal lambdaZeroRatio

References

Zhu Wang, Shuangge Ma, Michael Zappitelli, Chirag Parikh, Ching-Yun Wang and Prasad Devarajan (2014) Penalized Count Data Regression with Application to Hospital Stay after Pediatric Cardiac Surgery, Statistical Methods in Medical Research. 2014 Apr 17. [Epub ahead of print]

Zhu Wang, Shuangge Ma, Ching-Yun Wang, Michael Zappitelli, Prasad Devarajan and Chirag R. Parikh (2014) EM for Regularized Zero Inflated Regression Models with Applications to Postoperative Morbidity after Cardiac Surgery in Children, Statistics in Medicine. 33(29):5192-208.

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

See Also

zipath

Examples

## Not run:

## data
data("bioChemists", package = "pscl")

## inflation with regressors
## ("art ~ . | ." is "art ~ fem + mar + kid5 + phd + ment | fem + mar + kid5 + phd + ment")
fm_zip2 <- tuning.zipath(art ~ . | ., data = bioChemists, nlambda=10)
summary(fm_zip2)
fm_zinb2 <- tuning.zipath(art ~ . | ., data = bioChemists, family = "negbin", nlambda=10)
summary(fm_zinb2)
## End(Not run)