p_zipath function

compute p-values from penalized zero-inflated model with multi-split data

compute p-values from penalized zero-inflated Poisson, negative binomial and geometric model with multi-split data

pval.zipath(formula, data, weights, subset, na.action, offset, standardize=TRUE,
            family = c("poisson", "negbin", "geometric"),
            penalty = c("enet", "mnet", "snet"), gamma.count = 3, 
            gamma.zero = 3, prop=0.5, trace=TRUE, B=10, ...)

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 zipath
• penalty: penalty considered as one of enet, mnet, snet.
• 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.
• prop: proportion of data split, default is 50/50 split
• trace: logical value, if TRUE, print detailed calculation results
• B: number of repeated multi-split replications
• ...: Other arguments passing to glmreg_fit

Details

compute p-values from penalized zero-inflated Poisson, negative binomial and geometric model with multi-split data

Returns

• count.pval: raw p-values in the count component

• zero.pval: raw p-values in the zero component

• count.pval.q: Q value for the count component

• zero.pval.q: Q value for the zero component

References

Nicolai Meinshausen, Lukas Meier and Peter Buehlmann (2013) p-Values for High-Dimensional Regression, Journal of the American Statistical Association, 104(488), 1671--1681.

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