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mpath (version 0.1-20)

pval.zipath: compute p-values from penalized zero-inflated model with multi-split data

Description

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

Usage

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

Value

  • count.pvalraw p-values in the count component
  • zero.pvalraw p-values in the zero component
  • count.pval.qQ value for the count component
  • zero.pval.qQ value for the zero component

Details

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

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.