pval.zipath {mpath}

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

Details

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

Value

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

Author(s)

Zhu Wang <[e-mail]>

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.