p_zipath: compute p-values from penalized zero-inflated model with...

In zhuwang46/mpath: Regularized Linear Models

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.

zhuwang46/mpath documentation built on March 21, 2022, 4:27 a.m.