find optimal penalized zero-inflated model

Description

Fit penalized zero-inflated models, generate multiple paths with varying penalty parameters, therefore determine optimal penalty parameters

Usage

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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

find optimal lambdaZeroRatio for penalized zero-inflated Poisson, negative binomial and geometric model

Value

An object of class zipath with the optimal lambdaZeroRatio

Author(s)

Zhu Wang <zwang@connecticutchildrens.org>

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.

See Also

zipath

Examples

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## 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)

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