mpath (version 0.4-2.26)

zipath: Fit zero-inflated count data linear model with lasso (or elastic net), snet or mnet regularization

Description

Fit zero-inflated regression models for count data via penalized maximum likelihood.

Usage

# S3 method for formula
zipath(formula, data, weights, offset=NULL, contrasts=NULL, ... )
# S3 method for matrix
zipath(X, Z, Y, weights, offsetx=NULL, offsetz=NULL, ...)
# S3 method for default
zipath(X, ...)

Value

An object of class "zipath", i.e., a list with components including

coefficients

a list with elements "count" and "zero" containing the coefficients from the respective models,

residuals

a vector of raw residuals (observed - fitted),

fitted.values

a vector of fitted means,

weights

the case weights used,

terms

a list with elements "count", "zero" and "full" containing the terms objects for the respective models,

theta

estimate of the additional \(\theta\) parameter of the negative binomial model (if a negative binomial regression is used),

loglik

log-likelihood of the fitted model,

family

character string describing the count distribution used,

link

character string describing the link of the zero-inflation model,

linkinv

the inverse link function corresponding to link,

converged

logical value, TRUE indicating successful convergence of zipath, FALSE indicating otherwise

call

the original function call

formula

the original formula

levels

levels of the categorical regressors

contrasts

a list with elements "count" and "zero" containing the contrasts corresponding to levels from the respective models,

model

the full model frame (if model = TRUE),

y

the response count vector (if y = TRUE),

x

a list with elements "count" and "zero" containing the model matrices from the respective models (if x = TRUE),

Arguments

formula

symbolic description of the model, see details.

data

argument controlling formula processing via model.frame.

weights

optional numeric vector of weights.

offset

optional numeric vector with an a priori known component to be included in the linear predictor of the count model or zero model. See below for an example.

contrasts

a list with elements "count" and "zero" containing the contrasts corresponding to levels from the respective models

X

predictor matrix of the count model

Z

predictor matrix of the zero model

Y

response variable

offsetx, offsetz

optional numeric vector with an a priori known component to be included in the linear predictor of the count model (offsetx)or zero model (offsetz).

...

Other arguments which can be passed to glmreg or glmregNB

Author

Zhu Wang <[e-mail]>

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_fit, glmreg, glmregNB

Examples

## data
data("bioChemists", package = "pscl")
## with simple inflation (no regressors for zero component)
fm_zip <- zipath(art ~ 1 | ., data = bioChemists, nlambda=10)
summary(fm_zip)
fm_zip <- zipath(art ~ . | 1, data = bioChemists, nlambda=10)
summary(fm_zip)
if (FALSE) {
fm_zip <- zipath(art ~ . | 1, data = bioChemists, nlambda=10)
summary(fm_zip)
fm_zinb <- zipath(art ~ . | 1, data = bioChemists, family = "negbin", nlambda=10)
summary(fm_zinb)
## inflation with regressors
## ("art ~ . | ." is "art ~ fem + mar + kid5 + phd + ment | fem + mar + kid5 + phd + ment")
fm_zip2 <- zipath(art ~ . | ., data = bioChemists, nlambda=10)
summary(fm_zip2)
fm_zinb2 <- zipath(art ~ . | ., data = bioChemists, family = "negbin", nlambda=10)
summary(fm_zinb2)
### non-penalized regression, compare with zeroinfl
fm_zinb3 <- zipath(art ~ . | ., data = bioChemists, family = "negbin", 
lambda.count=0, lambda.zero=0, reltol=1e-12)
summary(fm_zinb3)
library("pscl")
fm_zinb4 <- zeroinfl(art ~ . | ., data = bioChemists, dist = "negbin")
summary(fm_zinb4)
### offset
exposure <- rep(0.5, dim(bioChemists)[1])
fm_zinb <- zipath(art ~ . +offset(log(exposure))| ., data = bioChemists, 
		  family = "poisson", nlambda=10)
coef <- coef(fm_zinb)
### offset can't be specified in predict function as it has been contained
pred <- predict(fm_zinb)
## without inflation
## ("art ~ ." is "art ~ fem + mar + kid5 + phd + ment")
fm_pois <- glmreg(art ~ ., data = bioChemists, family = "poisson")
coef <- coef(fm_pois)
fm_nb <- glmregNB(art ~ ., data = bioChemists)
coef <- coef(fm_nb)
### high-dimensional
#R CMD check --use-valgrind can be too time extensive for the following model
#bioChemists <- cbind(matrix(rnorm(915*100), nrow=915), bioChemists)
#fm_zinb <- zipath(art ~ . | ., data = bioChemists, family = "negbin", nlambda=10)
}