TSGMM_c0: Two-Step Generalized Method of Moments, Logistic Component of...
In lalondetl/GMM:

Description Usage Arguments Examples

This function calculates the Generalized Method of Moments (GMM) parameter estimates and standard errors for the logistic component (modeling the probability of a "certain zero") of a hurdle model for longitudinal excess zero count responses. The function allows for unbalanced data, meaning subjects can have different numbers of times of observation. Both time-independent covariates and time-dependent covariates can be accommodated. Time-dependent covariates can be handled either by specifying the type of each time-dependent covariate, or by allowing the data to determine appropriate moment conditions through the extended classification method. Data must be organized by subject, and an intercept term is assumed. The function outputs a list with parameter estimates betaHat along with parameter covariance estimates covEst.

1 2	TSGMM_c0(y, subjectID, Zmat, Xmat, Tvec, N, mc = "EC", covTypeVec = c(-1), betaI, r_l)

`y`	The vector of binary responses, where "1" indicates a zero from the original response and "0" indicates a positive count from the original response. This vector must be organized by subject, and by time within subject ((sum(Tvec)) x 1).
`subjectID`	The vector of subject ID values for each response ((sum(Tvec)) x 1).
`Zmat`	The design matrix for time-independent covariates ((sum(Tvec)) x K0).
`Xmat`	The design matrix for time-dependent covariates ((sum(Tvec)) x Ktv).
`Tvec`	The vector of times for each subject.
`N`	The number of subjects.
`mc`	The method of identifying appropriate moment conditions, either 'EC' for extended classification (default) or 'Types' for user-identified types.
`covTypeVec`	The vector indicating the type of each time-dependent covariate, according to the order of the columns of Xmat.
`betaI`	The initial parameter estimates, from hurdle GEE with independent working correlation structure. r_l The vector of residuals from hurdle GEE with independent working correlation structure.