MMLongit: Function used to fit marginalized models.
In mercaldo/MMLB: Marginalized Models for Longitudinal Binary Data

Description Usage Arguments Value Author(s) References See Also

Main function used to fit marginalized models. See mm() for a more user friendly function.

MMLongit(params, id, X, Y, Xgam, Xsig, Q, condlike = FALSE, 
         sampprobs = matrix(1, ncol=3, nrow=length(Y)), 
         sampprobi = rep(1, length(Y)), offset = 0, stepmax = 1, 
         steptol = 1e-06, hess.eps = 1e-07, AdaptiveQuad = FALSE, 
         verbose = FALSE,iterlim = 100)

`params`	a vector of initial values.
`id`	a vector of cluster identifiers.
`X`	a design matrix, including intercept, for the mean formula.
`Y`	a binary vector
`Xgam`	a design matrix for the transition formula.
`Xsig`	a design matrix for the latent variable formula.
`Q`	a scalar denoting the number of quadrature points.
`condlike`	indicator to denote if the conditional likelihood should be maximized.
`sampprobs`	a matrix of sampling probabilities. See mm().
`sampprobi`	a vector of sampling probabilities. This should be generally be 1.
`offset`	an optional offset term.
`stepmax`	a scalar.
`steptol`	a scalar.
`hess.eps`	a scalar.
`AdaptiveQuad`	an indicator if adaptive quadrature is to be used. NOT CURRENTLY IMPLEMENTED.
`verbose`	an indicator if model output should be printed to the screen during maximization (or minimization of negative log-likelihood). See nlm print.level.
`iterlim`	a scalar to denote the maximum iteration limit used by nlm. Default value is 100.

This function returns marginal parameters (beta) and dependence parameters (alpha) along with the associated covariance matricies.

Jonathan Schildcrout

Schildcrout, JS and Heagerty, PJ. (2007), Marginalized Models for Moderate to Long Series of Longitudinal Binary Response Data. Biometrics, 63: 322-331.

mm

mercaldo/MMLB documentation built on May 22, 2019, 6:51 p.m.