tmdm

a formula to specify a multinomial transitional missing data model in the form of g+gp ~Sterms.

formula

the name of the dataset for fitting missing data mechansim model

data

an optional vector of "prior weights" to be used in the fitting process for the missingness mechanism model.
Should be NULL or a numeric vector.

weights

an optional vector specifying a subset of observations to be used in the fitting process for the outcome model and the missingness mechanism model.

subset

Fit missing data model and obtain predicted probabilities of being observed for all observations.

The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the mixed-effects models for continuous and binary longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random is with respect to the assumption of ignorability. For a tutorial, download at <https://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and Xie (2008) <doi:10.1002/sim.3117> and Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.

tmdm: Function to fit the transitional missing data model and obtain the predicted probabilities of being observed for all observations.

Description

Usage

Arguments

Examples