fun.glssubi

vector of the response for the ith subject

matrix of the covariates for the ith subject

maxT

the vector of unique parameters in the variance-covariance matrix for the error term in the GLS model for Y

the form of within-subject correlation structure in the GLS model for Y

ycorr

logical indicating wether or not the parameter in D is transformed.

transform

vector of predicted probabilities of being observed for all the observations from the ith subject

gfiti

matrix of 3 columns of predicted transitional probabilities for the missing observations from the ith subject.

Afiti

1: calculated nabla11_i; 2: calculate nabla12_i

case

Calculate subject-level quantities when the regression outcome is subject to missingness and follows generalized least sqaures models (GLS)

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 linear mixed model for 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 more details, see Troxel Ma and Heitjan (2004) and Xie and Heijan (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>.

fun.glssubi: Internal Function for various subject-level computation required for ISNIGLS.

Description

Usage

Arguments