fun.generalmat

sigma

obsi

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

transform

1: return nobs x nobs general symmetric matrix; 2: return a list for first derivatives of the general symmetric matrix wrt D;
3: return an array containing 2nd derivatives of the general symmetric matrix wrt D.

case

indicator for variance and covariance parameters.

case2

Calculate the general symmetric matrix and its first and second derivatives wrt D.

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.generalmat: Internal Function for generate a general symmetric matrix and its first and second derivatives wrt D

Description

Usage

Arguments