We describe a method to estimate associations between random effects from multilevel models. We provide two new postestimation commands, reffadjustsim and reffadjust4nlcom, which are distributed as the reffadjust package. These commands produce the estimates and their associated confidence intervals. The commands are used after official Stata multilevel model estimation commands mixed, meqrlogit, and meqrpoisson (formerly named xtmixed, xtmelogit, and xtmepoisson, respectively, before Stata 13) and with models fit in the MLwiN statistical software package via the runmlwin command. We demonstrate our commands with several simulated datasets and for a bivariate outcome model investigating the relationship between weight and mean arterial pres...
An in-depth investigation on maximum likelihood estimators for variance components is proposed, wher...
The command mundlak estimates random-effects regression models (xtreg, re) adding group-means of var...
This chapter is devoted to regression models for ordinal responses with special emphasis on random e...
We describe a method to estimate associations between random effects from multilevel models. We prov...
We describe a method to estimate associations between random effects from multilevel models. We prov...
Estimating adjusted associations between random effects from multilevel models : the reffadjust pack...
We illustrate how to fit multilevel models in the MLwiN package seamlessly from within Stata using t...
Below we discuss random-intercept and random-slope models in the context of multilevel mod-els, and ...
Summary. Multilevel or mixed effects models are commonly applied to hierarchical data. The level 2 r...
The shared-parameter model and its so-called hierarchical or random-effects extension are widely use...
We illustrate how to fit multilevel models in the MLwiN package seamlessly from within Stata using t...
within schools, voters within districts, or workers within firms, to name a few exam-ples. Statistic...
Random intercept variances and model fit statistics comparison of multilevel mixed effect multinomia...
Availability of large multilevel longitudinal databases in various fields of research, including lab...
xthybrid estimates generalized linear mixed models that split the effects of cluster-varying covaria...
An in-depth investigation on maximum likelihood estimators for variance components is proposed, wher...
The command mundlak estimates random-effects regression models (xtreg, re) adding group-means of var...
This chapter is devoted to regression models for ordinal responses with special emphasis on random e...
We describe a method to estimate associations between random effects from multilevel models. We prov...
We describe a method to estimate associations between random effects from multilevel models. We prov...
Estimating adjusted associations between random effects from multilevel models : the reffadjust pack...
We illustrate how to fit multilevel models in the MLwiN package seamlessly from within Stata using t...
Below we discuss random-intercept and random-slope models in the context of multilevel mod-els, and ...
Summary. Multilevel or mixed effects models are commonly applied to hierarchical data. The level 2 r...
The shared-parameter model and its so-called hierarchical or random-effects extension are widely use...
We illustrate how to fit multilevel models in the MLwiN package seamlessly from within Stata using t...
within schools, voters within districts, or workers within firms, to name a few exam-ples. Statistic...
Random intercept variances and model fit statistics comparison of multilevel mixed effect multinomia...
Availability of large multilevel longitudinal databases in various fields of research, including lab...
xthybrid estimates generalized linear mixed models that split the effects of cluster-varying covaria...
An in-depth investigation on maximum likelihood estimators for variance components is proposed, wher...
The command mundlak estimates random-effects regression models (xtreg, re) adding group-means of var...
This chapter is devoted to regression models for ordinal responses with special emphasis on random e...