by Sophia Rabe-Hesketh
cme is a wrapper for
gllamm to estimate generalized linear models
with covariate measurement error by maximum likelihood using
The covariate measurement error model
comprises three submodels:
the outcome model, the measurement model and the true covariate model.
The outcome model is a generalized linear model including both observed
covariates and the true, unobserved or latent covariate.
The measurement model assumes that the continuous repeated measurements
of the true covariate are independently normally distributed
with mean equal to the true covariate and constant variance
(classical measurement model).
The true covariate model is a linear regression of the true covariate
on the observed covariates. Use
to use a different set of observed covariates in the true covariate
model than in the outcome model.
commands option causes
to print out all data manipulation commands and the
gllamm command for estimating the model.
gllamm itself can then
be used to extend
the model or to make predictions or simule data
Reference: Rabe-Hesketh, Skrondal, and Pickles (2003). Maximum likelihood estimation
of generalized linear models with covariate measurement error. The Stata Journal 3 (4), 385-410.
The command requires Stata 8 or later (available from Stata Corporation)
and the latest version of
gllamm (see here for
cme can be installed from Statistical Software Components (SSC):
. ssc describe cme
. ssc install cme
. ssc install cme, replace /* to replace previous version */
or downloaded directly from here: cme.ado, cme.hlp