Courses in Applied Linear Mixed Models and Applied Generalized Linear
Mixed Models are being offered March 8-9 and March 10-11,
respectively, at the University of Florida. The cost of each workshop
is $500 - accept cash or check (payable to University of Florida). To
register, pease contact Marilyn Marlow at
<mailto:[email protected]>[email protected] or 352.392.1946. Details of the
workshops are provided below.
Applied Linear Mixed Models
Course Description
Analysis of data from designed experiments and observational studies
often involve both fixed and random effects; that is, the studies
have mixed effects. Perhaps the simplest mixed effects model is that
for a randomized complete block design. As is the case for all mixed
models, the model for the randomized complete block design has an
underlying covariance structure associated with the response
variable. This covariance of the response is generally partitioned
into the covariance matrix G assocated with the modeled random
effects and the covariance structure of the residuals, R. Given a set
of data, determining the appropriate structure for these covariance
matrices is both important and challenging. This short course will
focus on the process of modeling G and R using real data sets. Topics
include a review of covariance structures available in SAS, using the
estimated covariance matrix to guide in the choice of covariance
structure, accounting for spatial variation in either covariates or
errors, and incorporating radial smoothing in the analysis. Methods
for asessing the fit of the model and new multiple comparison methods
will be presented. This two-day workshop will introduce participants
to SAS's PROC MIXED and PROC GLIMMIX. Participants will have the
opportunity to analyze data sets that illustrate the methods
discussed during the class. Extensive use will be made of real-data
sets throughout the course.
Who should come
This workshop is targeted toward those who have some experience with
design of experiments and who want to learn more about mixed models.
After this workshop, participants should be able to analyze normal
data arising from studies with mixed effects.
Instructor
Dr. Linda J. Young, Professor in UF's Department of Statistics, has
consulted with researchers on the faculties of Oklahoma State
University, University of Nebraska, and University of Florida. Her
research interests are in sampling and modeling of ecological and
environmental data.
Applied Generalized Linear Mixed Models
Course Description
Traditional statistical methods largely assume that data are normally
distributed. However, not all data are normally distributed.
Generalized linear models are simply linear models that have been
extended for the analysis of non-normal data with only fixed effects.
Generalized linear mixed models are designed to analyze data that are
not normally distributed and have both fixed and random effects. This
course begins by motivating the move from linear models to
generalized linear models and includes a discusssion of the
comparison of the analysis of data that have been transformed so that
the assumption of normality is more nearly met and the analysis of
data using generalized linear models. Logistic and Poisson regression
will be discussed in the context of generalized linear models. Then
after a quick review of the difference in fixed and random effects,
generalized linear mixed models will be presented. All generalized
linear mixed models have an underlying covariance structure
associated with the response variable. This covariance of the
responses is partitioned into the covariance matrix G associated with
the modeled random effects and the covariance matrix R associated
with the errors. How to determine an approrpiate covariance structure
for a model is both challenging and important. The various covariance
structures and how to choose from among them when analyzing data will
be reviewed. The challenges that arise with generalized linear mixed
models that are not present with linear mixed models will be
discussed, including the differences in marginal and conditional
models (and why you should care), prediction on the data scale versus
the scale of the analysis, and assessing the fit of the model. The
use of spatial covariance functions and radial smoothing to account
for the covariance structure will be presented. The ability to have
multiple response variables with possibly different distributions is
another aspect of GLIMMIX that will be explored. Students will have
the opportunity to practice the analysis of generalized linear mixed
models. Extensive use will be made of real-data sets throughout the course.
Who should come
This workshop is targeted toward those who are familiar with PROC
MIXED and who want to learn how to extend this to use mixed models
when the data are non-normal. After this workshop, participants
should be able to analyze non-normal data arising from studies with
mixed effects.
Instructor
Dr. Linda J. Young, Professor in UF's Department of Statistics, has
consulted with researchers on the faculties of Oklahoma State
University, University of Nebraska, and University of Florida. Her
research interests are in sampling and modeling of ecological and
environmental data.
