From cathie@statsci.com  Tue Nov 30 11:19:52 1999
Date: Tue, 30 Nov 1999 09:58:30 -0800 (PST)
From: Cathie Lynch <cathie@statsci.com>
Subject: SPLUS ADVANCED MIXED EFFECT MODELS COURSE!!!
To: undisclosed-recipients:;

Hello,

Mathsoft is offering the Advanced Models course.

Title:  Analyzing Mixed-Effects Models with S-PLUS
Instructor: Jose' C. Pinheiro - Bell Labs, Lucent Technologies

Date:   February 17-18, 2000
Site:   San Francisco, CA

Cost:   $1050   Commercial
        $950    Government/Academic

                               ABSTRACT

Mixed-effects models provide a powerful tool for analyzing grouped
data, because they model flexibly the within-group correlation often
present in this type of data. Examples of grouped data include
repeated measures, longitudinal data, multilevel data, and some
split-plot experiments, being collected in a wide variety of areas.
This course describes how to fit and analyze linear and nonlinear
mixed-effects models in S-PLUS, using the recently released NLME 3.2
software. This is an applied course, with emphasis on real life
examples to motivate and illustrate the use of mixed-effects models.


                               OUTLINE

1) Introduction
  - motivating examples of linear and non-linear mixed-effects models
  - a sample of S-PLUS functions and methods for fitting and
    analyzing mixed-effects models.

2) Grouped data
  - repeated measures data, longitudinal data, growth curve data
  - multilevel data
  - Data structures and Trellis displays for grouped data in S-PLUS

3) Fitting linear mixed-effects models
  - the linear mixed-effects (LME) model
  - using the lme function in S-PLUS
  - assessing the adequacy of an LME fit
  - confidence intervals, hypothesis tests, and predictions
  - fitting LME models to multilevel data

4) Extending the basic LME model
  - modeling the random effects covariance structure
  - variance functions to model unequal within-group variances
  - serial and spatial within-group correlation structures
  - an extended linear model with no random effects: the gls function

5) Fitting nonlinear mixed-effects models
  - brief review of nonlinear regression for independent observations
  - obtaining starting estimates: self-starting regression functions
  - nonlinear mixed-effects (NLME) models for grouped data
  - the nlme function in S-PLUS
  - assessing the adequacy of an NLME fit
  - confidence intervals, hypothesis tests, and predictions
  - extending the basic NLME model: variance functions and correlation
    structures
  - an extended nonlinear regression model with no random effects:
    the gnls function


Site Information:

Serrano Hotel
405 Taylor Street
San Francisco, CA  94102
415-885-2500
http://www.serranohotel.com