Re: [R] Course***Dr Frank Harrell's Regression Modeling Strategies in R/Splus course *** September 2006 near you (San Francisco, Washington DC, Atlanta)

2006-09-13 Thread paul king
Anyone from Chicago area interested in this course? Please email XLSolutions so 
they can schedule it in Chicago. 
We ran out of travel budget in my company  :(


Date: Wed, 2 Aug 2006 13:20:23 -0700From: [EMAIL PROTECTED]: [S] Course***Dr 
Frank Harrell's Regression Modeling Strategies in R/Splus course *** September 
2006 near you (San Francisco, Washington DC, Atlanta)To: [EMAIL PROTECTED]
XLSolutions Corporation (www.xlsolutions-corp.com) is very proud to announce Dr 
Frank Harrell's Regression Modeling Strategies in R/Splus this September 2006. 
http://xlsolutions-corp.com/Rstats2.htm
 
*** San Francisco, CA  / August 31st  - September 1st, 2006 ***
*** Atlanta, GA  / September 18th - 19th, 2006 ***
*** Washington, DC /  September 28th - 29th, 2006 ***
 
Please ask for group discount and reserve your seat Now - Earlybird Rates.Note 
that payment is due after the class! Email Sue Turner:  [EMAIL PROTECTED]
 
http://xlsolutions-corp.com/Rstats2.htm
 
This two-day course is designed for persons interested in multivariable 
regression analysis of univariate responses, in developing, validating, and 
graphically describing multivariable predictive models. The first part of the 
course presents the following elements of multivariable predictive modeling for 
a single response variable: using regression splines to relax linearity 
assumptions, perils of variable selection and overfitting, where to spend 
degrees of freedom, shrinkage, imputation of missing data, data reduction, and 
interaction surfaces. Then a default overall modeling strategy will be 
described. This is followed by methods for graphically understanding models 
(e.g., using nomograms) and using re-sampling to estimate a model's likely 
performance on new data. Then the freely available S-Plus Design library will 
be overviewed. Design facilitates most of the steps of the modeling process. 
Next, statistical methods related to binary logistic models will be covered. 
Thre!
 e of the following case studies will be presented: an exploration of voting 
tendencies over U.S. counties in the 1992 presidential election, an interactive 
exploration of the survival status of Titanic passengers, an interactive case 
study in developing a survival time model, and a case study in Cox regression. 
In the hands-on computer lab students will develop, validate, and graphically 
describe multivariable regression models themselves. This short course will 
also survey the advantages of modeling in randomized trials. The methods 
covered in this course will apply to almost any regression model, including 
ordinary least squares, logistic regression models, and survival models. 
 
Course Outline: http://xlsolutions-corp.com/Rstats2.htm
 
- Planning for Modeling, Covariable Adjustment.  - Notation for Regression 
Models, Interpreting Model Parameters.  - Relaxing Linearity Assumption for 
Continuous Predictors; Splines for Estimating Shape of Regression Function and 
Determining Predictor Transformations, Cubic Spline Functions, Advantages of 
Splines over Other Methods.  - Tests of Association, Assessment of Model Fit; 
Regression Assumptions Modeling and Testing Interactions.  - Missing Data; 
Strategies for Developing Imputation Algorithms , software for Fitting Models 
and Adjusting Variances for Multiple Imputation.  - Multivariable Modeling 
Strategy; Pre-Specification of Predictor Complexity ,Variable Selection 
,Overfitting and Limits on Number of Predictors, Shrinkage, Data Reduction.  - 
Resampling, Validating, Describing, and Simplifying the Model; The Bootstrap, 
Model Validation , Graphically Describing the Fitted Model, Simplifying the 
Model by Approximating It.  - Design library  - Binary Logistic Regress!
 ion, Interactive Case Study: Binary Logistic Model for Survival of Titanic 
Passengers.  - Interactive Case Study: Development of a Long-Term Survival 
Model for Critically Ill Patients.  - Cox Proportional Hazards Model, Case 
Study in Cox Regression.  - Case Study using Least Squares Multiple Regression: 
Voting Patterns in U.S. Counties.  
 
Email us for group discounts.Email Sue Turner: [EMAIL PROTECTED]: 
206-686-1578Visit us: http://xlsolutions-corp.com/Rstats2.htmPlease let us know 
if you and your colleagues are interested in thisclass to take advantage of 
group discount. Register now to secure yourseat! Cheers,Elvis Miller, 
PhDManager Training.XLSolutions Corporation206 686 [EMAIL PROTECTED]
 
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[R] Course***Dr Frank Harrell's Regression Modeling Strategies in R/Splus course *** September 2006 near you (San Francisco, Washington DC, Atlanta)

2006-08-02 Thread elvis

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R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.