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Abstract Details
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Activity Number:
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310
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Graphics
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| Abstract - #301956 |
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Title:
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Diagnostic Tools for Hierarchical Linear Models
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Author(s):
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Adam M. Loy*+
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Companies:
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Iowa State University
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Address:
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2414 Snedecor Hall, Ames, IA, 50011,
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Keywords:
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diagnostics ;
influential points ;
hierarchical linear models ;
R packages
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Abstract:
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Numerous familiar diagnostic tools exist for the linear regression model that enable us to check model assumptions and identify influential observations that might distort parameter estimates, predictions, and the precision of both. Less known are diagnostic measures for hierarchical linear models. Hierarchical linear models do not assume independence between data points, violating the usual modeling assumptions. Observations can be influential at multiple levels of a model. In the R package HLMdiag we have implemented residual analysis and case-deletion diagnostics for checking the model assumptions and detecting influential points in the hierarchical linear model. In particular we will emphasize graphical techniques for model checking and the detection and investigation of influential observations or groups of observations. We will present the functionality of these procedures using case studies.
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