The work presented in this dissertation centers on the theme of regression and
computation methodology. Functional data is an important class of longitudinal
data, and principal component analysis is an important approach to regression with
this type of data. Here we present an additive hierarchical bivariate functional data
model employing principal components to identify random e ects. This additive
model extends the univariate functional principal component model. These models
are implemented in the pfda package for R. To t the curves from this class of models
orthogonalized spline basis are used to reduce the dimensionality of the t, but retain
exibility. Methods for handing spline basis functions in a purely analytical manner,
including the orthogonalizing process and computing of penalty matrices used to t
the principal component models are presented. The methods are implemented in the
R package orthogonalsplinebasis.
The projects discussed involve complicated coding for the implementations in R.
To facilitate this I created the NppToR utility to add R functionality to the popular
windows code editor Notepad . A brief overview of the use of the utility is also
included.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2010-08-8290 |
| Date | 2010 August 1900 |
| Creators | Redd, Andrew Middleton |
| Contributors | Carroll, Raymond J. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | thesis, text |
| Format | application/pdf |
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