Abstract
The previous two chapters have introduced the Matlab and R code needed to specify basis function systems and then to define curves by combining these coefficient arrays. For example, we saw how to construct a basis object such as heightbasis to define growth curves and how to combine it with a matrix of coefficients such as heightcoef so as to define growth functional data objects such as were plotted in Figure 1.1.
We now turn to methods for computing these coefficients with more careful consideration of measurement error. For example, how do we compute these coefficients to obtain an optimal fit to data such as the height measurements for 54 girls in the Berkeley growth study stored in the 31 by 54 matrix that we name heightmat? Or how do we replace the rather noisy mean daily precipitation observations by smooth curves?
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2009 Springer-Verlag New York
About this chapter
Cite this chapter
Ramsay, J., Hooker, G., Graves, S. (2009). Smoothing: Computing Curves from Noisy Data. In: Functional Data Analysis with R and MATLAB. Use R. Springer, New York, NY. https://doi.org/10.1007/978-0-387-98185-7_5
Download citation
DOI: https://doi.org/10.1007/978-0-387-98185-7_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98184-0
Online ISBN: 978-0-387-98185-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)