Abstract
In Chapter 3, we presented methods to process samples, estimate parameters, and construct confidence intervals for design-based inference. In this chapter, we present model-based imputation (to fill in missing values) and interpolation (for predicting values at unsampled locations) methods to generate complete datasets so that 1) we have no missing values in our analysis dataset or so that 2) we have complete coverage using predicted values at unsampled locations for some variable of interest.
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© 2011 Springer Science+Business Media, LLC
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Robinson, A., Hamann, J. (2011). Imputation and Interpolation. In: Forest Analytics with R. Use R. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7762-5_4
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DOI: https://doi.org/10.1007/978-1-4419-7762-5_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4419-7761-8
Online ISBN: 978-1-4419-7762-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)