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Below is the sample data (out of approximately 8000 rows of data). How can I replace all NAs with values from a smoothing spline fit to the rest of the data?
Date Max Min Rain RHM RHE
4/24/1981 35.9 24.7 0.0 71 37
4/25/1981 36.8 22.8 0.0 62 40
4/26/1981 36.0 22.6 0.0 47 37
4/27/1981 35.1 24.2 0.0 51 39
4/28/1981 35.4 23.8 0.0 61 47
4/29/1981 35.4 25.1 0.0 67 43
4/30/1981 37.4 24.8 0.0 72 34
5/1/1981 NA NA NA NA NA
5/2/1981 39.0 25.3 NA NA 55
5/3/1981 35.9 23.0 0.0 68 66
5/4/1981 28.4 22.4 0.7 70 30
5/5/1981 35.5 24.6 0.0 47 31
5/6/1981 37.4 25.5 0.0 51 31
356
Smoothing Splines as a Blending Method Smoothing splines are useful in smoothing noisy time series. The degree of smoothing is controlled by a single parameter, which plays a role much like the K value in Bühlmann credibility.
The most common case considered is k = 3, i.e., that of cubic splines. These are piecewise cubic functions that are continuous, and have continuous first, and second derivatives. Note that the continuity in all of their lower order derivatives makes splines very smooth.
One thing to check out might be the na.spline function in the zoo package. It appears custom designed for this purpose.
Missing values (NAs) are replaced by linear interpolation via approx or cubic spline interpolation via spline, respectively.
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