tafa.control: Set parameters for LOESS and K-fold cross-validation.
In arkansas-water-center/TAFA: Trend Analysis with Flow Adjustment

Description Usage Arguments Details References See Also

Set parameters for LOESS and the K-fold cross-validation procedure in tafa.

1 2	tafa.control(degree = 1, folds = 10, iteration = 10, loss = c("MAD", "MSE"), setspan = NULL)

`degree`	The degree of the locally-fitted polynomial to use. Default is 1 (i.e., linear)
`folds`	The number of random partitions (folds) to use in K-fold CV. Default it 10.
`iteration`	The number of repetitions of K-fold CV to perform. Default is 10.
`loss`	The loss function (prediction error metric from the fit) to evaluate. Default is mean absolute deviation (MAD); alternative is mean squared error (MSE).
`setspan`	Optional; can define the smoothing parameter for LOESS to use and bypass the K-fold CV. Values can be greater than 0 and <= 1.

This control function is akin to how loess.wrapper is used with loess. Here, certain parameters of loess are set as well as specifics of the K-fold cross-validation (CV) procedure. Repeated K-fold CV is used by tafa to estimate f_opt, the optimized smoothing parameter for LOESS. For details on K-fold CV, I recommend Kohavi (1995).

10 iterations of 10-fold CV is the default setting for tafa, which performs well with water quality data (Simpson and Haggard, 2016). The user has the option to specify mean squared error (MSE) as the loss function of prediction though it may too harshly penalize certain fits due to the squared error term. Mean absolute deviation (MAD) seems to perform better (and so it's the default), though it has not been fully studied (there is extensive discussion in the literature on when various loss functions are more appropriate to use).

The user may also specify the smoothing parameter for LOESS to use via the setspan argument. Past water quality studies have defaulted to using a value of 0.5, which generally performs well (simpson and Haggard, 2016).

Kohavi, R. 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence 14(2): 1137-1145.

Simpson, Z.P. and B.E. Haggard. 2016. An optimized procedure for flow-adjustment of constituent concentrations for trend analysis. In preparation.

loess, tafa

arkansas-water-center/TAFA documentation built on May 10, 2019, 1:28 p.m.