SplineFit: A function to fit a smoothed spline to Phenological Data
In phenopix: Process Digital Images of a Vegetation Cover

Description Usage Arguments Details Value Author(s) Examples

A function to fit a smoothed spline to Phenological Data

1 2	SplineFit(ts, uncert = FALSE, nrep = 100, df.factor = 0.05, ncores='all', sf=quantile(ts, probs=c(0.05, 0.95), na.rm=TRUE))

`ts`	A ts or zoo object with gcc data. index(ts) must be numeric days of year (doys)
`uncert`	Should uncertainty be estimated?
`nrep`	Number of relications to estimate uncertainty, defaults to 100.
`df.factor`	Defaults to 0.05, it is multiplied by length(ts) to generate degrees of freedom for the spline fitting. The higher the number of data, the higher should be df factor. For a complete year of data (i.e. length(ts)=365) the default value is optimum.
`ncores`	Unused argument for compatibility
`sf`	Scaling factors required to normalize the data prior to the fitting. If the function is called by e.g. `greenProcess` sf is automatically calculated.

This function fits a smoothed spline to the data. Df for smoothing are set at 0.05*length(ts) by default and df.factor can be modified. Uncertainty is estimated by changing the degrees of freedom of the spline. In particular a sequence from 0.01 and df.factor, of length nrep is used as varying degrees of freedom for the spline fitting.

A list containing the following items.

`fit`	A list with fitted values and an object named 'params' set to NULL, for simmetry with other fittings
`uncertainty`	A list containing a zoo data.frame with the uncertainty predicted values, and an object named 'params' set to NULL, for simmetry with other fittings

Gianluca Filippa <gian.filippa@gmail.com>