This function fits a double logistic curve to observed values using the function as described in Elmore et al. (2012) (equation 4). It can also provide and uncertainty estimation.
ElmoreFit(ts, uncert = FALSE, nrep = 100, ncores='all',
sf=quantile(ts, probs=c(0.05, 0.95), na.rm=TRUE))A list containing the following items.
A list as returned by the function FitDoubleLogElmore
A list containing a zoo data.frame with the uncertainty predicted values, and a dataframe containing the respective uncertainty curve parameters
A ts or zoo object with gcc data. index(ts) must be numeric days of year (doys) or a POSIXct vector
Should uncertainty be estimated?
Number of relications to estimate uncertainty, defaults to 100.
Number of processors to be used in parallel computation, defaults to 'all' which will accidentally slow down any other activity on your computer. Otherwise set the number of processors you want to use in parallelization.
Scaling factors required to normalize the data prior to the fitting. If the function is called by e.g. greenProcess sf is automatically calculated.
Gianluca Filippa <gian.filippa@gmail.com>
The function estimates parameters of the double logistic equation from
Elmore et al. 2012 and provides an uncertainty estimation. Parameters are
estimated by a call to the function FitDoubleLogElmore from the
greenbrown package. Uncertainty is computed by adding noise to the raw
data and by estimating again the parameters. Noise is added according
to the standard deviation of the residuals (fitted - observed).
The procedure is repeated nrep times.
Elmore, A.J., S.M. Guinn, B.J. Minsley and A.D. Richardson (2012): Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. - Global Change Biology 18, 656-674.
FitDoubleLogElmore