ElmoreFit {phenopix}

Fit a double logistic equation to a time series according to Elmore et al. (2012)

Description

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.

Usage

ElmoreFit(ts, uncert = FALSE, nrep = 100, ncores='all', 
sf=quantile(ts, probs=c(0.05, 0.95), na.rm=TRUE))

Arguments

ts

A ts or zoo object with gcc data. index(ts) must be numeric days of year (doys) or a POSIXct vector

uncert

Should uncertainty be estimated?

nrep

Number of relications to estimate uncertainty, defaults to 100.

ncores

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.

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.

Details

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.

Value

A list containing the following items.

fit

A list as returned by the function FitDoubleLogElmore

uncertainty

A list containing a zoo data.frame with the uncertainty predicted values, and a dataframe containing the respective uncertainty curve parameters

Author(s)

Gianluca Filippa <[e-mail]>

References

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.

See Also

FitDoubleLogElmore

Examples

## Not run: 
library(zoo)
data(bartlett2009.filtered)
## fit without uncertainty estimation
fitted.elmore <- ElmoreFit(bartlett2009.filtered)
days <- as.numeric(format(index(bartlett2009.filtered), '
plot(days, bartlett2009.filtered)
lines(fitted.elmore$fit$predicted, col='red')
## look at fitting parameters
fitted.elmore$fit$params
## look at fitting equation, where t is time
fitted.elmore$fit$formula

## End(Not run)