KlostermanFit: A function to fit a double logisitic function to a vector...

In phenopix: Process Digital Images of a Vegetation Cover

Description

This function fits a double logistic curve to observed values using the function as described in klosterman et al. (2014), eq 7.

Usage

KlostermanFit(ts, which = "light", 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
which	A character to be chosen between 'light' (default) and 'heavy'. See details.
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 Klosterman et al. 2014. Two optimization procedures are available. If which='light' (the default) equation parameters are optimized using the function optim and computation is faster, whereas if which='heavy' the optimization procedure calls the function nsl and is based on a greater number of iteractions with different first guesses for parameters. This option is about ten times slower than the light one.

Value

A list containing the following items.

fit	A list as returned by the function FitDoubleLogGu
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

Klosterman ST, Hufkens K, Gray JM, Melaas E, Sonnentag O, Lavine I, Mitchell L, Norman R, Friedl MA, Richardson A D (2014) Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery, Biogeosciences, 11, 4305-4320, doi:10.5194/bg-11-4305-2014.

See Also

FitDoubleLogKlLight FitDoubleLogKlHeavy

Examples

## Not run: 
library(zoo)
data(bartlett2009.filtered)
## fit without uncertainty estimation
fitted.kl1 <- KlostermanFit(bartlett2009.filtered, which='light')
fitted.kl2 <- KlostermanFit(bartlett2009.filtered, which='heavy')
## check fitting
plot(bartlett2009.filtered)
lines(fitted.kl1$fit$predicted, col='red')
lines(fitted.kl2$fit$predicted, col='blue')
legend('topleft',col=c('red', 'blue'), lty=1, 
  legend=c('light', 'heavy'), bty='n')

## End(Not run)

phenopix documentation built on May 2, 2019, 4:50 p.m.