phenopix (version 2.4.5)

GuFit: Fit a double logistic equation to a time series according to Gu et al. (2003)

Description

This function fits a double logistic curve to observed values using the function as described in Gu et al. (2003).

Usage

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

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

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.

Author

Gianluca Filippa <[e-mail]>

Details

The function estimates parameters of the double logistic equation from Gu et al. 2009 and provides an uncertainty estimation. Parameters are estimated by a call to the function FitDoubleLogGu. 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.

References

Gu L, Post WM, Baldocchi D, Black TA, Suyker AE, Verma SB, Vesala T, Wofsy SC. (2009) Characterizing the Seasonal Dynamics of Plant Community Photosynthesis Across a Range of Vegetation Types. In: Phenology of Ecosystem Processes (Ed: Noormets A, Springer New York), pp 35-58.

See Also

FitDoubleLogGu

Examples

if (FALSE) {
library(zoo)
data(bartlett2009.filtered)
## fit without uncertainty estimation
fitted.gu <- GuFit(bartlett2009.filtered)
## convert 
plot(bartlett2009.filtered)
lines(fitted.gu$fit$predicted, col='red')
## look at fitting parameters
fitted.gu$fit$params
## look at fitting equation, where t is time
fitted.gu$fit$formula
}