A function to fit a double logistic function to a vector according to Beck et al. (2006)
This function fits a double logistic curve to observed values using the
function as described in Beck et al. (2006) (equation 3). It can also
provide and uncertainty estimation. Rather internal function.
See greenProcess
BeckFit(ts, uncert = FALSE, nrep = 100, 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) or a POSIXct vector |
uncert |
Should uncertainty be estimated? |
nrep |
Number of relications to estimate uncertainty, defaults to 1000. |
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. |
The function estimates parameters of the double logistic equation from
Beck et al. 2006 and provides an uncertainty estimation. Parameters are
estimated by a call to the function FitDoubleLogBeck 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.
A list containing the following items.
fit |
A list as returned by the function FitDoubleLogBeck |
uncertainty |
A list containing a zoo data.frame with the uncertainty predicted values, and a dataframe containing the respective uncertainty curve parameters |
Gianluca Filippa <gian.filippa@gmail.com>
Beck, P.S.A., C. Atzberger, K.A. Hodga, B. Johansen, A. Skidmore (2006): Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. - Remote Sensing of Environment 100:321-334.
## Not run: library(zoo) data(bartlett2009.filtered) ## fit without uncertainty estimation fitted.beck <- BeckFit(bartlett2009.filtered) days <- as.numeric(format(index(bartlett2009.filtered), ' plot(days, bartlett2009.filtered) lines(fitted.beck$fit$predicted, col='red') ## look at fitting parameters fitted.beck$fit$params ## look at fitting equation, where t is time fitted.beck$fit$formula ## End(Not run)
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