Description Usage Arguments Value Author(s) References See Also Examples
This function fits a double logistic curve to observed values using the function as described in Beck et al. (2006) (equation 3).
1 2 3 4 5 |
x |
vector or time series to fit |
t |
time steps |
tout |
time steps of output (can be used for interpolation) |
weighting |
apply the |
hessian |
compute standard errors of parameters based on the Hessian? |
plot |
|
ninit |
number of inital parameter sets from which to start optimization |
... |
further arguments (currently not used) |
The function returns a list with fitted values, parameters, fitting formula and standard errors if hessian
is TRUE
Matthias Forkel <matthias.forkel@tu-dresden.de> [aut, cre]
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # select one year of data
x <- as.vector(window(ndvi, start=c(1994,1), end=c(1994, 12)))
plot(x)
# fit double-logistic function to one year of data
fit <- FitDoubleLogBeck(x)
lines(fit$predicted, col="blue")
# # do more inital trials, plot iterations and compute parameter uncertainties
# FitDoubleLogBeck(x, hessian=TRUE, plot=TRUE, ninit=100)
#
# # fit double-logistic function to one year of data,
# # interpolate to daily time steps and calculate phenology metrics
# tout <- seq(1, 12, length=365) # time steps for output (daily)
# fit <- FitDoubleLogBeck(x, tout=tout)
# PhenoDeriv(fit$predicted, plot=TRUE)
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