| FitDoubleLogElmore {greenbrown} | R Documentation |
This function fits a double logistic curve to observed values using the function as described in Elmore et al. (2012) (equation 4).
FitDoubleLogElmore(x, t = 1:length(x), tout = t, hessian = FALSE,
plot = FALSE, ninit = 100, ...)
x |
vector or time series to fit |
t |
time steps |
tout |
time steps of output (can be used for interpolation) |
hessian |
compute standard errors of parameters based on the Hessian? |
plot |
plot iterations for logistic fit? |
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@geo.tuwien.ac.at> [aut, cre]
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.
# select one year of NDVi data data(ndvi) x <- as.vector(window(ndvi, start=c(1991,1), end=c(1991, 12))) plot(x) # fit double-logistic function to one year of data fit <- FitDoubleLogElmore(x) fit plot(x) lines(fit$predicted, col="blue") # do more inital trials, plot iterations and compute parameter uncertainties FitDoubleLogElmore(x, hessian=TRUE, plot=TRUE, ninit=1000) # fit double-logistic function to one year of data, # interpolate to daily time steps and calculate phenology metrics tout <- seq(1, 12, length=365) # time steps for output (daily) fit <- FitDoubleLogElmore(x, tout=tout) plot(x) lines(tout, fit$predicted, col="blue") PhenoDeriv(fit$predicted, plot=TRUE)