FitDoubleLogElmore {greenbrown} R Documentation

## Fit a double logisitic function to a vector according to Elmore et al. (2012)

### Description

This function fits a double logistic curve to observed values using the function as described in Elmore et al. (2012) (equation 4).

### Usage

```FitDoubleLogElmore(x, t = 1:length(x), tout = t, hessian = FALSE,
plot = FALSE, ninit = 100, ...)```

### Arguments

 `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)

### Value

The function returns a list with fitted values, parameters, fitting formula and standard errors if hessian is TRUE

### Author(s)

Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]

### References

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.

`TSGFdoublelog`, `Phenology`

### Examples

```
# 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)

```

[Package greenbrown version 2.4.3 Index]