litterfitter

This vignette provides an overview of the main functions in litterfitter

Getting started

library(litterfitter)

At the moment there is one key function which is fit_litter which can fit 6 different types of decomposition trajectories. Note that the fitted object is a litfit object

fit <- fit_litter(
  time = c(0, 1, 2, 3, 4, 5, 6),
  mass.remaining = c(1, 0.9, 1.01, 0.4, 0.6, 0.2, 0.01),
  model = "weibull",
  iters = 500
)

class(fit)

You can visually compare the fits of different non-linear equations with the plot_multiple_fits function:

plot_multiple_fits(
  time = c(0, 1, 2, 3, 4, 5, 6),
  mass.remaining = c(1, 0.9, 1.01, 0.4, 0.6, 0.2, 0.01),
  model = c("neg.exp", "weibull"),
  iters = 500
)

Calling plot on a litfit object will show you the data, the curve fit, and even the equation, with the estimated coefficients:

plot(fit)

The summary of a litfit object will show you some of the summary statistics for the fit.

#> Summary of litFit object
#> Model type: weibull 
#> Number of observations:  7 
#> Parameter fits: 4.19 
#> Parameter fits: 2.47 
#> Time to 50% mass loss: 3.61 
#> Implied steady state litter mass: 3.71 in units of yearly input 
#> AIC:  -3.8883 
#> AICc:  -0.8883 
#> BIC:  -3.9965

From the litfit object you can then see the uncertainty in the parameter estimate by bootstrapping