# 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