readme.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

library(litterfitter)
```

# litterfitter <img src="man/figures/litterfitter_hex.png" align="right" alt="" width="120" />
> R package for fitting and testing alternative models for single cohort litter decomposition data

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### Installation 

```{R, eval = FALSE}
  #install.packages("remotes")
  remotes::install_github("cornwell-lab-unsw/litterfitter")
  
library(litterfitter)
```

### Getting started 

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

```{R,results="hide",warning=FALSE,message = FALSE}
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:

```{R,fig.height=6,results='hide',fig.keep=TRUE,warning=FALSE,message = FALSE}
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:

```{R,fig.keep=TRUE}
   plot(fit)
```

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

```{R,echo=FALSE,fig.keep=TRUE}
   summary(fit)
```

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

```{R,echo=FALSE,fig.keep=TRUE}
   post<-bootstrap_parameters(fit)
   plot(post)
```