litterfitter

In litterfitter: Fit a Collection of Curves to Single Cohort Decomposition Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

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

```{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)

litterfitter documentation built on Aug. 29, 2023, 9:07 a.m.