README.Rmd

---
output:
  md_document:
    variant: markdown_github
---

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

# {nlstimedist}

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{nlstimedist} fits a biologically meaningful distribution function to time-sequence data (phenology), estimates parameters to draw the cumulative distribution function and probability density function and calculates standard statistical moments and percentiles.

## Installation

You can install:

* the latest released version from CRAN with

```{r cran, eval = FALSE}
install.packages("nlstimedist")
```

* the latest development version from GitHub with

```{r gh, eval = FALSE}
# install.packages("remotes")
remotes::install_github("nathaneastwood/nlstimedist")
```

## Usage

### Preparing the data

Data should be in tidy format. {nlstimedist} provides three example tidy datasets: `lobelia`, `pupae` and `tilia`.

```{r data}
library(nlstimedist)
head(tilia)
```

We first need to calculate the cumulative number of trees as well as the proportions. We do this using the `tdData()` function.

```{r tidy}
tdTilia <- tdData(tilia, x = "Day", y = "Trees")
tdTilia
```

### Fitting the model
We fit the model to the proportion of the cumulative number of trees (`propMax`) in the `tdTilia` object using the `timedist()` function.

```{r model}
model <- timedist(data = tdTilia, x = "Day", y = "propMax", r = 0.1, c = 0.5, t = 120)
model
```

### Extracting the moments
We can extract the mean, variance, standard deviation, skew, kurtosis and entropy of the model as follows.

```{r moments}
model$m$getMoments()
```

### Extracting the RSS
Similarly we can extract the RSS of the model

```{r rss}
model$m$rss()
```

### Plotting the PDF and CDF
The probability density function (PDF) and the cumulative distribution function (CDF) of the model have their own plotting functions.

```{r pdfPlot}
tdPdfPlot(model)
```

```{r cdfPlot}
tdCdfPlot(model)
```