Added more detailed README. This is a copy of the vignette which will…

… subsequentley be replaced.

.Rbuildignore

@@ -3,3 +3,5 @@
 ^\.travis\.yml$
 ^codecov.yml$
 cran-comments.md
+^README\.Rmd$
+^README-.*\.png$

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: nlstimedist
 Type: Package
 Title: Non-Linear Model Fitting of Time Distribution of Biological Phenomena
-Version: 1.0.1.9000
+Version: 1.0.1.9001
 Authors@R: c(
   person("Nathan", "Eastwood", , "[email protected]",
          role = c("aut", "cre")),

README.Rmd

@@ -0,0 +1,91 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r opts, echo = FALSE}
+library(nlstimedist)
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "README-"
+)
+
+```
+
+# nlstimedist
+
+[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
+[![Travis-CI Build Status](https://travis-ci.org/NattyE/nlstimedist.svg?branch=master)](https://travis-ci.org/NattyE/nlstimedist)
+[![codecov](https://codecov.io/gh/NattyE/nlstimedist/branch/master/graph/badge.svg)](https://codecov.io/gh/NattyE/nlstimedist)
+
+nlstimedist fits a biologically meaningful distribution function to time-sequence data (phenology), estimates parameters to draw the cumulative distribution function and probability density function and calculates standard statistical moments and percentiles.
+
+# Installation
+
+You can install:
+
+* the latest released version from CRAN with
+
+```{r cran, eval = FALSE}
+install.packages("nlstimedist")
+```
+
+* the latest development version from GitHub with
+
+```{r gh, eval = FALSE}
+devtools::install_github("NattyE/nlstimedist")
+```
+
+# Usage
+
+## Preparing the data
+Data should be in tidy format. `nlstimedist` provides three example tidy datasets: `lobelia`, `pupae` and `tilia`.
+
+```{r data}
+head(tilia)
+```
+
+We first need to calculate the cumulative number of trees as well as the proportions. We do this using the `tdData` function.
+
+```{r tidy}
+tdTilia <- tdData(tilia, x = "Day", y = "Trees")
+tdTilia
+```
+
+## Fitting the model
+We fit the model to the proportion of the cumulative number of trees (`propMax`) in the `tdTilia` data using the `timedist` function.
+
+```{r model}
+model <- timedist(data = tdTilia, x = "Day", y = "propMax", r = 0.1, c = 0.5, t = 120)
+model
+```
+
+## Extracting the moments
+We can extract the mean, variance, standard deviation, skew, kurtosis and entropy of the model as follows.
+
+```{r moments}
+model$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 pdf and cdf of the model have their own plotting functions.
+
+```{r pdfPlot}
+tdPdfPlot(model)
+```
+
+```{r cdfPlot}
+tdCdfPlot(model)
+```
+
+# Citation
+Franco, M. (2012). *The time-course of biological phenomenon - illustrated with the London Marathon*. Unpublished manuscript. Plymouth University.

README.md

@@ -1,23 +1,127 @@
-# nlstimedist
 
-> 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.
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+nlstimedist
+===========
 
-[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
-[![Travis-CI Build Status](https://travis-ci.org/NattyE/nlstimedist.svg?branch=master)](https://travis-ci.org/NattyE/nlstimedist)
-[![codecov](https://codecov.io/gh/NattyE/nlstimedist/branch/master/graph/badge.svg)](https://codecov.io/gh/NattyE/nlstimedist)
+[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) [![Travis-CI Build Status](https://travis-ci.org/NattyE/nlstimedist.svg?branch=master)](https://travis-ci.org/NattyE/nlstimedist) [![codecov](https://codecov.io/gh/NattyE/nlstimedist/branch/master/graph/badge.svg)](https://codecov.io/gh/NattyE/nlstimedist)
 
-Usage
------
+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
+============
 
-Please refer to the vignette
+You can install:
+
+-   the latest released version from CRAN with
 
 ``` r
-vignette(nlstimedist)
+install.packages("nlstimedist")
 ```
 
-Installation
-------------
+-   the latest development version from GitHub with
 
 ``` r
 devtools::install_github("NattyE/nlstimedist")
 ```
+
+Usage
+=====
+
+Preparing the data
+------------------
+
+Data should be in tidy format. `nlstimedist` provides three example tidy datasets: `lobelia`, `pupae` and `tilia`.
+
+``` r
+head(tilia)
+#>   Day Trees
+#> 1  94     0
+#> 2  95     0
+#> 3  96     1
+#> 4 103     1
+#> 5 104     0
+#> 6 105     3
+```
+
+We first need to calculate the cumulative number of trees as well as the proportions. We do this using the `tdData` function.
+
+``` r
+tdTilia <- tdData(tilia, x = "Day", y = "Trees")
+tdTilia
+#> # A tibble: 26 × 4
+#>      Day Trees  cumN    propMax
+#>    <int> <dbl> <dbl>      <dbl>
+#> 1     96     1     1 0.01538462
+#> 2    103     1     2 0.03076923
+#> 3    105     3     5 0.07692308
+#> 4    107     1     6 0.09230769
+#> 5    110     4    10 0.15384615
+#> 6    111     7    17 0.26153846
+#> 7    112     3    20 0.30769231
+#> 8    114     1    21 0.32307692
+#> 9    115     3    24 0.36923077
+#> 10   116     6    30 0.46153846
+#> # ... with 16 more rows
+```
+
+Fitting the model
+-----------------
+
+We fit the model to the proportion of the cumulative number of trees (`propMax`) in the `tdTilia` data using the `timedist` function.
+
+``` r
+model <- timedist(data = tdTilia, x = "Day", y = "propMax", r = 0.1, c = 0.5, t = 120)
+model
+#> Nonlinear regression model
+#>   model: propMax ~ 1 - (1 - (r/(1 + exp(-c * (Day - t)))))^Day
+#>    data: data
+#>         r         c         t 
+#>   0.02721   0.17126 124.84320 
+#>  residual sum-of-squares: 0.01806
+#> 
+#> Number of iterations to convergence: 10 
+#> Achieved convergence tolerance: 1.49e-08
+```
+
+Extracting the moments
+----------------------
+
+We can extract the mean, variance, standard deviation, skew, kurtosis and entropy of the model as follows.
+
+``` r
+model$m$getMoments()
+#>       mean variance       sd     skew kurtosis entropy
+#> 1 118.0325 180.7509 13.44436 4.324762 46.82073 5.36145
+```
+
+Extracting the RSS
+------------------
+
+Similarly we can extract the RSS of the model
+
+``` r
+model$m$rss()
+#> [1] 0.9930469
+```
+
+Plotting the PDF and CDF
+------------------------
+
+The pdf and cdf of the model have their own plotting functions.
+
+``` r
+tdPdfPlot(model)
+```
+
+![](README-pdfPlot-1.png)
+
+``` r
+tdCdfPlot(model)
+```
+
+![](README-cdfPlot-1.png)
+
+Citation
+========
+
+Franco, M. (2012). *The time-course of biological phenomenon - illustrated with the London Marathon*. Unpublished manuscript. Plymouth University.