---
title: "Stable Isotope Mixing Models with Covariates in R using cosimmr"
author: "Emma Govan and Andrew Parnell"
date: "`r Sys.Date()`"
output: 
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  toc: true
vignette: >
  %\VignetteIndexEntry{cosimmr}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
---


## Introduction

`cosimmr` is a package in R designed for running Stable Isotope Mixing Models in
R, using Fixed Form Variational Bayes. The aim of this vignette is to allow users
to quickly learn how to use the package and giving examples of all the 
features within the package.
If you find any bugs in the software, or have new features which you think would be 
useful, please add this to the [Github issues page](https://github.com/emmagovan/cosimmr/issues)

## Installation of the `cosimmr` package

First, start Rstudio and find the window with the command prompt (the symbol `>`). Type

```{r,eval = FALSE}
install.packages("cosimmr")
```
It may ask you to pick your nearest CRAN mirror (the nearest site which hosts R packages). You will then see some activity on the screen as the `cosimmr` package and the other packages it uses are downloaded. The final line should then read: 

`package 'cosimmr' successfully unpacked and MD5 sums checked`

You then need to load the package. Type

```{r,eval = FALSE}
library(cosimmr)
```

This will load the `cosimmr` package and all the associated packages. You’ll need to type the `library(cosimmr)` command every time you start R.


There is some sample data sets (from Inger et al 2006, Nifong et al 2015, Galloway et al 2015) available within cosimmr. 
Use the following command to access one.
```{r}
geese_data_day1 = cosimmr::geese_data_day1
```

This data can then be loaded into cosimmr using the function `cosimmr_load`

```{r,include = FALSE}
library(cosimmr)
```
```{r}
cosimmr_1 <- with(
  geese_data_day1,
  cosimmr_load(
    formula = mixtures ~ 1,
    source_names = source_names,
    source_means = source_means,
    source_sds = source_sds,
    correction_means = correction_means,
    correction_sds = correction_sds,
    concentration_means = concentration_means
  )
)
```
This is a simple example that doesn't include any covariates. Note the formula is given in the form `tracer_data ~ covariates`. 

An isospace plot can be generated using this data
```{r}
plot(cosimmr_1)
```

The data can then be run through `cosimmr_ffvb`, the main function of the `cosimmr` 
package.
```{r, results='hide', message=FALSE}
cosimmr_1_out = cosimmr_ffvb(cosimmr_1)
```

Summary statistics for the run can then be viewed, several options are available, such as "statistics" and "quantiles"
```{r}
summary(cosimmr_1_out, type = "statistics")
```

The output of this can be plotted, using the plot function. There are several different options available in the plot function.
```{r}
plot(cosimmr_1_out, type ="prop_histogram", obs = 1)
```

The posterior values can also be accessed to make more unique plots
```{r}
dim(cosimmr_1_out$output$BUGSoutput$sims.list$p)
head(cosimmr_1_out$output$BUGSoutput$sims.list$sigma)
```


For another example, with a continuous covariate, we can look at Alligator data from Nifong et al, 2015.
For this example we just use Length as the covariate - but the dataset contains other covariates that can be looked at too.
First we load in the data into R - this data is included in cosimmr:
```{r, eval = FALSE}
data("alligator_data")
```

Then we use `cosimmr_load` to create a "cosimmr_in" object
```{r, eval = FALSE}
Length = alligator_data$length
cosimmr_ali <-cosimmr_load(
    formula = as.matrix(alligator_data$mixtures) ~ Length,
    source_names = alligator_data$source_names,
    source_means = as.matrix(alligator_data$source_means),
    source_sds = as.matrix(alligator_data$source_sds),
    correction_means = as.matrix(alligator_data$TEF_means),
    correction_sds = as.matrix(alligator_data$TEF_sds))
```

We then plot our data to make sure our iso-space plot looks good
```{r, eval = FALSE}
plot(cosimmr_ali, colour_by_cov = TRUE, cov_name = "Length")
```

Then we can run the mixing model:
```{r, results='hide', message=FALSE, eval = FALSE}
cosimmr_ali_out = cosimmr_ffvb(cosimmr_ali)
```

We can then look at a summary of the data. This defaults to observation 1.
```{r, eval = FALSE}
summary(cosimmr_ali_out, type = "statistics")
```

We can create plots of our data. This code creates a proportion plot and a histogram plot of beta value for individuals 1 and 2
```{r, eval = FALSE}
plot(cosimmr_ali_out, type = c("prop_histogram", "beta_histogram"), obs = c(1,2), cov_name = "Length")
```

We can use the predict function to predict proportions for individuals of lengths 100, 210, and 203 by creating a data frame and then using the `predict` function
```{r, eval = FALSE}
x_pred = data.frame(length = c(100,210,302))
alli_pred = predict(cosimmr_ali_out, x_pred)
```

"alli_pred" can be treated like a normal cosimmr_out object - we can get summary values for each individual or we can create plots
```{r, eval = FALSE}
summary(alli_pred, obs = c(1,2,3), type = "statistics")

plot(alli_pred, type = "prop_density")
```


We can create a `covariates_plot` to show the change in consumption of Freshwater as an individual increases in Length
```{r, eval = FALSE}
plot(cosimmr_ali_out, type = "covariates_plot", source = "Freshwater", cov_name = "Length")
```

Alternatively we can look at the change in both sources on one plot
```{r, eval = FALSE}
plot(cosimmr_ali_out, type = "covariates_plot", cov_name = "Length", one_plot = TRUE, n_pred = 100)
```