Author: Emma Govan and Andrew Parnell
Date: 2024-05-31

# Quick start using cosimmr

#### Emma Govan and Andrew Parnell

#### 2024-05-31

## Step 1: install `cosimmr`

Use:

    install.packages("cosimmr")

then

    library(cosimmr)

## Step 2: load in the data

Some geese isotope data is included with this package. Find where it is with:

    system.file("extdata", "geese_data_small.xls", package = "cosimmr")

Load into R with:

    library(readxl)

    path <- system.file("extdata", "geese_data_small.xls", package = "cosimmr")

    geese_data <- lapply(excel_sheets(path), read_excel, path = path)

If you want to see what the original Excel sheet looks like you can run `system(paste('open',path))`.

We can now separate out the data into parts

    targets <- geese_data[[1]]
    sources <- geese_data[[2]]
    TEFs <- geese_data[[3]]
    concdep <- geese_data[[4]]

Note that if you don’t have TEFs or concentration dependence you can just leave them blank in the step below.

## Step 3: load the data into `cosimmr`

Here we are using Weight as a covariate. data are inputted as matrices

    Weight <- targets$`Net Wt`

    geese_cosimmr <- cosimmr_load(
      formula = as.matrix(targets[, 1:2]) ~ Weight,
      source_names = sources$Sources,
      source_means = as.matrix(sources[, 2:3]),
      source_sds = as.matrix(sources[, 4:5]),
      correction_means = as.matrix(TEFs[, 2:3]),
      correction_sds = as.matrix(TEFs[, 4:5]),
      concentration_means = as.matrix(concdep[, 2:3])
      )

## Step 4: plot the data

    plot(geese_cosimmr, colour_by_cov = TRUE, cov_name = "Weight")

##Step 5: Run through cosimmr

    geese_out = cosimmr_ffvb(geese_cosimmr)

##Step 5: Look at the output Look at the influence of the prior:

    prior_viz(geese_out)

Look at the histogram of the dietary proportions for observations 1 and 2:

    plot(geese_out, type = "prop_hist", obs = c(1,2))

For the many more options available to run and analyse output, see the main vignette via `vignette('cosimmr')`