Quick start using cosimmr

Emma Govan and Andrew Parnell

2024-05-30

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')