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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')

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.