The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

simmr: quick start guide

Andrew Parnell

2023-10-27

Step 1: install simmr

Use:

install.packages("simmr")

then

library(simmr)

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.xls", package = "simmr")

Load into R with:

library(readxl)
path <- system.file("extdata", "geese_data.xls", package = "simmr")
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 set these all to the value 0 or just leave them blank in the step below.

Step 3: load the data into simmr

geese_simmr <- simmr_load(
  mixtures = targets[, 1:2],
  source_names = sources$Sources,
  source_means = sources[, 2:3],
  source_sds = sources[, 4:5],
  correction_means = TEFs[, 2:3],
  correction_sds = TEFs[, 4:5],
  concentration_means = concdep[, 2:3],
  group = as.factor(paste("Day", targets$Time))
)

Step 4: plot the data

plot(geese_simmr, group = 1:8)

Step 5: run through simmr and check convergence

geese_simmr_out <- simmr_mcmc(geese_simmr)
summary(geese_simmr_out,
  type = "diagnostics",
  group = 1
)

Check that the model fitted well:

posterior_predictive(geese_simmr_out, group = 5)
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 40
##    Unobserved stochastic nodes: 46
##    Total graph size: 198
## 
## Initializing model

Step 6: look at the output

Look at the influence of the prior:

prior_viz(geese_simmr_out)

Look at the histogram of the dietary proportions:

plot(geese_simmr_out, type = "histogram")

compare_groups(geese_simmr_out,
  groups = 1:4,
  source_name = "Enteromorpha"
)
## Most popular orderings are as follows:
##                                     Probability
## Day 428 > Day 124 > Day 398 > Day 1      0.2142
## Day 428 > Day 398 > Day 124 > Day 1      0.1811
## Day 428 > Day 124 > Day 1 > Day 398      0.1561
## Day 428 > Day 398 > Day 1 > Day 124      0.0936
## Day 428 > Day 1 > Day 124 > Day 398      0.0869
## Day 428 > Day 1 > Day 398 > Day 124      0.0619
## Day 398 > Day 428 > Day 124 > Day 1      0.0467
## Day 124 > Day 428 > Day 398 > Day 1      0.0419
## Day 124 > Day 428 > Day 1 > Day 398      0.0275
## Day 398 > Day 428 > Day 1 > Day 124      0.0203
## Day 1 > Day 428 > Day 124 > Day 398      0.0139
## Day 398 > Day 124 > Day 428 > Day 1      0.0139
## Day 124 > Day 398 > Day 428 > Day 1      0.0131
## Day 1 > Day 428 > Day 398 > Day 124      0.0064
## Day 124 > Day 1 > Day 428 > Day 398      0.0058
## Day 398 > Day 1 > Day 428 > Day 124      0.0039
## Day 1 > Day 398 > Day 428 > Day 124      0.0033
## Day 1 > Day 124 > Day 428 > Day 398      0.0019
## Day 124 > Day 1 > Day 398 > Day 428      0.0019
## Day 398 > Day 124 > Day 1 > Day 428      0.0019
## Day 398 > Day 1 > Day 124 > Day 428      0.0011
## Day 1 > Day 124 > Day 398 > Day 428      0.0008
## Day 1 > Day 398 > Day 124 > Day 428      0.0008
## Day 124 > Day 398 > Day 1 > Day 428      0.0008

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

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.