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simmr
Use:
install.packages("simmr")
then
library(simmr)
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)
<- system.file("extdata", "geese_data.xls", package = "simmr")
path <- lapply(excel_sheets(path), read_excel, path = path) geese_data
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
<- geese_data[[1]]
targets <- geese_data[[2]]
sources <- geese_data[[3]]
TEFs <- geese_data[[4]] concdep
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.
simmr
<- simmr_load(
geese_simmr 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))
)
plot(geese_simmr, group = 1:8)
simmr
and check convergence<- simmr_mcmc(geese_simmr)
geese_simmr_out 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
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.