Ms.GSI is here to help you conducting multistage genetic stock identification. This package includes functions to setup input data, run the multistage model, and make summary statistics and convergence diagnostics. It also includes a function for making trace plots.
Installation
You can install the development version of Ms.GSI from GitHub with:
# install.packages("devtools")
devtools::install_github("boppingshoe/Ms.GSI", build_vignettes = TRUE)Example
This example shows the basic workflows for running a multistage model. First thing first, the background: we made up a scenario where we have samples for Chinook salmon bycatch from Bering Sea groundfish fisheries. The mixture sample contains Chinook from all over the North Pacific, but we are interested in contribution from the Yukon River. We will conduct GSI using a broad-scale baseline (base_templin) in combination with a regional baseline (base_yukon) in a multistage framework.
The fake Chinook data sets are pre-loaded in the Ms.GSI package. Here we prepare the input data:
library(Ms.GSI)
msgsi_dat <-
prep_msgsi_data(mixture_data = mix,
baseline1_data = base_templin, baseline2_data = base_yukon,
pop1_info = templin_pops211, pop2_info = yukon_pops50, sub_group = 3:5,
harvest_mean = 500, harvest_cv = 0.05)
#> Compiling input data, may take a minute or two...
#> Time difference of 7.764232 secsUsing the prepared input data, we run the model with four chains of 150 iterations. In reality, you should of course run it with more iterations. We set the first 50 iterations in each chain as the warm-ups (not kept in the final output). Here’s the summary for the estimates and convergence diagnostics.
msgsi_out <- msgsi_mdl(msgsi_dat, nreps = 150, nburn = 50, thin = 1, nchains = 4)
#> Running model... and good things come to Best Dressed!
#> Time difference of 1.927239 secs
#> March-26-2026 13:23
msgsi_out$summ_comb
#> # A tibble: 12 × 10
#> group mean median sd ci.05 ci.95 p0 GR n_eff z0
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Russia 4.77e-2 4.42e-2 0.0202 2.13e- 2 0.0874 0.01 1.02 259. 0
#> 2 Coastal Wes… 1.25e-1 1.10e-1 0.0979 1.58e-10 0.292 0.2 1.85 36.2 0.182
#> 3 North Alask… 4.69e-2 4.44e-2 0.0237 1.41e- 2 0.0873 0.08 1.01 143. 0
#> 4 Northwest G… 3.37e-1 3.31e-1 0.0610 2.41e- 1 0.444 0 1.22 124. 0
#> 5 Copper 1.24e-3 1.29e-6 0.00437 1.99e-19 0.00849 1 1.20 253. 0.945
#> 6 Northeast G… 2.54e-3 5.27e-6 0.00874 1.11e-17 0.0152 1 1.20 125. 0.89
#> 7 Coastal Sou… 2.32e-3 3.15e-5 0.00512 3.89e-16 0.0122 1 1.04 148. 0.818
#> 8 British Col… 7.83e-4 1.39e-6 0.00267 4.98e-19 0.00429 1 1.10 319. 0.978
#> 9 WA/OR/CA 5.70e-4 2.08e-6 0.00166 8.82e-20 0.00379 1 1.01 248. 0.99
#> 10 Lower Yukon 1.75e-1 1.82e-1 0.0970 2.88e- 2 0.335 0 2.03 45.8 0
#> 11 Middle Yukon 7.21e-2 7.06e-2 0.0225 4.12e- 2 0.112 0 1.01 331. 0
#> 12 Upper Yukon 1.88e-1 1.87e-1 0.0333 1.40e- 1 0.247 0 1.00 457. 0Summary for the stock-specific harvest is called separately:
msgsi_harv_summ(msgsi_out, msgsi_dat)
#> # A tibble: 12 × 6
#> repunit mean_harv median_harv sd_harv ci05_harv ci95_harv
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Northeast Gulf of Alaska 1.12 0 4.00 0 7.05
#> 2 Coastal Southeast Alaska 1.06 0 2.53 0 6
#> 3 Coastal West Alaska 63.5 59 49.0 0 149
#> 4 WA/OR/CA 0.23 0 0.827 0 1
#> 5 Northwest Gulf of Alaska 170. 167 29.6 128. 222.
#> 6 British Columbia 0.288 0 1.09 0 2
#> 7 Russia 24.4 22 9.25 13 41
#> 8 North Alaska Peninsula 23.8 22 11.0 8 44.0
#> 9 Copper 0.612 0 2.14 0 5
#> 10 Upper Yukon 56.6 56 18.3 30 85.0
#> 11 Lower Yukon 140. 138 28.1 106 192.
#> 12 Middle Yukon 22.9 22 8.19 12 38Individual assignment summary:
indiv_assign(msgsi_out, msgsi_dat)
#> # A tibble: 150 × 13
#> ID Russia `Coastal West Alaska` `North Alaska Peninsula`
#> * <chr> <dbl> <dbl> <dbl>
#> 1 fish_1 0 0.485 0
#> 2 fish_2 0 0.3 0.0875
#> 3 fish_3 0.01 0.0575 0.405
#> 4 fish_4 0 0.415 0.0275
#> 5 fish_5 0 0.457 0.01
#> 6 fish_6 0 0.31 0
#> 7 fish_7 0.09 0.172 0.178
#> 8 fish_8 0.155 0.225 0.0325
#> 9 fish_9 0.0075 0.258 0.005
#> 10 fish_10 0.0425 0.155 0.06
#> # ℹ 140 more rows
#> # ℹ 9 more variables: `Northwest Gulf of Alaska` <dbl>, Copper <dbl>,
#> # `Northeast Gulf of Alaska` <dbl>, `Coastal Southeast Alaska` <dbl>,
#> # `British Columbia` <dbl>, `WA/OR/CA` <dbl>, `Lower Yukon` <dbl>,
#> # `Middle Yukon` <dbl>, `Upper Yukon` <dbl>There’s a function in the package to make trace plots and inspect mixing of chains.
tr_plot(mdl_out = msgsi_out, trace_obj = "trace_comb", pop_info = msgsi_out$comb_groups)
Details of the mathematical model of integrated multistage framework and instructions for using Ms.GSI package can be found in the “articles” tab of the package website. Or, once you installed Ms.GSI, you can call the article using vignette("msgsi_vignette").