Here we step through the Lake Example using the **script** version of MixSIAR. For a demonstration using the **GUI** version, see the MixSIAR Manual. For a thorough walkthrough of how to use MixSIAR in a script, see the Wolves Example, which provides more commentary and explanation.

For a clean, runnable `.R`

script, look at `mixsiar_script_lake.R`

in the `example_scripts`

folder of the MixSIAR package install:

```
library(MixSIAR)
mixsiar.dir <- find.package("MixSIAR")
paste0(mixsiar.dir,"/example_scripts")
```

`## [1] "/tmp/Rtmpm2SefP/Rinst74a7f7f1e2c/MixSIAR/example_scripts"`

You can run the lake example script directly with:

`source(paste0(mixsiar.dir,"/example_scripts/mixsiar_script_lake.R"))`

The Lake Example data is simulated based on Francis et al. 2011 and we include it as a example of how MixSIAR can include a **continuous effect**. Here we examine the diet of zooplankton in 21 lakes using:

- 2 biotracers (\(\delta^{13}\)C, \(\delta^{15}\)N)
- 1
**continuous effect**(Secchi Depth : Mixed Layer Depth) - Raw source data

MixSIAR fits a continuous covariate as a linear regression in ILR/transform-space. Two terms are fit for the proportion of each source: an intercept and a slope. The plot uses the posterior median estimates of the intercept and slope, and the lines are curved because of the ILR-transform back into p-space. For details, or if you would like to make modifications, see ?plot_continous_var.R.

Fitting a model with a continuous effect is more complex than the categorical fixed/random effects and can be a bit finicky.

`library(MixSIAR)`

See ?load_mix_data for details.

The lake consumer data has 1 covariate, which we fit as a continuous effect (`cont_effects="Secchi.Mixed"`

). There are no fixed/random effects (`factors=NULL`

, `fac_random=NULL`

, `fac_nested=NULL`

).

```
# Replace the system.file call with the path to your file
mix.filename <- system.file("extdata", "lake_consumer.csv", package = "MixSIAR")
mix <- load_mix_data(filename=mix.filename,
iso_names=c("d13C","d15N"),
factors=NULL,
fac_random=NULL,
fac_nested=NULL,
cont_effects="Secchi.Mixed")
```

See ?load_source_data for details.

We have no fixed/random effects in the model (`source_factors=NULL`

), and we do not have concentration dependence data (`conc_dep=FALSE`

). We have the original “raw” source data, not means and SDs (`data_type="raw"`

).

```
# Replace the system.file call with the path to your file
source.filename <- system.file("extdata", "lake_sources.csv", package = "MixSIAR")
source <- load_source_data(filename=source.filename,
source_factors=NULL,
conc_dep=FALSE,
data_type="raw",
mix)
```

See ?load_discr_data for details.

```
# Replace the system.file call with the path to your file
discr.filename <- system.file("extdata", "lake_discrimination.csv", package = "MixSIAR")
discr <- load_discr_data(filename=discr.filename, mix)
```

This is your chance to check:

- Are the data loaded correctly?
- Is your mixture data in the source polygon?
- Are one or more of your sources confounded/hidden?

```
# Make an isospace plot
plot_data(filename="isospace_plot", plot_save_pdf=TRUE, plot_save_png=FALSE, mix,source,discr)
```

Calculate normalized surface area of the convex hull polygon(s) as in Brett (2014).

**Note 1:** discrimination SD is added to the source SD (see ?calc_area for details)

```
# Calculate the convex hull area, standardized by source variance
calc_area(source=source,mix=mix,discr=discr)
```

`## [1] 3.053018`

Define your prior, and then plot using “plot_prior”

- RED = your prior
- DARK GREY = “uninformative”/generalist (alpha = 1)
- LIGHT GREY = “uninformative” Jeffrey’s prior (alpha = 1/n.sources)

```
# default "UNINFORMATIVE" / GENERALIST prior (alpha = 1)
plot_prior(alpha.prior=1,source)
```

In the Lake Example we demo the “Residual only” error option. The differences between “Residual * Process”, “Residual only”, and “Process only” are explained in Stock and Semmens (in revision).

```
# Write the JAGS model file
model_filename <- "MixSIAR_model.txt"
resid_err <- TRUE
process_err <- FALSE
write_JAGS_model(model_filename, resid_err, process_err, mix, source)
```

Choose one of the MCMC run options:

run == | Chain Length | Burn-in | Thin | # Chains |
---|---|---|---|---|

“test” | 1,000 | 500 | 1 | 3 |

“very short” | 10,000 | 5,000 | 5 | 3 |

“short” | 50,000 | 25,000 | 25 | 3 |

“normal” | 100,000 | 50,000 | 50 | 3 |

“long” | 300,000 | 200,000 | 100 | 3 |

“very long” | 1,000,000 | 500,000 | 500 | 3 |

“extreme” | 3,000,000 | 1,500,000 | 500 | 3 |

First use `run = "test"`

to check if 1) the data are loaded correctly and 2) the model is specified correctly:

```
jags.1 <- run_model(run="test", mix, source, discr, model_filename,
alpha.prior = 1, resid_err, process_err)
```

After a test run works, increase the MCMC run to a value that may converge:

```
jags.1 <- run_model(run="normal", mix, source, discr, model_filename,
alpha.prior = 1, resid_err, process_err)
```

MixSIAR fits a continuous covariate as a linear regression in ILR/transform-space. Two terms are fit for the proportion of each source: an intercept and a slope. The plot uses the posterior median estimates of the intercept and slope, and the lines are curved because of the ILR-transform back into p-space. For details, or if you would like to make modifications, see ?plot_continous_var. See ?output_JAGS for output options.

The other posterior plots MixSIAR produces for a continuous effect show the estimated diet for the minimum, median, and maximum individuals.

`output_JAGS(jags.1, mix, source, output_options)`