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MultiNMix: A Package for Multispecies N-Mixture Models

MultinMix is an R package designed for fitting Multispecies N-Mixture (MNM) Models (Mimnagh, Niamh, et al. (2022)), a powerful tool for estimating abundance and occurrence of multiple species in a hierarchical Bayesian framework.

Features

• Bayesian Modeling: Fit hierarchical Bayesian MNM models using Nimble.
• Customisable Priors: Define prior distributions easily for each parameter.
• Comprehensive Outputs: Includes posterior summaries, convergence diagnostics, and model fit statistics (log-likelihood, AIC, BIC).
• User-Friendly API: Simple interface to specify data, initial values, and model parameters.
• Visualisation: Built-in methods for producing density plots and traceplots, for model diagnostics.

Installation

To install the development version of MultiNMix, use the following commands in R:

if (!requireNamespace("devtools", quietly = TRUE)) {
  install.packages("devtools")

devtools::install_github("niamhmimnagh/MultiNMix")

Getting Started

Here is a quick example to get you started with MultiNMix:

library(MultinMix)

# Example data
x <- simulateData(model = "MNM")
 R<-x$R
 T<-x$T
 S<-x$S
 K<-x$K

 Xp <- array(rnorm(R * S * 2), dim = c(R,  S, 2)) # creating 2 detection probability covariates
 Xn <- array(rnorm(R * S *3), dim = c(R, S,  3)) # creating 3 abundance covariates
 
# Fit 
fit <- MNM_fit(
  Y = species_counts,
  Xp = Xp,
  Xn = Xn,
  Hurdle=FALSE,
  AR = FALSE,
  iterations = 5000,  # Number of iterations
  burnin = 1000,  # Burn-in period
  thin = 10,  # Thinning interval
  prior_detection_probability="dnorm(0,0.01)" # user-defined normal prior distribution
)

# Summarize results
fit@summary

# Plot diagnostic results by specifying the model and the parameter
tracePlot(fit, param="N[8,1]")
density(fit, param="N[8,1]")

# A list of all available diagnostic plots can be found:
View(y@plot)

Functions

Main Function

• MNM_fit(): Fits a Multispecies N-Mixture Model using specified data and parameters.

Utility Functions

• tracePlot(): Generates traceplots of monitored parameters.
• density(): Generates density plots of monitored parameters.
• logLik(): Extracts the log-likelihood of the model.
• AIC(), BIC(): Computes AIC and BIC values for model comparison.
• check_convergence(): Assesses model convergence using Gelman-Rubin diagnostics.

Documentation

Detailed documentation and vignettes are available in the package. After installation, access them using:

??MultiNMix

Datasets

There are two datasets available in the package birds and the zero-inflated birds_ZI. Both are a subset of the North American Breeding Bird Survey dataset (https://www.pwrc.usgs.gov/BBS/). birds is a dataframe with 2,880 observations and 13 columns (R=24, T=10, S=20, K=6) while birds_ZI is a dataframe with 600 observations and 13 columns (R=15, T=10, S=10, K=4).

In this vignette, we will show the birds dataset, the processing steps required and a worked example of it.

The birds Dataset

data(birds)
head(birds)

The birds dataset is currently a data frame of dimension (600, 10). It needs to be reformatted into an array of dimension (R=15, T=10, S=10, K=4) before it can be used with the MultiNMix functions.

 # Data must first be reformatted to an array of dimension (R,T,S,K)
   R <- 15
   T <- 10
   S <- 10
   K <- 4

 # Ensure data is ordered consistently
   birds <- birds[order(birds$Route, birds$Year, birds$English_Common_Name), ]
  
 # Create a 4D array with proper dimension
   Y <- array(NA, dim = c(R, T, S, K))
  
 # Map route, species, and year to indices
   route_idx <- as.numeric(factor(birds$Route))
   species_idx <- as.numeric(factor(birds$English_Common_Name))
   year_idx <- as.numeric(factor(birds$Year))
  
 # Populate the array
   stop_data <- as.matrix(birds[, grep("^Stop", colnames(birds))])
  
   for (i in seq_len(nrow(birds))) {
     Y[route_idx[i], , species_idx[i], year_idx[i]] <- stop_data[i, ]
     }
  
 # Assign dimnames
     dimnames(Y) <- list(
       Route = sort(unique(birds$Route)),
         Stop = paste0("Stop", 1:T),
           Species = sort(unique(birds$English_Common_Name)),
             Year = sort(unique(birds$Year)))

The function MNM_fit in the MultiNMix package allows for easy implementation of a multi-species N-mixture model using data of this format.

model<-MNM_fit((Y=Y, AR=FALSE, Hurdle=FALSE))

We can then access elements of the model as follows:

model@summary # outputs the mean estimate, standard deviation, standard error, 95% credible interval, effective sample size and gelman rubin statistic for each monitored variable

model@estimates$N # outputs the estimated mean  N

logLik(model) # estimates the log likelihood of the model

AIC(model)/BIC(model) # outputs the AIC or BIC values

tracePlot(model, param="N[1,1]") # outputs the traceplot of the N[1,1] parameter

density(model, param="N[1,1]") #outputs the density plot for the N[1,1] parameter

Contributions

Contributions are welcome! If you encounter any issues or have suggestions for improvement, please submit a report or a pull request.

References

Mimnagh, Niamh, et al. “Bayesian multi-species N-mixture models for unmarked animal communities.” Environmental and Ecological Statistics 29.4 (2022): 755-778.

Acknowledgements

MultiNMix was developed as part of research into multispecies abundance modeling. Special thanks to the creators of Nimble (r-nimble.org) for their invaluable tools in Bayesian modeling.

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.