Extended Batch Marking Models.
extBatchMarking: Implementation of Extended Batch Marking Model
The primary objective of extBatchMarking is to facilitate the fitting of models developed by Cowen et al., $2017$ for the ecologist. The marked models can be seamlessly integrated with unmarked models to estimate population size. The combined model harnesses the power of both the N-mixture model and the Viterbi algorithm for hidden markov model to provide accurate population size estimates.
The primary objective of extBatchMarking is to facilitate the fitting of models developed by Cowen et al., $2017$ for the ecologist. The marked models can be seamlessly integrated with unmarked models to estimate population size. The combined model harnesses the power of both the N-mixture model and the Viterbi algorithm for hidden markov model to provide accurate population size estimates.
In ecological research, it’s often challenging to directly count every individual of a species due to various factors such as their elusive nature or inaccessible habitats. As a result, Cowen et al., $2017$ employ two distinct modeling approaches: marked and unmarked models.
Marked Models: These models focus on individuals that have been uniquely identified or ‘marked’ in some way, such as through tagging, banding, or other identification methods. These marked individuals are tracked over time, and their data is used to estimate parameters related to the population, such as survival rates, population growth, or movement patterns.
Unmarked Models: Unmarked models, on the other hand, are designed to estimate population parameters without relying on individual identification.
The beauty of combining these two modeling approaches lies in their synergy. By leveraging both marked and unmarked data, ecologists can achieve more accurate and robust estimates of population abundance. Marked data provide insights into specific individuals, while unmarked data give a broader perspective on the entire population.
In practice, this combination is achieved through sophisticated statistical techniques, often utilizing concepts like the N-mixture model and algorithms like the Viterbi algorithm. These methods allow ecologists to integrate data from marked and unmarked individuals, resulting in more comprehensive and reliable population abundance estimates.
The models showcased in this example represent the foundational instances drawn from a set of four complex models available within the extBatchMarking package. These models serve as essential building blocks for understanding the advanced functionalities and capabilities offered by the package.
The extBatchMarking package is designed to empower researchers with a powerful tool set for the analysis of batch-marked data in ecological and population studies. It allows users to efficiently fit and assess batch-marked models, aiding in the estimation of critical population parameters such as survival and capture probabilities.
In particular, the models illustrated here provide a comprehensive introduction to the core concepts and methodologies underpinning the package’s functionality. They are intended to facilitate an initial grasp of how to work with batch-marked data, offering insights into the modeling techniques used in the field of population ecology.
It’s worth noting that the results obtained using the extBatchMarking package align with the findings presented in Cowen et al. (2017). This alignment demonstrates the package’s reliability and ability to replicate established research outcomes. The Cowen et al. (2017) results section serves as a benchmark against which the package’s performance can be validated, providing users with confidence in the accuracy of their analyses.
By starting with these basic examples, users can progressively delve into more intricate and tailored analyses within the extBatchMarking package, ultimately enabling them to make meaningful contributions to the understanding of population dynamics and ecology. The package’s versatility and fidelity to established research findings make it a valuable resource for both novice and experienced researchers in the field.
The example will guide you through the steps of how to employ this approach effectively, demonstrating its relevance and importance in ecological and wildlife studies. It showcases the power of merging marked and unmarked models to gain a deeper understanding of species populations and their dynamics within natural ecosystems.”
Installation
You can install the released version of extBatchMarking from CRAN with:
devtools::load_all(".")
#> ℹ Loading extBatchMarking
devtools::document()
#> ℹ Updating extBatchMarking documentation
#> ℹ Loading extBatchMarking
#> Writing 'batchMarkHmmLL.Rd'
#> Writing 'batchUnmarkHmmLL.Rd'
#> Writing 'batchUnmarkViterbi.Rd'
#> Writing 'batchMarkUnmarkHmmLL.Rd'
#> Writing 'batchUnmark2Viterbi.Rd'
# usethis::use_package_doc()
devtools::load_all()
#> ℹ Loading extBatchMarking
Example 1
This is a basic example which shows how to fit a Batch marking model with constant phi and p. Example 1 can also be found in the Cowen et al., 2017 results using the WeatherLoach data using in the same paper:
library(extBatchMarking)
Load the data WeatherLoach from the extBatchMarking package: Here is the step-by-step guide on how to load data directly from extBatchMarking package. The defult data discussed in Cowen et al. 2017.
data("WeatherLoach", package = "extBatchMarking")
First, we show with an example how to fit the batchMarkHmmLL and batchMarkUnmarkHmmLL functions. batchMarkHmmLL and batchMarkUnmarkHmmLL functions output the unoptimized log-likelihood values of marked only model and the combined models. These allow users know if the likelihood functions can be computed at the specified initial values. Otherwise, NAN or Inf will be returned. If so, the arguments of the functions should be revisited.
# Initial parameter
theta <- c(0, -1)
cores <- detectCores()-1
res1 <- batchMarkHmmLL(par = theta,
data = WeatherLoach,
choiceModel = "model4",
cores)
res1
#> [1] 132.3349
Example 2
thet <- c(0.1, 0.1, 7, -1.5)
res3 <- batchMarkUnmarkHmmLL(par = thet,
data = WeatherLoach,
choiceModel = "model4",
Umax = 1800,
nBins = 20,
cores = cores)
Example 3
#> initial value 132.334856
#> final value 124.984186
#> converged
Model survival probability value:
res$phi
#> [1] 0.6
Model detection probability value:
res$p
#> [1] 0.2
Model log-likelihood value:
res$ll
#> [1] 124.98
Model AIC value
res$AIC
#> [1] 253.97
If heesian = TRUE the hessian matrix will be outputed
res$hessian
#> [,1] [,2]
#> [1,] 216.6291 141.3538
#> [2,] 141.3538 150.7772
Example 4
This example serves as a fundamental illustration of the process of combining both marked and unmarked models to estimate the population abundance of a species. It demonstrates a key approach used in ecological and wildlife studies to gain insights into the size of a specific species population within a given habitat.
theta <- c(0.1, 0.1, 7, -1.5)
res2 <- batchMarkUnmarkOptim(par=theta,
data=WeatherLoach,
Umax=1800,
nBins=20,
choiceModel="model4",
popSize = "Horvitz_Thompson",
method="BFGS",
parallel=FALSE,
control=list(trace = 1))
#> initial value 394.338558
#> iter 10 value 202.900359
#> final value 202.899020
#> converged
results
res2$phi
#> [1] 0.59
res2$p
#> [1] 0.2
res2$lambda
#> [1] 1318.6
res2$gam
#> [1] 0.2
res2$ll
#> [1] 202.9
res2$AIC
#> [1] 413.8
res2$U
#> [1] 1310 1010 790 630 490 370 290 250 230 210 170
res2$M
#> [1] 0 160 250 300 110 100 50 80 100 130 60
res2$N
#> [1] 1310 1170 1040 930 600 470 340 330 330 340 230