Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multispecies distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. 
Here, we present a general multispecies distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalized linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on..., This study performed simulation study to test the hypothesis and applied the multi-species distribution model to gulls dataset that was accessed from the Global Biodiversity Infrastructure Facility (with DOI: https://doi.org/10.15468/dl.h24bp5). Attached here are the R-scripts required to reproduce the simulation study and the analysis of the case study (the gull dataset would have to be downloaded first). The details of the scripts are provided in README file., , # Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance

The uploads consist of R-scripts that are used for the simulation study and the analysis of the gulls dataset.

**A. Simulation study:**

*i. simulationData.R*
This script is used to simulate the data needed for the analysis.

*ii. nimbleSimulatedData.R*
This script contains the function that is used to analyse the simulated dataset from the *simulationData.R* script.

*iii. constant.R, fixedIntercov.R, intercept.R, main.R, onlyCov.R, variable.R*
These scripts are used to run a specific model specification described in the main paper. It calls the function defined in *simulationData.R* and the dataset followed from *simulationData.R*.

*iv. plotSimulations.R*
The scripts are used to summarise the results and generate the plots and tables presented in the main paper and the supplementary information.

**B. Case study : Gull dataset**

*i. gull_data_formatting....