Fangorn

📃 Description

Fangorn simplifies key steps in SDMtune model analyses by automating AUC, TSS, prediction and seamlessly integrates with the enmSdmX package to calculate the CBI (Boyce Index).

Utilize the Onering function for a single model or employ the palantiris function for a list of models.

Additionally, rohirrim calculates auxiliary metrics proposed by Barbosa et al., (2013), by providing important results for SDM's, such as Over-prediction rate (OPR), Under-prediction rate (UPR), Potential Presence Increment (PPI), and Potential Absence Increment (PAI) from a confusion matrix

🚀 Instalation

You can install the package from GitHub using the following command:

remotes::install_github('rogerio-bio/Fangorn', dependencies=TRUE)

💻 Usage

Onering

library (terra)
library (SDMtune)
library (enmSdmX)
library (dismo)
library (Fangorn)

# Call the function
Onering(model, "model_name", test, variables, p, bg, "maxSSS")

AUC and TSS values:
AUC :  0.7787997 
TSS :  0.3963085 
Predict - Maxent  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s - 00:03:25  

 The Black Riders are shadowing the paths revealed by your distribution analysis... 

Thresholds Information (Maximum test sensitivity plus specificity):
Cloglog Value:  0.3954995 
Test Omission Rate:  0.01282651 

 Frodo maps the impact of the Boyce Index, a burden as heavy as the One Ring.. 
Boyce Index (CBI): 0.6914735 
Final results:
        AUC       TSS Threshold   Omission       CBI
1 0.7787997 0.3963085 0.3954995 0.01282651 0.6914735

palantiris

> palantiris(model_list, test, variables, p, bg, "maxSSS")

AUC and TSS values for lq_01_2.58 :
AUC :  0.7792154 
TSS :  0.3981394 
Predict - Maxent  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s - 00:03:25.9

 Sauron is delving into the heart of your distribution models, searching for control... 

Thresholds Information (Maximum training sensitivity plus specificity) for lq_01_2.58 :
Cloglog Value:  0.3659253 
Training Omission Rate:  0.1966161 
Boyce Index (CBI) for lq_01_2.58 : 0.7155504 

AUC and TSS values for lq_05_2.58 :
AUC :  0.7787997 
TSS :  0.3963085 
Predict - Maxent  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s - 00:03:22.6

 Treebeard is analyzing your request... 

Thresholds Information (Maximum training sensitivity plus specificity) for lq_05_2.58 :
Cloglog Value:  0.3595249 
Training Omission Rate:  0.1904901 
Boyce Index (CBI) for lq_05_2.58 : 0.6914735 
Final results:
       Model       AUC       TSS Threshold  Omission       CBI
1 lq_01_2.58 0.7792154 0.3981394 0.3659253 0.1966161 0.7155504
2 lq_05_2.58 0.7787997 0.3963085 0.3595249 0.1904901 0.6914735

rohirrim

You will need a data.frame object that contains TP, TN, FP and FN information, which are obtained from a confusion matrix.

# Use the function rohirrim to calculate the auxiliary metrics

rohirrim(obj)

#The function provides a data.frame object with all the metricas calculated
  OPR   UPR    PPI    PAI
  0.5   0.5  0.3333 -0.3333

rivendell

This function was added in version 1.0.4

> rivendell(op, test, predictors, jaguar, bg, "maxSSS", remove_prediction = TRUE , identifier = "7.5")

AUC and TSS values for lqph_3.1_7.5 :
AUC :  0.763560275482655 
TSS :  0.382962525464953 
Predict - Maxent  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■  100% | ETA:  0s - 00:06:53.7
Thresholds Information (Maximum training sensitivity plus specificity) for lqph_3.1_7.5 :
Cloglog Value:  0.3858708 
Training Omission Rate:  0.1487696 
Boyce Index (CBI) for lqph_3.1_7.5 : 0.999988351776354

 Model       AUC       TSS Threshold  Omission       CBI       OPR        UPR      PPI        PAI
1  lqph_3.1_7.5 0.7635603 0.3829625 0.3858708 0.1487696 0.9999884 0.9344828 0.01101726 11.87206 -0.4550185
2    lq_0.6_7.5 0.7526314 0.3706172 0.3742082 0.1498881 0.9162143 0.9361958 0.01205490 12.01305 -0.4604223
3  lqph_4.1_7.5 0.7619091 0.3833312 0.3891604 0.1465324 0.9999767 0.9351796 0.01112553 12.01044 -0.4603222

👾 Issues and Bugs

If you find any issues or bugs, please open an issue on the Issues page.

References

Márcia Barbosa, A., Real, R., Muñoz, A.-.-R. and Brown, J.A. (2013), New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity Distrib., 19: 1333-1338. https://doi.org/10.1111/ddi.12100.

Smith A, Murphy S, Henderson D, Erickson K (2023). “Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth.” Global Ecology & Biogeography, 32, -13. doi:10.1111/geb.13628.

Vignali S, Barras AG, Arlettaz R, Braunisch V. SDMtune: An R package to tune and evaluate species distribution models. Ecol Evol. 2020; 10: 11488–11506. https://doi.org/10.1002/ece3.6786.