Repositorio
Institucional

Salas-Eljatib, Christian

Fuentes-Ramirez, Andres#Gregoire, Timothy G.#Altamirano, Adison#Yaitul, Valeska

A study on the effects of unbalanced data when fitting logistic regression models in ecology

ELSEVIER SCIENCE BV

ECOLOGICAL INDICATORS

en

Binary variables have two possible outcomes: occurrence or non-occurrence of an event (usually with 1 and 0 values, respectively). Binary data are common in ecology, including studies of presence/absence, alive/dead, and change/no-change. Logistic regression analysis has been widely used to model binary response variables. Unbalanced data (i.e., an extremely larger proportion of zeros than ones) are often found across a variety of ecological datasets. Sometimes the data are balanced (i.e., same amount of zeros and ones) before fitting the model, however, the statistical implications of balancing (or not) the data remain unclear. We assessed the statistical effects of balancing data when fitting a logistic regression model by studying both its statistical properties of the estimated parameters and its predictive capabilities. We used a base forest-mortality model as reference, and by using stochastic simulations representing different configurations of 0/1 data in a sample (unbalanced data scenarios), we fitted the logistic regression model by maximum likelihood. For each scenario we computed the bias and variance of the estimated parameters and several prediction indexes. We found that the variability of the estimated parameters is affected, with the balanced-data scenario having the lowest variability, thus, affecting the statistical inference as well. Furthermore, the prediction capabilities of the model are altered by balancing the data, with the balanced-data scenario having the better sensitivity/specificity ratio. Balancing, or not, the data to be used for fitting a logistic regression models may affect the conclusion that can arise from the fitted model and its subsequent applications.

Artículo original

This study was supported by the Chilean research grant Fondecyt No. 1151495. AFR is supported by a Postdoctoral Scholarship from Vicerrectoria de Investigacion y Postgrado, Universidad de La Frontera, Temuco, Chile.

10.1016/j.ecolind.2017.10.030

pdf

Statistical inference# Model prediction# Logit model# Binary variable# Bias# Precision

Statistical inference# Model prediction# Logit model# Binary variable# Bias# Precision

http://dx.doi.org/10.1016/j.ecolind.2017.10.030

Biodiversity Conservation# Environmental Sciences

Inferencia estadística# Predicción de modelos# modelo logístico# variable binaria# Inclinación# Precisión

Inferencia estadística# Predicción de modelos# modelo logístico# variable binaria# Inclinación# Precisión

WOS:000430634500051

A study on the effects of unbalanced data when fitting logistic regression models in ecology

Artículo original

version publicada

ELSEVIER SCIENCE BV

ECOLOGICAL INDICATORS

Conservación de la Biodiversidad# Ciencias Ambientales

1470-160X

en

ANID FONDECYT 1151495

This study was supported by the Chilean research grant Fondecyt No. 1151495. AFR is supported by a Postdoctoral Scholarship from Vicerrectoria de Investigacion y Postgrado, Universidad de La Frontera, Temuco, Chile.

pdf

suscripción#verde

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1495

1501