modEvA (version 3.5)

prevalence: Prevalence

Description

For building and evaluating species distribution models, the porportion of presences of the species may be an issue to take into account (e.g. Jimenez-Valverde & Lobo 2006, Barbosa et al. 2013). The prevalence function calculates this measure.

Usage

prevalence(obs, model = NULL, event = 1, na.rm = TRUE)

Value

Numeric value of the prevalence of event in the obs vector.

Arguments

obs

a vector or a factor of binary observations (e.g. 1 vs. 0, male vs. female, disease vs. no disease, etc.). This argument is ignored if 'model' is provided.

model

alternatively to 'obs', a binary-response model object of class "glm", "gam", "gbm", "randomForest" or "bart". If this argument is provided, 'obs' will be extracted with mod2obspred.

event

the value whose prevalence we want to calculate (e.g. 1, "present", etc.). This argument is ignored if 'model' is provided.

na.rm

logical, whether NA values should be excluded from the calculation. The default is TRUE.

Author

A. Marcia Barbosa

References

Barbosa A.M., Real R., Munoz A.R. & Brown J.A. (2013) New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions, in press

Jimenez-Valverde A. & Lobo J.M. (2006) The ghost of unbalanced species distribution data in geographical model predictions. Diversity and Distributions, 12: 521-524.

See Also

evenness

Examples

# calculate prevalence from binary vectors:

(x <- rep(c(0, 1), each = 5))

(y <- c(rep(0, 3), rep(1, 7)))

(z <- c(rep(0, 7), rep(1, 3)))

prevalence(x)

prevalence(y)

prevalence(z)


(w <- c(rep("yes", 3), rep("nope", 7)))

prevalence(w, event = "yes")


# calculate prevalence from a model object:

data(rotif.mods)

prevalence(mod = rotif.mods$models[[1]])