Boyce: Boyce Index

In modEvA: Model Evaluation and Analysis

Boyce Index

Description

This function computes the (continuous) Boyce index (Boyce 2002; Hirzel et al. 2006) for either: 1) a model object; or 2) two paired numeric vectors of observed (binary, 1 for occurrence vs. 0 for no occurrence records) and predicted (continuous, e.g. occurrence probability) values; or 3) a set of presence point coordinates and a raster map with the predicted values for the entire model evaluation area. This metric is designed for evaluating model predictions against presence/background data (i.e. presence/available, where "available" includes both presences and absences; Boyce 2002), so the function uses the model predictions for the presence sites (ones) against the predictions for the entire dataset (ones and zeros).

Usage

Boyce(model = NULL, obs = NULL, pred = NULL, n.bins = NA,
bin.width = "default", res = 100, method = "spearman", rm.dup.classes = FALSE,
rm.dup.points = FALSE, pbg = FALSE, plot = TRUE, plot.lines = TRUE,
plot.values = TRUE, plot.digits = 3, na.rm = TRUE, ...)

Arguments

model	a binary-response model object of class "glm", "gam", "gbm", "randomForest" or "bart". If this argument is provided, 'obs' and 'pred' will be extracted with mod2obspred. Alternatively, you can input the 'obs' and 'pred' arguments (e.g. for external test data) instead of 'model'.
obs	alternatively to 'model' and together with 'pred', a numeric vector of observed presences (1) and absences (0) of a binary response variable. Alternatively (and if 'pred' is a 'SpatRaster'), a two-column matrix or data frame containing, respectively, the x (longitude) and y (latitude) coordinates of the presence points, in which case the 'obs' vector will be extracted with ptsrast2obspred. This argument is ignored if 'model' is provided.
pred	alternatively to 'model' and together with 'obs', a vector with the corresponding predicted values of presence probability, habitat suitability, environmental favourability or alike. Must be of the same length and in the same order as 'obs'. Alternatively (and if 'obs' is a set of point coordinates), a 'SpatRaster' map of the predicted values for the entire evaluation region, in which case the 'pred' vector will be extracted with ptsrast2obspred. This argument is ignored if 'model' is provided.
n.bins	number of classes or bins (e.g. 10) in which to group the 'pred' values, or a vector with the bin thresholds. If n.bins = NA (the default), a moving window is used (see next parameters), so as to compute the "continuous Boyce index" (Hirzel et al. 2006).
bin.width	width of the moving window (if n.bins = NA), in the units of 'pred' (e.g. 0.1). By default, it is 1/10th of the 'pred' range).
res	resolution of the moving window (if n.bins = NA). By default it is 100 focals, providing 100 moving bins).
method	argument to be passed to cor indicating which correlation coefficient to use. The default is 'spearman' as per Boyce et al. (2002), but 'pearson' and 'kendall' can also be used.
rm.dup.classes	if TRUE (as in 'ecospat::ecospat.boyce') and if there are different bins with the same predicted/expected ratio, only one of each is used to compute the correlation. See Examples.
rm.dup.points	if TRUE and if 'pred' is a SpatRaster and if there are repeated points within the same pixel, a maximum of one point per pixel is used to compute the presences. See examples in ptsrast2obspred. The default is FALSE.
pbg	logical value to pass to inputMunch indicating whether to use presence/background (rather than presence/absence) data. Default FALSE.
plot	logical, whether or not to plot the predicted/expected ratio against the median prediction of each bin. Defaults to TRUE.
plot.lines	logical, whether or not to add lines connecting the points in the plot (if plot=TRUE). Defaults to TRUE.
plot.values	logical, whether or not to show in the plot the value of the Boyce index. Defaults to TRUE.
plot.digits	number of digits to which the value in the plot should be rounded (if 'plot' and 'plot.values' are TRUE). Defaults to 3.
na.rm	Logical value indicating if missing values should be removed from computations. The default is TRUE.
...	some additional arguments can be passed to plot, e.g. 'main' or 'xlim'.

Details

The Boyce index is the correlation between model predictions and area-adjusted frequencies (i.e., observed vs. expected proportion of occurrences) along different prediction classes (bins). In other words, it measures how model predictions differ from a random distribution of the observed presences across the prediction gradient (Boyce et al. 2002). It can take values between -1 and 1. Positive values indicate that presences are more frequent than expected by chance (given availability) in areas with higher predicted values. Values close to zero mean that predictions are no better than random (i.e. presences are distributed among prediction classes as expected by chance), and negative values indicate counter predictions (i.e., presences are more frequent in areas with lower predicted values).

The R code is largely based on the 'ecospat.boyce' function in the ecospat package (version 3.2.1), but it is modified to match the input types in the remaining functions of 'modEvA', and to return a more complete and informative output.

Value

This function returns a list with the following components:

bins	a data frame with the number of values in each bin, their median and range of predicted values, and the corresponding predicted/expected ratio of presences.
B	the numeric value of the Boyce index, i.e. the coefficient of correlation between the median predicted value in each bin and the corresponding predicted/expected ratio.

If plot=TRUE (the default), the function also plots the predicted/expected ratio for the utilized bins along the prediction range. A good model should yield a monotonically increasing curve (but see Note).

Note

This index is designed for evaluating predictions of habitat suitability, not presence probability (which also depends on the species' presence/absence ratio: rare species do not usually show high proportions of presences, even in highly suitable areas). If your predictions are of presence probability with a prevalence different from 50% presences, you should convert those predictions e.g. with the Fav function of package fuzzySim, before evaluating them with the Boyce index.

In bins with overly small sample sizes, the comparison between median prediction and random expectation may not be meaningful, although these bins will equally contribute to the overall Boyce index. When there are bins with less than 30 values, a warning is emitted and their points are plotted in red, but mind that 30 is a largely arbitrary number. See the $bins$bin.N section of the console output, and use the 'bin.width' argument to enlarge the bins.

Author(s)

A. Marcia Barbosa, with significant chunks of code from the 'ecospat::ecospat.boyce' function by Blaise Petitpierre and Frank Breiner (ecospat package version 3.2.1).

References

Boyce, M.S., P.R. Vernier, S.E. Nielsen & F.K.A. Schmiegelow (2002) Evaluating resource selection functions. Ecological Modelling 157: 281-300

Hirzel, A.H., G. Le Lay, V. Helfer, C. Randin & A. Guisan (2006) Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling 199: 142-152

Examples

# load sample models:
data(rotif.mods)

# choose a particular model to play with:
mod <- rotif.mods$models[[1]]

# compute the Boyce index:
Boyce(model = mod, main = "My model Boyce plot")
Boyce(model = mod, main = "My model Boyce plot", rm.dup.classes = TRUE)


# you can also use vectors of observed and predicted values
# instead of a model object:

presabs <- mod$y
prediction <- mod$fitted.values

Boyce(obs = presabs, pred = prediction)


# 'obs' can also be a table of presence point coordinates
# and 'pred' a SpatRaster of predicted values

modEvA documentation built on Oct. 30, 2024, 1:06 a.m.