modEvA (version 2.0)

predDensity: Plot the density of predicted values for presences and absences.

Description

This function produces a histogram and/or a kernel density plot of predicted values for a binomial GLM, by default separately for the observed presences and absences, given a model object or a vector of predicted values and (optionally) a vector of the corresponding observed values.

Usage

predDensity(model = NULL, obs = NULL, pred = NULL, separate = TRUE, 
type = c("both"), legend.pos = "topright")

Arguments

model

a model object of class "glm" and family "binomial".

obs

numeric vector of the observed data, consisting of zeros and ones. This argument is ignored if 'model' is provided.

pred

numeric vector of the values predicted by a GLM of the observed data. This argument is ignored if 'model' is provided. Must be of the same length and in the same order as 'obs'.

separate

logical value indicating whether prediction densities should be computed separately for observed presences (ones) and absences (zeros). Defaults to TRUE, but is changed to FALSE if either 'model' or 'obs' not provided.

type

character vector specifying whether to produce a "histogram", a "density" plot, or "both" (the default). Partial argument matching is used.

legend.pos

character specifying the position for the legend, or "n" for no legend. Position can be "topright" (the default), "topleft, "bottomright"", "bottomleft", "top", "bottom", "left", "right", or "center". Partial argument matching is used.

Value

This function outputs and plots the object(s) specified in 'type' -- by default, a density object and a histogram.

Details

For more details, please refer to the documentation of the functions mentioned under "See Also".

See Also

hist, density

Examples

# NOT RUN {
# load sample models:
data(rotif.mods)

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

predDensity(model = mod)

predDensity(model = mod, type = "histogram")

predDensity(model = mod, type = "density")

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

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

predDensity(obs = presabs, pred = prediction)

predDensity(pred = prediction)
# }