response_curve {enmpa} | R Documentation |
Variable response curves for GLMs
Description
A view of variable responses in models. Responses based on single or multiple models can be provided.
Usage
response_curve(fitted, variable, data = NULL, modelID = NULL, n = 100,
new_data = NULL, extrapolate = TRUE, xlab = NULL,
ylab = "Probability", col = "red", ...)
Arguments
fitted |
an object of class |
variable |
(character) name of the variables to be plotted. |
data |
data.frame or matrix of data used in the model calibration step. Default = NULL. |
modelID |
(character) vector of ModelID(s) to be considered when the
fitted models is an |
n |
(numeric) an integer guiding the number of breaks. Default = 100 |
new_data |
a |
extrapolate |
(logical) whether to allow extrapolation to study the
behavior of the response outside the calibration limits. Ignored if
|
xlab |
(character) a label for the x axis. The default, NULL, uses the
name defined in |
ylab |
(character) a label for the y axis. Default = "Probability". |
col |
(character) color for lines. Default = "red". |
... |
additional arguments passed to |
Details
The function calculates these probabilities by focusing on a single environmental variable while keeping all other variables constant at their mean values.
When responses for multiple models are to be plotted, the mean and confidence intervals for the set of responses are calculated using a GAM.
Value
A plot with the response curve for a variable
.
Examples
# Load a fitted selected model
data(sel_fit, package = "enmpa")
# Response curve for single models
response_curve(sel_fit$ModelID_7, variable = "bio_1")
# Response curve when model(s) are in a list (only one model in this one)
response_curve(sel_fit, variable = "bio_12")