Variable response curves for fitted models

A view of variable responses in fitted models. Responses based on single or multiple models can be plotted.

Usage

response_curve(models, variable, modelID = NULL, n = 100,
               by_replicates = FALSE, data = NULL, new_data = NULL,
               averages_from = "pr_bg", extrapolate = TRUE,
               extrapolation_factor = 0.1, add_points = FALSE, p_col = NULL,
               l_limit = NULL, u_limit = NULL,
               xlab = NULL, ylab = "Suitability",
               col = "darkblue", ...)

Arguments

models

an object of class fitted_models returned by the fit_selected() function.

variable

(character) name of the variable to be plotted.

modelID

(character) vector of ModelID(s) to be considered in the models object. By default all models are included.Default = NULL.

n

(numeric) an integer guiding the number of breaks. Default = 100

by_replicates

(logical) whether use replicates or full_model to estimate the model's response curve. Default = FALSE.

data

data.frame or matrix of data used in the model calibration step. Default = NULL.

new_data

a SpatRaster, data.frame, or matrix of variables representing the range of variable values in an area of interest. Default = NULL. It must be defined in case the model entered does not explicitly include a data component.

averages_from

(character) specifies how the averages or modes of the variables are calculated. Available options are "pr" (to calculate averages from the presence localities) or "pr_bg" (to use the combined set of presence and background localities). Default is "pr_bg". See details.

extrapolate

(logical) whether to allow extrapolation to study the behavior of the response outside the calibration limits. Ignored if new_data is defined. Default = TRUE.

extrapolation_factor

(numeric) a multiplier used to calculate the extrapolation range. Larger values allow broader extrapolation beyond the observed data range. Default is 0.1.

add_points

(logical) if TRUE, adds the original observed points (0/1) to the plot. Default = FALSE.

p_col

(character) color for the observed points when add_points = TRUE. Any valid R color name or hexadecimal code. Default = "black".

l_limit

(numeric) specifies the lower limit for the variable. Default is NULL, meaning the lower limit will be calculated based on the data's minimum value and the extrapolation_factor (if extrapolation = TRUE).

u_limit

(numeric) specifies the upper limit for the variable. Default is NULL, meaning the upper limit will be calculated based on the data's minimum value and the extrapolation_factor (if extrapolation = TRUE).

xlab

(character) a label for the x axis. The default, NULL, uses the name defined in variable.

ylab

(character) a label for the y axis. Default = "Suitability".

col

(character) color for lines. Default = "darkblue".

...

additional arguments passed to plot.

Value

A plot with the response curve for a variable.

Details

The response curves are generated with all other variables set to their mean values (or mode for categorical variables), calculated either from the presence localities (if averages_from = "pr") or from the combined set of presence and background localities (if averages_from = "pr_bg").

For categorical variables, a bar plot is generated with error bars showing variability across models (if multiple models are included).

See also

bivariate_response()

Examples

# Example with maxnet
# Import example of fitted_models (output of fit_selected())
data(fitted_model_maxnet, package = "kuenm2")

#Response curves
response_curve(models = fitted_model_maxnet,
               variable = "bio_1", by_replicates = TRUE)

response_curve(models = fitted_model_maxnet, variable = "bio_1",
               modelID = "Model_13", by_replicates = TRUE)
#> Error in response_curve(models = fitted_model_maxnet, variable = "bio_1",     modelID = "Model_13", by_replicates = TRUE): 'ModelID' is not correct, check the following: [Model_159, Model_189]

# Example with GLM
# Import example of fitted_models (output of fit_selected())
data(fitted_model_glm, package = "kuenm2")

#Response curves
response_curve(models = fitted_model_glm,
               variable = "bio_1", by_replicates = TRUE)

response_curve(models = fitted_model_glm, variable = "bio_1",
               modelID = "Model_1", by_replicates = TRUE)
#> Error in response_curve(models = fitted_model_glm, variable = "bio_1",     modelID = "Model_1", by_replicates = TRUE): 'ModelID' is not correct, check the following: [Model_86]