Create partial dependence plot(s) to explore the marginal effect of predictors on suitability
Usage
p_pdp(
model,
predictors = NULL,
resolution = 100,
resid = FALSE,
training_data = NULL,
projection_data = NULL,
clamping = FALSE,
rug = FALSE,
colorl = c("#462777", "#6DCC57"),
colorp = "black",
alpha = 0.2,
theme = ggplot2::theme_classic()
)Arguments
- model
A model object of class "gam", "gbm", "glm", "graf", "ksvm", "ksvm", "maxnet”, “nnet", and "randomForest" This model can be found in the first element of the list returned by any function from the fit_, tune_, or esm_ function families
- predictors
character. Vector of predictor name(s) to calculate partial dependence plots. If NULL all predictors will be used. Default NULL
- resolution
numeric. Number of equally spaced points at which to predict suitability values for continuous predictors. Default 50
- resid
logical. Calculate residuals based on training data. Default FALSE
- training_data
data.frame. Database with response (0,1) and predictor values used to fit a model. Default NULL
- projection_data
SpatRaster. Raster layer with environmental variables used for model projection. When this argument is used, function will calculate partial dependence curves distinguishing conditions used in training and projection conditions (i.e., projection data present in projection area but not training). Default NULL
- clamping
logical. Perform clamping. Only for maxent models. Default FALSE
- rug
logical. Display training data as a rug plot on the x-axis. Note: this could be time-consuming for large databases. Default FALSE
- colorl
character. A vector with one or two colors used to color lines. If projection_data argument is used it is necessary to provide two colors. Default c("#462777", "#6DCC57")
- colorp
character. A vector with one color used to color points of residuals, Default "black"
- alpha
numeric. a value between 0 to 1 to control transparency of residual points. Lower values corresponding to more transparent colors. Default 0.2
- theme
ggplot2 theme. Default ggplot2::theme_classic()
Details
This function creates partial dependent plots to explore the marginal effect of predictors on suitability. If projection_data is used, function will extract the minimum and maximum values found in a region or time period to which a model will be projected. If the range of projection data is greater than that of the training data it will be plotted with a different color. Partial dependence curves could be used to interpret a model or to explore how a model may extrapolate outside the environmental conditions used to train the model.
Examples
if (FALSE) { # \dontrun{
library(terra)
library(dplyr)
somevar <- system.file("external/somevar.tif", package = "flexsdm")
somevar <- terra::rast(somevar) # environmental data
names(somevar) <- c("aet", "cwd", "tmx", "tmn")
data(abies)
# set seed
abies2 <- abies %>%
dplyr::select(x, y, pr_ab) %>%
dplyr::group_by(pr_ab) %>%
dplyr::slice_sample(prop = 0.5)
abies2 <- sdm_extract(abies2,
x = "x",
y = "y",
env_layer = somevar
)
abies2 <- part_random(abies2,
pr_ab = "pr_ab",
method = c(method = "kfold", folds = 5)
)
svm_t1 <- fit_svm(
data = abies2,
response = "pr_ab",
predictors = c("aet", "cwd", "tmx", "tmn"),
partition = ".part",
thr = c("max_sens_spec")
)
# Partial depence plot
p_pdp(model = svm_t1$model, training_data = abies2)
p_pdp(model = svm_t1$model, training_data = abies2, predictors = c("aet", "cwd"))
p_pdp(model = svm_t1$model, training_data = abies2, resolution = 5)
p_pdp(model = svm_t1$model, training_data = abies2, resolution = 50)
p_pdp(model = svm_t1$model, training_data = abies2, resid = TRUE)
p_pdp(
model = svm_t1$model, training_data = abies2, resid = TRUE,
colorl = "black", colorp = "red", alpha = 0.1
)
p_pdp(
model = svm_t1$model, training_data = abies2, resid = TRUE,
colorl = "black", colorp = "red", alpha = 0.1, rug = TRUE
)
# Partial depence plot for training and projection condition found in a projection area
plot(somevar[[1]], main = "Projection area")
p_pdp(model = svm_t1$model, training_data = abies2, projection_data = somevar)
p_pdp(
model = svm_t1$model, training_data = abies2, projection_data = somevar,
colorl = c("#CC00FF", "#CCFF00")
)
p_pdp(
model = svm_t1$model, training_data = abies2, projection_data = somevar,
colorl = c("#CC00FF", "#CCFF00"), resid = TRUE, colorp = "gray"
)
p_pdp(
model = svm_t1$model, training_data = abies2, projection_data = somevar,
colorl = c("#CC00FF", "#CCFF00"), resid = TRUE, colorp = "gray", rug = TRUE,
theme = ggplot2::theme_dark()
)
} # }