SDMtune (version 0.1.0)

plotResponse: Plot Response Curve

Description

Plot the Response Curve of the given environmental variable.

Usage

plotResponse(model, var, type, marginal = FALSE, fun = mean,
  clamp = TRUE, rug = FALSE, color = "red")

Arguments

model

'>SDMmodel or '>SDMmodelCV object.

var

character. Name of the variable to be plotted.

type

character. Output type, see predict,SDMmodel-method for more details.

marginal

logical, if TRUE it plots the marginal response curve, default is FALSE.

fun

function used to compute the level of the other variables for marginal curves, possible values are mean and median, default is mean.

clamp

logical for clumping during prediction, default is TRUE.

rug

logical, if TRUE it adds the rug plot for the presence and absence/background locations, available only for continuous variables, default is FALSE.

color

The color of the curve, default is "red".

Value

A ggplot object.

Details

Note that fun is not a character argument, you must use mean and not "mean".

Examples

# NOT RUN {
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd", full.names = TRUE)
predictors <- raster::stack(files)

# Prepare presence locations
p_coords <- condor[, 1:2]

# Prepare background locations
bg_coords <- dismo::randomPoints(predictors, 5000)

# Create SWD object
presence <- prepareSWD(species = "Vultur gryphus", coords = p_coords,
                       env = predictors, categorical = "biome")
bg <- prepareSWD(species = "Vultur gryphus", coords = bg_coords,
                 env = predictors, categorical = "biome")

# Train a model
model <- train(method = "Maxnet", p = presence, a = bg, fc = "l")

# Plot cloglog response curve for a continuous environmental variable (bio1)
plotResponse(model, var = "bio1", type = "cloglog")

# Plot marginal cloglog response curve for a continuous environmental
# variable (bio1)
plotResponse(model, var = "bio1", type = "cloglog", marginal = TRUE)

# Plot logistic response curve for a continuous environmental variable
# (bio12) adding the rugs and giving a custom color
plotResponse(model, var = "bio12", type = "logistic", rug = TRUE,
             color = "blue")

# Plot response curve for a categorical environmental variable (biome) giving
# a custom color
plotResponse(model, var = "biome", type = "logistic", color = "green")

# Train a model with cross validation
model <- train(method = "Maxnet", p = presence, a = bg, fc = "lq", rep = 4)

# Plot cloglog response curve for a continuous environmental variable (bio17)
plotResponse(model, var = "bio1", type = "cloglog")

# Plot logistic response curve for a categorical environmental variable
# (biome) giving a custom color
plotResponse(model, var = "biome", type = "logistic", color = "green")
# }