plotResponse: Plot Response Curve

Description Usage Arguments Details Value Author(s) Examples

View source: R/plotResponse.R

Description

Plot the Response Curve of the given environmental variable.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
plotResponse(
  model,
  var,
  type = NULL,
  only_presence = FALSE,
  marginal = FALSE,
  fun = mean,
  rug = FALSE,
  color = "red"
)

Arguments

model

SDMmodel or SDMmodelCV object.

var

character. Name of the variable to be plotted.

type

character. The output type used for "Maxent" and "Maxnet" methods, possible values are "cloglog" and "logistic", default is NULL.

only_presence

logical, if TRUE it uses only the presence locations when applying the function for the marginal response, default is FALSE.

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, default is mean.

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".

Details

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

Value

A ggplot object.

Author(s)

Sergio Vignali

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
# 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 and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
                   env = predictors, categorical = "biome")

# Train a model
model <- train(method = "Maxnet", data = data, fc = "lq")

# 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
folds <- randomFolds(data, k = 4, only_presence = TRUE)
model <- train(method = "Maxnet", data = data, fc = "lq", folds = folds)

# 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")

SDMtune documentation built on July 17, 2021, 9:06 a.m.