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Compute Marginal Response Curve

Source: R/model_diagnostics.R
maxent_response_curve.Rd

Generates a response curve by varying one environmental variable from its minimum to maximum value while holding all other variables at their mean. Applies the Java Maxent cloglog transform, matching the output of Java Maxent and dismo: $$cloglog = 1 - exp(-exp(H) \cdot raw)$$

Usage

maxent_response_curve(
  model,
  env_grids,
  feature_names,
  var_index,
  n_steps = 100L
)

Arguments

model

External pointer to a FeaturedSpace object.

env_grids

List of external pointers to Grid<float> objects.

feature_names

Character vector of environment variable names.

var_index

0-based index of the variable to vary.

n_steps

Number of steps across the variable range (default 100).

Value

A data.frame with columns:

value

Environmental variable value

prediction

Cloglog-transformed prediction

See also

maxent_response_curve_fixed

Examples

# \donttest{
set.seed(42)
n <- 50L; idx <- c(5L, 15L, 25L, 35L, 45L)
env <- list(temp = runif(n), precip = runif(n))
feats <- maxent_generate_features(env, types = "linear")
model <- maxent_featured_space(n, idx, feats)
maxent_fit(model, max_iter = 100)
#> $loss
#> [1] 3.781356
#> 
#> $entropy
#> [1] 3.887762
#> 
#> $iterations
#> [1] 100
#> 
#> $converged
#> [1] FALSE
#> 
#> $lambdas
#> [1] 0.6202339 0.4108689
#> 
g1 <- maxent_grid_from_matrix(matrix(env$temp, 5, 10),
        -120, 35, 1, name = "temp")
g2 <- maxent_grid_from_matrix(matrix(env$precip, 5, 10),
        -120, 35, 1, name = "precip")
curve <- maxent_response_curve(model, list(g1, g2),
           c("temp", "precip"), var_index = 0)
plot(curve$value, curve$prediction, type = "l")

# }