predictPotential {webSDM}

Predicts species potential niche

Description

Computes predicted values of the potential niches of species from the fitted trophicSDMfit model at environmental conditions specified by Xnew. Predictions are obtained by setting preys to present when mode = "prey" or setting predators to absent when mode = "predator".

Usage

predictPotential(
  tSDM,
  Xnew = NULL,
  pred_samples = NULL,
  verbose = FALSE,
  fullPost = TRUE
)

Arguments

tSDM

A trophicSDMfit object obtained with trophicSDM()

Xnew

a matrix specifying the environmental covariates for the predictions to be made. If NULL (default), predictions are done on the training dataset (e.g. by setting Xnew = tSDM$data$X).

pred_samples

Number of samples to draw from species posterior predictive distribution when method = "stan_glm". If NULL, set by the default to the number of iterations/10.

verbose

Whether to print advances of the algorithm.

fullPost

Optional parameter for stan_glm only. Whether to give back the full posterior predictive distribution (default, fullPost = TRUE) or just the posterior mean, and 2.5% and 97.5% quantiles.

Value

A list containing for each species the predicted value at each sites. If method = "stan_glm", then each element of the list is a sites x pred_samples matrix containing the posterior predictive distribution of the species at each sites.

Author(s)

Giovanni Poggiato and Jérémy Andréoletti

Examples

data(Y, X, G)
# define abiotic part of the model
env.formula = "~ X_1 + X_2"
# Run the model with bottom-up control using stan_glm as fitting method and no penalisation
# (set iter = 1000 to obtain reliable results)
m = trophicSDM(Y, X, G, env.formula, iter = 100,
               family = binomial(link = "logit"), penal = NULL, 
               mode = "prey", method = "stan_glm")
# Obtain 100 draws from the posterior predictive distribution of species potential niche
# (pred_samples = 50)
# Since we don't specify Xnew, the function sets Xnew = X by default
Ypred = predictPotential(m, fullPost = TRUE, pred_samples = 50)
# We can ask the function to only give back posterior mean and 95% credible intervals with
# fullPost = FALSE

Ypred = predictPotential(m, fullPost = FALSE, pred_samples = 50)

#' We can now evaluate species probabilities of presence for the enviromental
# conditions c(0.5, 0.5)
predictPotential(m, Xnew = data.frame(X_1 = 0.5, X_2 = 0.5), pred_samples = 50)

# If we fit the model using in a frequentist  way (e.g. glm)
m = trophicSDM(Y, X, G, env.formula, 
               family = binomial(link = "logit"), penal = NULL, 
               mode = "prey", method = "glm")
# We are obliged to set pred_samples = 1 
# (this is done by default if pred_samples is not provided)
# In the frequentist case, fullPost is useless.
Ypred = predictPotential(m, pred_samples = 1)