webSDM (version 1.1-5)

predict.SDMfit: Predicts with a local model

Description

Computes predicted values for a local model, i.e., a fitted SDMfit object This is sequentially called, for each species, by the function trophicSDM.predict

Usage

# S3 method for SDMfit
predict(object, newdata, pred_samples = NULL, prob.cov = TRUE, ...)

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. If prob.cov = TRUE, it returns a list containing:

• predictions.prob: Predicted probabilities of presence.

• predictions.bin: Predicted probabilities of presence.

Arguments

object

A SDMfit object, typically obtained with trophicSDM() and available in the field $model of a trophicSDMfit object

newdata

A matrix containing both environmental covariates and the biotic variables that the local model uses to predict the species distribution.

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.

prob.cov

Only for presence-absence data. If set to FALSE, it gives back also predicted presence-absences (which is then used by trophicSDM.predict to predict the predators).

...

additional arguments

Author

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")
# In order to predict non-basal species, we need to also provide
# the predicted occurrences of its preys. Here we compute the probability of
# presence of species Y4 at environemntal conditions c(0.5,0.5)
# when its prey Y3 is present.
predict(m$model$Y4, newdata = data.frame(X_1 = 0.5, X_2 = 0.5, Y3 = 1), pred_samples = 10)