
Gets regression coefficients (eventually standardised) of a fitted trophicSDM. p-values or credible intervals are returned when available.
# S3 method for trophicSDMfit
coef(object, standardise = FALSE, level = 0.95, ...)
A list containing, for each species, the inferred coefficients (with credible intervals or p-values when available).
A trophicSDMfit object obtained with trophicSDM()
Whether to standardise regression coefficients. Default to FALSE. If TRUE, coefficients are standardised using the latent variable standardisation (see Grace et al. 2018) for more details.
The confidence level of credible intervals, only available for stan_glm method. Default to 0.95.
additional arguments
Giovanni Poggiato
Grace, J. B., Johnson, D. J., Lefcheck, J. S., and Byrnes, J. E. K.. 2018. Quantifying relative importance: computing standardized effects in models with binary outcomes. Ecosphere 9(6):e02283.
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")
# unstandardised regression coefficients
coef(m)
#standardised regression coefficients with 90% credible intervals
coef(m, standardised = TRUE, level = 0.9)
# Run the same model using glm as fitting method
m = trophicSDM(Y, X, G, env.formula,
family = binomial(link = "logit"), penal = NULL,
mode = "prey", method = "glm")
# Now we have p-values instead of credible intervals
coef(m)
# Notice that unstandardised coefficients are always accessible
# in the fitted model:
m$coef
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