devpart {BayesComm} | R Documentation |
Runs a deviance partitioning procedure on a set of four bayescomm
objects.
devpart(null, environment, community, full)
null |
a |
environment |
a |
community |
a |
full |
a |
The deviance partitioning procedure determines the proportion of the null deviance explained by each of the other three model types.
The four model types are those created by BC
.
A list containing elements
devpart |
matrix containing the proportion of the null deviance explained by each model for each species |
null |
a matrix containing the mean and 95% credible intervals for the deviance for each species in the null model |
environment |
a matrix containing the mean and 95% credible intervals for the deviance for each species in the evironment model |
community |
a matrix containing the mean and 95% credible intervals for the deviance for each species in the community model |
full |
a matrix containing the mean and 95% credible intervals for the deviance for each species in the full model |
# create fake data n <- 100 nsp <- 4 k <- 3 X <- matrix(c(rep(1, n), rnorm(n * k)), n) # covariate matrix W <- matrix(rnorm(nsp * nsp), nsp) W <- W %*% t(W) / 2 # true covariance matrix B <- matrix(rnorm(nsp * (k + 1), 0, 3), nsp) # true covariates mu <- apply(B, 1, function(b, x) x %*% b, X) # true mean e <- matrix(rnorm(n * nsp), n) %*% chol(W) # true e z <- mu + e # true z Y <- ifelse(z > 0, 1, 0) # true presence/absence # run BC (after removing intercept column from design matrix) null <- BC(Y, X[, -1], model = "null", its = 100) comm <- BC(Y, X[, -1], model = "community",its = 100) envi <- BC(Y, X[, -1], model = "environment", its = 100) full <- BC(Y, X[, -1], model = "full", its = 100) devpart(null, envi, comm, full)