devpart: Deviance partitioning

In BayesComm: Bayesian Community Ecology Analysis

Description

Runs a deviance partitioning procedure on a set of four bayescomm objects.

Usage

devpart(null, environment, community, full)

Arguments

null	a bayescomm object containing a 'null' model
environment	a bayescomm object containing an 'environment' model
community	a bayescomm object containing a 'community' model
full	a bayescomm object containing a 'full' model

Details

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.

Value

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

See Also

BC

Examples

# 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)

Example output

$devpart
          env        com      full total
sp1 0.5656081 0.01829010 0.5442947     1
sp2 0.4722921 0.03302527 0.5550907     1
sp3 0.5384847 0.01865383 0.5300307     1
sp4 0.5409303 0.05344746 0.5952947     1

$null
           sp1      sp2      sp3      sp4
Mean  59.72335 36.55178 35.89692 49.25254
25%   56.45592 32.78801 31.92609 46.98274
97.5% 68.29313 47.48585 44.54037 56.46274

$environment
           sp1      sp2      sp3      sp4
Mean  25.94334 19.28866 16.56698 22.61035
25%   21.08225 14.98754 10.44144 18.30549
97.5% 48.68486 35.60690 35.93843 40.87600

$community
           sp1      sp2      sp3      sp4
Mean  58.63101 35.34465 35.22730 46.62012
25%   55.45385 32.50487 32.26385 43.55613
97.5% 66.40259 43.42552 48.57801 54.71969

$full
           sp1      sp2      sp3      sp4
Mean  27.21625 16.26223 16.87045 19.93277
25%   21.69271 13.37547 13.01667 14.32622
97.5% 44.03784 28.18769 31.47239 36.16930

BayesComm documentation built on May 2, 2019, 1:43 p.m.