ppcAbund: Function for performing posterior predictive checks

In spAbundance: Univariate and Multivariate Spatial Modeling of Species Abundance

Function for performing posterior predictive checks

Description

Function for performing posterior predictive checks on spAbundance model objects.

Usage

ppcAbund(object, fit.stat, group, type = 'marginal', ...)

Arguments

object	an object of class NMix, spNMix, msNMix, lfMsNMix, sfMsNMix, abund, spAbund, msAbund, lfMsAbund, sfMsAbund, DS, spDS, msDS, lfMsDS, sfMsDS.
fit.stat	a quoted keyword that specifies the fit statistic to use in the posterior predictive check. Supported fit statistics are "freeman-tukey" and "chi-squared".
group	a positive integer indicating the way to group the abundance data for the posterior predictive check. Value 0 will not group the data and use the raw counts, 1 will group values by row (site), and value 2 will group values by column (replicate).
type	a character string indicating whether fitted values should be generated conditional on the estimated latent abundance values (type = 'conditional') estimated during the model or based on the marginal expected abundance values (type = 'marginal'). This is only relevant for N-mixture models.
...	currently no additional arguments

Value

An object of class ppcAbund that is a list comprised of:

fit.y	a numeric vector of posterior samples for the fit statistic calculated on the observed data when object is of class NMix, spNMix, abund, spAbund, DS, or spDS. When object is of class msNMix, lfMsNMix, sfMsNMix, msAbund, lfMsAbund, sfMsAbund, msDS, lfMsDS, sfMsDS, this is a numeric matrix with rows corresponding to posterior samples and columns corresponding to species.
fit.y.rep	a numeric vector of posterior samples for the fit statistic calculated on a replicate data set generated from the model when object is of class NMix, spNMix, abund, spAbund, DS, spDS. When object is of class msNMix, lfMsNMix, sfMsNMix, msAbund, lfMsAbund, sfMsAbund, msDS, lfMsDS, sfMsDS, this is a numeric matrix with rows corresponding to posterior samples and columns corresponding to species.
fit.y.group.quants	a matrix consisting of posterior quantiles for the fit statistic using the observed data for each unique element the fit statistic is calculated for (i.e., observations when group = 0, sites when group = 1, replicates when group = 2) when object is of class NMix, spNMix, abund, spAbund, DS, or spDS. When object is of class msNMix, lfMsNMix, sfMsNMix, msAbund, lfMsAbund, sfMsAbund, msDS, lfMsDS, sfMsDS, this is a three-dimensional array with the additional dimension corresponding to species.
fit.y.rep.group.quants	a matrix consisting of posterior quantiles for the fit statistic using the model replicated data for each unique element the fit statistic is calculated for (i.e., observations when group = 0, sites when group = 1, replicates when group = 2) when object is of class NMix, spNMix, abund, spAbund, DS, spDS. When object is of class msNMix, sfMsNMix, msAbund, lfMsAbund, sfMsAbund, msDS, lfMsDS, sfMsDS, this is a three-dimensional array with the additional dimension corresponding to species.

The return object will include additional objects used for standard extractor functions.

Note

ppcAbund will return an error for Gaussian or zero-inflated Gaussian models. For Gaussian models, standard residual diagnostics can be used to assess model fit.

Author(s)

Jeffrey W. Doser [e-mail],

Examples

set.seed(1010)
J.x <- 10
J.y <- 10
J <- J.x * J.y
n.rep <- sample(3, J, replace = TRUE)
beta <- c(0, -1.5)
p.abund <- length(beta)
alpha <- c(0.5, 1.2, -0.5)
p.det <- length(alpha)
mu.RE <- list()
p.RE <- list()
phi <- 3/.6
sigma.sq <- 2
kappa <- 0.3
sp <- FALSE 
cov.model <- 'exponential'
dist <- 'NB'
dat <- simNMix(J.x = J.x, J.y = J.y, n.rep = n.rep, beta = beta, alpha = alpha,
               kappa = kappa, mu.RE = mu.RE, p.RE = p.RE, sp = sp, 
               phi = phi, sigma.sq = sigma.sq, cov.model = cov.model, 
               family = 'NB')

y <- dat$y
X <- dat$X
X.p <- dat$X.p

abund.covs <- X
colnames(abund.covs) <- c('int', 'abund.cov.1')

det.covs <- list(det.cov.1 = X.p[, , 2], det.cov.2 = X.p[, , 3])

data.list <- list(y = y, abund.covs = abund.covs,
                  det.covs = det.covs)

# Priors
prior.list <- list(beta.normal = list(mean = rep(0, p.abund), 
                                      var = rep(100, p.abund)),
                   alpha.normal = list(mean = rep(0, p.det),
                                       var = rep(2.72, p.det)), 
                   kappa.unif = c(0, 10)) 
# Starting values
inits.list <- list(alpha = 0, beta = 0, kappa = kappa, 
                   N = apply(y, 1, max, na.rm = TRUE))


tuning <- 0.5
n.batch <- 4
batch.length <- 25
n.burn <- 50
n.thin <- 1
n.chains <- 1

out <- NMix(abund.formula = ~ abund.cov.1,
            det.formula = ~ det.cov.1 + det.cov.2, 
            data = data.list, 
            n.batch = n.batch, 
            batch.length = batch.length, 
            inits = inits.list, 
            priors = prior.list, 
            accept.rate = 0.43, 
            n.omp.threads = 1, 
            verbose = TRUE, 
            n.report = 1,
            n.burn = n.burn,
            n.thin = n.thin,
            n.chains = n.chains) 

# Posterior predictive check
ppc.out <- ppcAbund(out, fit.stat = 'chi-squared', group = 0)
summary(ppc.out)

spAbundance documentation built on Oct. 6, 2024, 1:08 a.m.