The function spDiag calculates measurements of model fit for
objects of class NNGP and PGLogit.
spDiag(object, sub.sample, ...)A list with the following tags:
a data frame holding Deviance information criterion (DIC) and associated values. Values in DIC include
DIC the criterion (lower is better), D a goodness of fit, and pD the effective
number of parameters, see Spiegelhalter et al. (2002) for details.
a data frame holding D=G+P and associated values. Values in
GPD include G a goodness of fit, P a penalty term, and D the
criterion (lower is better), see Gelfand
and Ghosh (1998) for details.
a scoring rule, see Equation 27 in Gneiting and Raftery (2007) for details.
a data frame hold Watanabe-Akaike information criteria (WAIC) and associated values. Values in
WAIC include LPPD log pointwise predictive density,
P.1 penalty term defined in unnumbered equation above Equation
(11) in Gelman et al. (2014), P.2 an alternative penalty
term defined in Equation (11), and the criteria WAIC.1 and
WAIC.2 (lower is better) computed using P.1 and
P.2, respectively.
if y.rep.samples in object were not
used (or not available), then the newly computed y.rep.samples is returned.
if y.fit.samples in object were not
used (or not available), then the newly computed y.fit.samples is returned.
the index of samples used for the computations.
an object of class NNGP or PGLogit.
an optional list that specifies the samples to included in
the computations. Valid tags are start,
end, and thin. Given the value associated with the tags,
the sample subset is selected using seq(as.integer(start),
as.integer(end), by=as.integer(thin)). The default values are
start=floor(0.5*n.samples), end=n.samples and
thin=1. If sub.samples is not specified, then it is
taken from object, or, if not aviable in object the
default values of start, end, and thin are
used. Note, if the object is a NNGP response model
and n is large, then computing the replicated data needed for
GPD and GRS can take a long time.
currently no additional arguments.
Andrew O. Finley finleya@msu.edu,
Sudipto Banerjee sudipto@ucla.edu
Finley, A.O., A. Datta, S. Banerjee (2022) spNNGP R Package for Nearest Neighbor Gaussian Process Models. Journal of Statistical Software, tools:::Rd_expr_doi("10.18637/jss.v103.i05").
Gelfand A.E. and Ghosh, S.K. (1998). Model choice: a minimum posterior predictive loss approach. Biometrika, 85:1-11.
Gelman, A., Hwang, J., and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24:997-1016.
Gneiting, T. and Raftery, A.E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102:359-378.
Spiegelhalter, D.J., Best, N.G., Carlin, B.P., van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B., 64:583-639.