spDiag {spNNGP} | R Documentation |
Model fit diagnostics
Description
The function spDiag
calculates measurements of model fit for
objects of class NNGP
and PGLogit
.
Usage
spDiag(object, sub.sample, ...)
Arguments
object |
an object of class |
sub.sample |
an optional list that specifies the samples to included in
the computations. Valid tags are |
... |
currently no additional arguments. |
Value
A list with the following tags:
DIC |
a data frame holding Deviance information criterion (DIC) and associated values. Values in |
GPD |
a data frame holding D=G+P and associated values. Values in
|
GRS |
a scoring rule, see Equation 27 in Gneiting and Raftery (2007) for details. |
WAIC |
a data frame hold Watanabe-Akaike information criteria (WAIC) and associated values. Values in
|
y.rep.samples |
if |
y.fit.samples |
if |
s.indx |
the index of samples used for the computations. |
Author(s)
Andrew O. Finley finleya@msu.edu,
Sudipto Banerjee sudipto@ucla.edu
References
Finley, A.O., A. Datta, S. Banerjee (2022) spNNGP R Package for Nearest Neighbor Gaussian Process Models. Journal of Statistical Software, 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.