Construct an empirical HPD interval from samples
evalZdensity: Evaluate the (negative log) unnormalized marginal z density...
In glmBfp: Bayesian Fractional Polynomials for GLMs
Description
Usage
Arguments
Value
Description
Based on the result list from glmBayesMfp, for the first model
in the list, the marginal z density can be evaluated.
Usage
1
2
evalZdensity(config, object, zValues, conditional = FALSE, debug = FALSE,
higherOrderCorrection = FALSE)
Arguments
config
the configuration of a single GlmBayesMfp model. The
null model is not allowed. It is interpreted in the context of
object.
object
the GlmBayesMfp object
zValues
the z values
conditional
return the approximate *conditional* density f(y | z)?
(not default)
debug
print debugging information? (not default)
higherOrderCorrection
should a higher-order correction of the
Laplace approximation be used? (not default)
Value
the negative log marginal unnormalized density values at the
zValues. (Note the words “negative”, “log”, and
“unnormalized” !!!)
glmBfp documentation built on June 7, 2017, 3:01 p.m.
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Description Usage Arguments Value
Description
Based on the result list from glmBayesMfp, for the first model
in the list, the marginal z density can be evaluated.
Usage
1 2 | evalZdensity(config, object, zValues, conditional = FALSE, debug = FALSE,
higherOrderCorrection = FALSE)
|
Arguments
config |
the configuration of a single |
object |
the |
zValues |
the z values |
conditional |
return the approximate *conditional* density f(y | z)? (not default) |
debug |
print debugging information? (not default) |
higherOrderCorrection |
should a higher-order correction of the Laplace approximation be used? (not default) |
Value
the negative log marginal unnormalized density values at the
zValues. (Note the words “negative”, “log”, and
“unnormalized” !!!)
glmBfp documentation built on June 7, 2017, 3:01 p.m.
Search within the glmBfp package
Search all R packages, documentation and source code
R Package Documentation
Browse R Packages
We want your feedback!
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