logLik.logLikVec

S3 logLik method for logLikVec objects.

Performs adjusted inferences based on model objects fitted, using
maximum likelihood estimation, by the extreme value analysis packages
'eva' <https://cran.r-project.org/package=eva>,
'evd' <https://cran.r-project.org/package=evd>,
'evir' <https://cran.r-project.org/package=evir>,
'extRemes' <https://cran.r-project.org/package=extRemes>,
'fExtremes' <https://cran.r-project.org/package=fExtremes>,
'ismev' <https://cran.r-project.org/package=ismev>,
'mev' <https://cran.r-project.org/package=mev>,
'POT' <https://cran.r-project.org/package=POT> and
'texmex' <https://cran.r-project.org/package=texmex>.
Adjusted standard errors and an adjusted loglikelihood are provided, using
the 'chandwich' package <https://cran.r-project.org/package=chandwich>
and the object-oriented features of the 'sandwich' package
<https://cran.r-project.org/package=sandwich>. The adjustment is based on a
robust sandwich estimator of the parameter covariance matrix, based on the
methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This
can be used for cluster correlated data when interest lies in the
parameters of the marginal distributions, or for performing inferences that
are robust to certain types of model misspecification. Univariate extreme
value models, including regression models, are supported.

Paul Northrop

Loglikelihood Adjustment for Extreme Value Models

Camellia Yin

logLik.logLikVec function

<dl><dt>object</dt>
<dd>An object of class <code>"logLikVec"</code> return from a
<code>logLikVec</code> method.</dd>
<dt>...</dt>
<dd>Further arguments.</dd></dl>

Arguments

See the example in <code>bernoulli</code>.

Examples

Sum loglikelihood contributions from individual observations — logLik.logLikVec

<dl>

<dt>object</dt>
<dd>An object of class <code>"logLikVec"</code> return from a
<code>logLikVec</code> method.</dd>


<dt>...</dt>
<dd>Further arguments.</dd>

</dl>

See the example in <code>bernoulli</code>.

logLik.logLikVec: Sum loglikelihood contributions from individual observations

Description

Usage

Value

Arguments

Examples

Details