View source: R/mgplikelihoods.R
clikmgp | R Documentation |
Censored likelihoods for various parametric limiting models over region determined by
\{y \in F: \max_{j=1}^D \sigma_j \frac{y^\xi_j-1}{\xi_j}+\mu_j > u\};
where \mu
is loc
, \sigma
is scale
and \xi
is shape
.
clikmgp(
dat,
thresh,
mthresh = thresh,
loc,
scale,
shape,
par,
model = c("log", "neglog", "br", "xstud"),
likt = c("mgp", "pois", "binom"),
lambdau = 1,
...
)
dat |
matrix of observations |
thresh |
functional threshold for the maximum |
mthresh |
vector of individuals thresholds under which observations are censored |
loc |
vector of location parameter for the marginal generalized Pareto distribution |
scale |
vector of scale parameter for the marginal generalized Pareto distribution |
shape |
vector of shape parameter for the marginal generalized Pareto distribution |
par |
list of parameters: |
model |
string indicating the model family, one of |
likt |
string indicating the type of likelihood, with an additional contribution for the non-exceeding components: one of |
lambdau |
vector of marginal rate of marginal threshold exceedance. |
... |
additional arguments (see Details) |
Optional arguments can be passed to the function via ...
censored
matrix of booleans and NA
indicating whether observations dat
fall below the mthreshold mthresh
cl
cluster instance created by makeCluster
(default to NULL
)
ncors
number of cores for parallel computing of the likelihood
numAbovePerRow
number of observations above mthreshold (non-missing) per row
numAbovePerCol
number of observations above mthreshold (non-missing) per column
mmax
maximum per column
B1
number of replicates for quasi Monte Carlo integral for the exponent measure
B2
number of replicates for quasi Monte Carlo integral for the censored intensity contribution
genvec1
generating vector for the quasi Monte Carlo routine (exponent measure), associated with B1
genvec2
generating vector for the quasi Monte Carlo routine (individual obs contrib), associated with B2
the value of the log-likelihood with attributes
expme
, giving the exponent measure
The location and scale parameters are not identifiable unless one of them is fixed.
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