Likelihood function (spectral density) and random generator in the Pairwise Beta and NL models.
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dnestlog(x = rbind(c(0.1, 0.3, 0.6), c(0.3, 0.3, 0.4)), par = c(0.5, 0.5, 0.2, 0.3), log = FALSE, vectorial = T) dpairbeta(x, par = c(1, rep(2, choose(4, 2) + 1)), log = FALSE, vectorial = TRUE) rnestlog(n = 5, par = c(0.2, 0.3, 0.4, 0.5), threshold = 1000, return.points = FALSE) rpairbeta(n = 1, dimData = 3, par = c(1, rep(1, 3)))
An angular data set (may be reduced to a single
The parameter for the Pairwise Beta or the Nested Logistic density.
Logical. Should the density be returned on the log scale ?
Logical. Should a vector or a single value be returned ?
The radial threshold r above which the simulated points should be kept to build the angular dataset. Should be set to a high value, for the asymptotic approximation
P(W \in B | ||X|| >r) ~ H(B)
logical: should the censored vectorial dataset corresponding to the angular one be returned ?
The number of points on the simplex to be generated.
the dimension of the sample space, which is 1 + the dimension of the simplex.
Applies to angular data sets. The density is given with
respect to the Lebesgue measure on
p is the number of
x (or the length of
x, if the
latter is a single point).
The value returned by the likelihood function is imposed
contrast, the random variable have unconstrained output
dpairbeta returns the
likelihood as a single number if
==FALSE, or as a vector of size
containing the likelihood of each angular data point. If
log == TRUE, the log-likelihood is returned
rpairbeta returns a matrix with
returns the likelihood as a single number if
vectorial ==FALSE, or as a vector of size
nrow(x) containing the likelihood of each angular
data point. If
log == TRUE, the log-likelihood is
rnestlog returns a matrix with
n rows and
dimData columns if
return.points==FALSE (the default). Otherwise, a
list is returned, with two elements:
Angles: The angular data set
The full tri-variate data set above
Angles multiplied by the radial
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