Description Usage Arguments Details See Also Examples
View source: R/distributions.R
Gaussian distribution objects
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The mean of the distribution as a numeric vector; implicitly specifies the dimension.
The covariance of the distribution.
The marginal correlations between parameters.
make.gaussian returns a distribution object representing
a multivariate normal distribution. If
sigma is specified,
that is taken to be its covariance. Otherwise, if
specified, the covariance is taken to be a matrix with ones on
the diagonal and
rho on the off-diagonal elements. To
preserve positive definiteness,
rho must be between
-1/(length(mean)-1) and 1.
are predefined distributions generated with
They are intended to be used as test cases with
compare.samplers. The examples below show how they
N2weakcor.dist is a weakly positively
correlated two-dimensional Gaussian.
N4poscor.dist is a
highly positively correlated four-dimensional Gaussian.
N4negcor.dist is a highly negatively correlated four-dimensional
similarly conditioned, but
N4poscor.dist has one large
eigenvalue and three small ones, while
one small eigenvalue and three large ones.
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N2weakcor.dist <- make.gaussian(c(0,0), rho=0.8) N4poscor.dist <- make.gaussian(c(1,2,3,4), rho=0.999) N4negcor.dist <- make.gaussian(c(1,2,3,4), rho=-0.3329)
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