rvm | R Documentation |
The univariate von Mises distribution
rvm(n, kappa = 1, mu = 0) dvm(x, kappa = 1, mu = 0, log = FALSE)
n |
number of observations. Ignored if at least one of the other parameters have length k > 1, in which case, all the parameters are recycled to length k to produce k random variates. |
kappa |
vector of concentration (inverse-variance) parameters; |
mu |
vector of means. |
x |
vector of angles (in radians) where the densities are to be evaluated. |
log |
logical. Should the log density be returned instead? |
If mu
and kappa
are not specified they assume the default values of 0
and 1
respectively.
The univariate von Mises distribution has density
f(x) = 1/(2π I_0 (κ)) \exp(κ \cos(x - mu))
where I_0 (κ) denotes the modified Bessel function of the first kind with order 0 evaluated at the point κ.
dvm
gives the density and rvm
generates random deviates.
kappa <- 1:3 mu <- 0:2 x <- 1:10 n <- 10 # when x and both parameters are scalars, dvm returns a single density dvm(x[1], kappa[1], mu[1]) # when x is a vector but both the parameters are scalars, dmv returns a vector of # densities calculated at each entry of x with the same parameters dvm(x, kappa[1], mu[1]) # if x is scalar and at least one of the two paraemters is a vector, both parameters are # recycled to the same length, and dvm returns a vector of with ith element being the # density evaluated at x with parameter values kappa[i] and mu[i] dvm(x[1], kappa, mu) # if x and at least one of the two paraemters is a vector, x and the two parameters are # recycled to the same length, and dvm returns a vector of with ith element being the # density at ith element of the (recycled) x with parameter values kappa[i] and mu[i] dvm(x, kappa, mu) # when parameters are all scalars, number of observations generated by rvm is n rvm(n, kappa[1], mu[1]) # when at least one of the two parameters is a vector, both are recycled to the same length, # n is ignored, and the number of observations generated by rvm is the same as the length of # the recycled vectors rvm(n, kappa, mu)
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