View source: R/all_wnorm_fns.R
rwnorm | R Documentation |
The univariate Wrapped Normal distribution
rwnorm(n = 1, kappa = 1, mu = 0) dwnorm(x, kappa = 1, mu = 0, int.displ, 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. |
int.displ |
integer displacement. If |
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 wrapped normal distribution has density
f(x) = √(κ/(2π)) ∑ \exp(-κ/2 (x - μ(2πω))^2)
where the sum extends over all integers ω,
and is approximated by a sum over ω in \{-M, -M+1, ..., M-1, M \} if int.displ =
M.
dwnorm
gives the density and rwnorm
generates random deviates.
kappa <- 1:3 mu <- 0:2 x <- 1:10 n <- 10 # when x and both parameters are scalars, dwnorm returns a single density dwnorm(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 dwnorm(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 dwnorm returns a vector of with ith element being the # density evaluated at x with parameter values kappa[i] and mu[i] dwnorm(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 dwnorm 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] dwnorm(x, kappa, mu) # when parameters are all scalars, number of observations generated by rwnorm is n rwnorm(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 rwnorm is the same as the length # of the recycled vectors rwnorm(n, kappa, mu)
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