dBetaBinom | R Documentation |
nimble
modelsdBetaBinom_v
and dBetaBinom_s
provide a beta binomial
distribution that can be used directly from R or in nimble
models. These are also used by beta binomial variations of dNmixture distributions.
nimBetaFun
is the beta function.
nimBetaFun(a, b, log)
dBetaBinom_v(x, N, shape1, shape2, len, log = 0)
dBetaBinom_s(x, N, shape1, shape2, len, log = 0)
rBetaBinom_v(n, N, shape1, shape2, len)
rBetaBinom_s(n, N, shape1, shape2, len)
a |
shape1 argument of the beta function. |
b |
shape2 argument of the beta function. |
log |
TRUE or 1 to return log probability. FALSE or 0 to return probability. |
x |
vector of integer counts. |
N |
number of trials, sometimes called "size". |
shape1 |
shape1 parameter of the beta distribution. |
shape2 |
shape2 parameter of the beta distribution. |
len |
length of |
n |
number of random draws, each returning a vector of length
|
These nimbleFunctions provide distributions that can be used
directly in R or in nimble
hierarchical models (via
nimbleCode
and nimbleModel
).
They are used by the beta-binomial variants of the N-mixture distributions
(dNmixture
).
The beta binomial is the marginal distribution of a binomial distribution whose probability follows a beta distribution.
The probability mass function of the beta binomial is
choose(N, x) * B(x + shape1, N - x + shape2) /
B(shape1, shape2)
, where B(shape1, shape2)
is the beta function.
nimBetaFun(shape1, shape2)
calculates B(shape1, shape2)
.
The beta binomial distribution is provided in two forms. dBetaBinom_v
and
is when shape1
and shape2
are vectors.
dBetaBinom_s
is used when shape1
and shape2
are scalars.
In both cases, x
is a vector.
Ben Goldstein and Perry de Valpine
For beta binomial N-mixture models, see dNmixture
.
For documentation on the beta function, use ?stats::dbeta
# Calculate a beta binomial probability with different shape1 and shape2 for each x[i]
dBetaBinom_v(x = c(4, 0, 0, 3), N = 10,
shape1 = c(0.5, 0.5, 0.3, 0.5), shape2 = c(0.2, 0.4, 1, 1.2))
# or with constant shape1 and shape2
dBetaBinom_s(x = c(4, 0, 0, 3), N = 10, shape1 = 0.5, shape2 = 0.5, log = TRUE)
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