Description Usage Arguments Details Value Note Examples
Density, distribution function, quantile function and random generation for the conditional prior predictive distribution of x given y.
1 2 3 4 5 6 7 8 9 | dprior_x_given_y(x, y, a, c, d)
pprior_x_given_y(q, y, a, c, d)
qprior_x_given_y(p, y, a, c, d)
rprior_x_given_y(n, y, a, c, d)
sprior_x_given_y(y, a, c, d, ...)
|
x,q |
vector of non-negative integer quantiles |
y |
count (integer) in the control group |
a |
non-negative shape parameter of the Gamma prior distribution on the rate μ |
c,d |
non-negative shape parameters of the prior distribution on φ |
p |
vector of probabilities |
n |
number of observations to be simulated |
... |
arguments passed to |
The prior predictive distribution of the count x is the
Beta-negative binomial distribution
with shape parameters a+y, d, c.
dprior_x_given_y
gives the density,
pprior_x_given_y
the distribution function,
qprior_x_given_y
the quantile function,
rprior_x_given_y
samples from the distribution, and
sprior_x_given_y
gives a summary of the distribution.
Prior_x_given_y
is a generic name for the functions documented.
1 2 | barplot(dprior_x_given_y(0:10, 5, 3, 10, 20))
sprior_x_given_y(5, 3, 10, 20, output="pandoc")
|
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