Post_x {brr} | R Documentation |
Density, distribution function, quantile function and random generation for the posterior predictive distribution of the count in the treated group.
dpost_x(xnew, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)
ppost_x(q, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)
qpost_x(p, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)
rpost_x(n, Snew, a = 0.5, c = 0.5, d = 0, x, y, S)
spost_x(Snew, a = 0.5, c = 0.5, d = 0, x, y, S, ...)
xnew , q |
vector of non-negative integer quantiles |
a |
non-negative shape parameter of the Gamma prior distribution on the rate |
c , d |
non-negative shape parameters of the prior distribution on |
x , y |
counts (integer) in the treated group and control group of the observed experiment |
S , Snew |
sample sizes of the treated group in the observed experiment and the predicted experiment |
p |
vector of probabilities |
n |
number of observations to be simulated |
... |
arguments passed to |
The posterior predictive distribution of the count in the treated group is a
Poisson-Gamma-Inverse Beta distribution
.
dpost_x
gives the density, ppost_x
the distribution function,
qpost_x
the quantile function, rpost_x
samples from the distribution,
and spost_x
gives a summary of the distribution.
Post_x
is a generic name for the functions documented.
barplot(dpost_x(0:10, 10, 2, 3, 4, 5, 3, 10))
qpost_x(0.5, 10, 2, 3, 4, 5, 3, 10)
ppost_x(4, 10, 2, 3, 4, 5, 3, 10)