Post_y {brr} | R Documentation |
Density, distribution function, quantile function and random generation for the posterior predictive distribution of the count in the control group.
dpost_y(ynew, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)
ppost_y(q, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)
qpost_y(p, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)
rpost_y(n, Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T)
spost_y(Tnew, a = 0.5, b = 0, c = 0.5, d = 0, x, y, T, ...)
ynew , q |
vector of non-negative integer quantiles |
a , b |
non-negative shape parameter and rate 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 |
T , Tnew |
sample sizes of the control 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_y
gives the density, ppost_y
the distribution function,
qpost_y
the quantile function, rpost_y
samples from the distribution,
and spost_y
gives a summary of the distribution.
Post_y
is a generic name for the functions documented.
barplot(dpost_y(0:10, 10, 2, 7, 3, 4, 5, 3, 10))
spost_y(10, 2, 7, 3, 4, 5, 3, 10, output="pandoc")