dp.post.est {rmp} | R Documentation |
Posterior probability mass function estimation with DP prior
Description
Performs Bayesian probability mass function estimation under DP prior with Poisson base measure.
Usage
dp.post.est(x, y, alpha, lambda)
Arguments
x |
Values on which to compute the pmf. |
y |
Vector of observed data. |
alpha |
DP precision parameter |
lambda |
Mean parameter for the Poisson base measure. |
Details
Performs probability mass function estimation under th following model
y_i \mid P \sim P, i=1, \dots, n
P \sim DP(\alpha, P_0),
where P_0
is Poisson with mean lambda
.
Value
A vector of size length(x)
containing the probability masses
Author(s)
Antonio Canale
References
Carota, C., and Parmigiani, G. (2002), “Semiparametric Regression for Count Data,” Biometrika, 89, 265–281.
Examples
data(ethylene)
y <- tapply(ethylene$impl,FUN=mean,INDEX=ethylene$id)
z <- tapply(ethylene$dose,FUN=mean,INDEX=ethylene$id)
# Estimate the pmf of the number of implants in the control group
y0 <- y[z==0]
pmf.control = dp.post.est(0:30, y0, alpha = 1)
[Package rmp version 2.2 Index]