normmixp {Bolstad} | R Documentation |
Bayesian inference on a normal mean with a mixture of normal priors
Description
Evaluates and plots the posterior density for mu, the mean of a normal
distribution, with a mixture of normal priors on mu
Usage
normmixp(x, sigma.x, prior0, prior1, p = 0.5, n.mu = 100, ret = FALSE)
Arguments
x |
a vector of observations from a normal distribution with unknown mean and known std. deviation. |
sigma.x |
the population std. deviation of the observations |
prior0 |
the vector of length 2 which contains the means
and standard deviation of your precise prior |
prior1 |
the vector of length 2 which contains the means
and standard deviation of your vague prior |
n.mu |
the number of possible mu values in the prior |
p |
the mixing proportion for the two component normal priors |
ret |
this argument is deprecated. |
Value
A list will be returned with the following components:
mu |
the vector of possible mu values used in the prior |
prior |
the associated probability mass for the values in mu |
likelihood |
the scaled likelihood function for mu
given x and sigma.x |
posterior |
the posterior probability of mu given
x and sigma.x |
See Also
binomixp
normdp
normgcp
Examples
## generate a sample of 20 observations from a N(-0.5,1) population
x = rnorm(20,-0.5,1)
## find the posterior density with a N(0,1) prior on mu - a 50:50 mix of
## two N(0,1) densities
normmixp(x,1,c(0,1),c(0,1))
## find the posterior density with 50:50 mix of a N(0.5,3) prior and a
## N(0,1) prior on mu
normmixp(x,1,c(0.5,3),c(0,1))
## Find the posterior density for mu, given a random sample of 4
## observations from N(mu,1), y = [2.99, 5.56, 2.83, 3.47],
## and a 80:20 mix of a N(3,2) prior and a N(0,100) prior for mu
x = c(2.99,5.56,2.83,3.47)
normmixp(x,1,c(3,2),c(0,100),0.8)
[Package
Bolstad version 0.2-17
Index]