normmixp {Bolstad} | R Documentation |
Evaluates and plots the posterior density for mu, the mean of a normal distribution, with a mixture of normal priors on mu
normmixp( x, sigma.x, prior0, prior1, p = 0.5, mu = NULL, n.mu = max(100, length(mu)), ... )
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. |
p |
the mixing proportion for the two component normal priors. |
mu |
a vector of prior possibilities for the mean. If it is |
n.mu |
the number of possible mu values in the prior. |
... |
additional arguments that are passed to |
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 |
## 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)