Description Usage Arguments Value Examples
View source: R/conjugatepriors.R
Univariate Gaussian Conjugate Prior This function takes a vector of proportions and iteratives over it applying a gaussian conjugate prior calculation at each point, and using the posterior as the new prior in the next iteration.
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data |
a vector of the data or a single point |
priormean |
mean of prior distribution |
priorvar |
variance of prior distribution if invgamma is not true, shift parameter for variance if invgamma is true |
datavar |
data variance, if null var is called to estimate it |
singlepoll |
if true it calculates the variance based on the sample size |
n |
approriate sample size |
logit |
if a logit transformation should be used |
invgamma |
a logical indicator of whether or not a inverse gamma prior for sigma is (T) or isn't included |
a0 |
prior a if invgamma is T |
b0 |
prior b if invgamma is T |
A list with the following components: priormean: the prior mean, priorvar: the prior var, a0: the prior of the inverse gamma distribution if invgamma is true, b0: the prior of the inverse gamma distribution if invgamma is true, n: which is the sample size if supplied, datavar: which is the variance of the data if it is supplied, invgamma: which states if the inverse gamma prior was used, postmean: which is the posterior mean, postvar: which is the posterior variance, postsd: which is the posterior standard deviation, posta: which is the posterior a of a inverse gamma (a,b), and postb: which is the posterior of b of a inverse gamma(a, b), and dataweight: a scalar of the weight of the data over the prior.
1 2 3 4 5 6 7 8 | set.seed(10)
data1 = rnorm(30, mean = .48, sd = 0.05)
n1 = floor(runif(1, 200, 800))
datavar1 = mean(sqrt(data1[1]*(1-data1[1])/n1))
unigausscp(data1, 0.5, 0.05)
unigausscp(data1[1], 0.5, 0.05, singlepoll = TRUE, n = n1)
unigausscp(data1, 0.5, 1, invgamma = TRUE, a0 = 0.0001, b0 = 0.0001)
unigausscp(data1, 0.5, 1, logit = T, invgamma = TRUE, a0 = 0.0001, b0 = 0.0001)
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