bayesian_crank: Computes Posterior Probabilities for Discrete Models
In ProbBayes: Probability and Bayesian Modeling
Description
Usage
Arguments
Value
Author(s)
Examples
View source: R/bayesian_crank.R
Description
Given a data table with columns Prior and Likelihood, computes posterior probabilities
Usage
1
Arguments
d
data frame with columns Prior and Likelihood
Value
data frame with new columns Product and Posterior
Author(s)
Jim Albert
Examples
1
2
3
4
5
6
df <- data.frame(p=c(.1, .3, .5, .7, .9),
Prior=rep(1/5, 5))
y <- 5
n <- 10
df$Likelihood <- dbinom(y, prob=df$p, size=n)
df <- bayesian_crank(df)
ProbBayes documentation built on March 13, 2020, 1:31 a.m.
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Description Usage Arguments Value Author(s) Examples
View source: R/bayesian_crank.R
Description
Given a data table with columns Prior and Likelihood, computes posterior probabilities
Usage
1 |
Arguments
d |
data frame with columns Prior and Likelihood |
Value
data frame with new columns Product and Posterior
Author(s)
Jim Albert
Examples
1 2 3 4 5 6 | df <- data.frame(p=c(.1, .3, .5, .7, .9),
Prior=rep(1/5, 5))
y <- 5
n <- 10
df$Likelihood <- dbinom(y, prob=df$p, size=n)
df <- bayesian_crank(df)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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