R: Posterior predictive simulation from Bayesian normal sampling...
normpostpred
R Documentation
Posterior predictive simulation from Bayesian normal sampling model
Description
Given simulated draws from the posterior from a normal sampling model, outputs
simulated draws from the posterior predictive distribution of a statistic of interest.
Usage
normpostpred(parameters,sample.size,f=min)
Arguments
parameters
list of simulated draws from the posterior where mu contains the normal mean
and sigma2 contains the normal variance
sample.size
size of sample of future sample
f
function defining the statistic
Value
simulated sample of the posterior predictive distribution of the statistic
Author(s)
Jim Albert
Examples
# finds posterior predictive distribution of the min statistic of a future sample of size 15
data(darwin)
s=normpostsim(darwin$difference)
sample.size=15
sim.stats=normpostpred(s,sample.size,min)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(LearnBayes)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LearnBayes/normpostpred.Rd_%03d_medium.png", width=480, height=480)
> ### Name: normpostpred
> ### Title: Posterior predictive simulation from Bayesian normal sampling
> ### model
> ### Aliases: normpostpred
> ### Keywords: models
>
> ### ** Examples
>
> # finds posterior predictive distribution of the min statistic of a future sample of size 15
> data(darwin)
> s=normpostsim(darwin$difference)
> sample.size=15
> sim.stats=normpostpred(s,sample.size,min)
>
>
>
>
>
> dev.off()
null device
1
>