summarize.posterior.or.prior: Title
In Auburngrads/SMRD: Statistical Methods For Reliability Data

Title

summarize.posterior.or.prior(
  posterior.object,
  task,
  marginal.on.pos,
  marginal.on.sigma,
  type.position,
  newdata,
  include.likelihood,
  post.or.prior,
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  xlab = parameter.contour.axes.labels.out$xlab,
  ylab = parameter.contour.axes.labels.out$ylab,
  axes.range.default.post = T,
  factor = 4.5,
  size = GetSMRDDefault("SMRD.DefaultGridPoints"),
  number.points.plot = 500
)

`posterior.object`
`task`
`marginal.on.pos`
`marginal.on.sigma`
`type.position`
`newdata`
`include.likelihood`
`post.or.prior`
`xlim`
`ylim`
`xlab`
`ylab`
`axes.range.default.post`
`factor`
`size`
`number.points.plot`

## Not run: 
# Specify prior distribution characteristics for the 
# \code{bearingcage} data using a non-informative quantile 
# and a noninformative sigma

prior.spec1 <-
  specify.simple.prior(p = .01,
                       qdist = "loguniform",
                       qlower = 100,
                       qupper = 5000,
                       sigma.dist =  "lognormal",
                       sigma.lower = 0.2,
                       sigma.upper =  0.5,
                       distribution = "Weibull")
                       
# Specify prior distribution characteristics for the 
# \code{bearingcage} data using a noninformative quantile 
# and an informative sigma

prior.spec2 <-
  specify.simple.prior(p = .01,
                       qdist = "loguniform",
                       qlower = 1000,
                       qupper = 1400,
                       sigma.dist = "lognormal",
                       sigma.lower = 1.5,
                       sigma.upper = 2.5, 
                       distribution  = "Weibull")
                       
# Specify prior distribution characteristics for the 
# \code{bearingcage} data using an informative quantile 
# and an informative sigma

prior.spec3 <-
  specify.simple.prior(p = .01,
                       qdist = "lognormal",
                       qlower = 1000,
                       qupper = 1400,
                       sigma.dist = "lognormal",
                       sigma.lower = 1.5,
                       sigma.upper = 2.5,
                       distribution  = "Weibull")
 
# Create the prior distributions                      
prior3.bcage <- 
  make.prior(spec = prior.spec3, 
             number.in.prior = 3000)


prior.and.post3.bcage <-
  get.big.posterior(prior.spec3,
                    BearingCage.ld)

prior.and.post3.bcage$post[1:10,] 

prior.and.post3.bcage <- 
  make.small.posterior.object(prior.and.post3.bcage)
  
summarize.posterior.or.prior(prior.and.post3.bcage,
                             post.or.prior = "post",
                             task = "Marginals only",
                             marginal.on.sigma = T,
                             marginal.on.pos = F,
                             type.position = "Parameter",
                             newdata = "mu",
                             include.likelihood = T)

## End(Not run)