Constructing prior distibutions for the location, scale and shape parameters using normal, beta or gamma distributions. A linear trend for the location can also be specified, using a prior normal distribution centered at zero for the trend parameter.
prior.prob(quant, alpha, trendsd = 0)
prior.quant(prob = 10^-(1:3), shape, scale, trendsd = 0)
prior.norm(mean, cov, trendsd = 0)
prior.loglognorm(mean, cov, trendsd = 0)Returns an object of class "evprior", which is essentially
just a list of the arguments passed.
Numeric vectors of length three and four
respectively.
Beta prior distibutions are placed on probability ratios
corresponding to the quantiles given in quant.
Numeric vectors of length three.
Gamma prior distibutions, with parameters shape and
scale, are placed on quantile differences corresponding
to the probabilities given in prob.
The prior distibution for the location, log(scale)
and shape is taken to be trivariate normal, with mean mean
(a numeric vector of length three) and covariance matrix cov
(a symmetric positive definite three by three matrix).
The standard deviation for the marginal normal prior distribution (with mean zero) placed on the linear trend parameter for the location. If this is zero (the default) a linear trend is not implemented.
See the user's guide.
posterior, pplik
mat <- diag(c(10000, 10000, 100))
prior.norm(mean = c(0,0,0), cov = mat, trendsd = 10)
prior.quant(shape = c(38.9,7.1,47), scale = c(1.5,6.3,2.6))
prior.prob(quant = c(85,88,95), alpha = c(4,2.5,2.25,0.25))
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