jdpar.asymp: Joint asymptotic mutivariate density of parameters

In bayesdistreg: Bayesian Distribution Regression

Description

jdpar.asymp takes input object from dr_asympar() for asymptotic bayesian distribution. It returns objects for joint mutivariate density of parameters across several thresholds. Check for positive definiteness of the covariance matrix, else exclude thresholds yielding negative eigen values.

Usage

jdpar.asymp(drabj, data, jdF = FALSE, vcovfn = "vcovHC", ...)

Arguments

drabj	object from dr_asympar()
data	dataframe, first column is the outcome
jdF	logical to return joint density of F(yo) across thresholds in drabj
vcovfn	a string denoting the function to extract the variance-covariance. Defaults at "vcov". Other variance-covariance estimators in the sandwich package are usable.
...	additional input to pass to vcovfn

Value

mean vector Theta and variance-covariance matrix vcovpar of parameters across thresholds and if jdF=TRUE, a mean vector mnF and a variance-covariance matrix vcovF of F(yo)

Examples

y = faithful$waiting
x = scale(cbind(faithful$eruptions,faithful$eruptions^2))
qtaus = quantile(y,c(0.05,0.25,0.5,0.75,0.95))
drabj<- dr_asympar(y=y,x=x,thresh = qtaus); data = data.frame(y,x)
(drjasy = jdpar.asymp(drabj=drabj,data=data,jdF=TRUE))

bayesdistreg documentation built on May 1, 2019, 8:03 p.m.