jdpar.asymp {bayesdistreg} | R Documentation |
Joint asymptotic mutivariate density of parameters
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 |
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))