# distreg.sas: Semi-asymptotic bayesian distribution In bayesdistreg: Bayesian Distribution Regression

## Description

`distreg.sas` takes input object from dr_asympar() for semi asymptotic bayesian distribution. This involves taking random draws from the normal approximation of the posterior at each threshold value.

## Usage

 `1` ```distreg.sas(ind, drabj, data, vcovfn = "vcov", iter = 100) ```

## Arguments

 `ind` index of object in list `drabj` (i.e. a threshold value) from which to take draws `drabj` object from dr_asympar() `data` dataframe, first column is the outcome `vcovfn` a string denoting the function to extract the variance-covariance. Defaults at "vcov". Other variance-covariance estimators in the sandwich package are usable. `iter` number of draws to simulate

## Value

fitob vector of random draws from density of F(yo) using semi-asymptotic BDR

## Examples

 ```1 2 3 4 5 6 7 8``` ```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) drsas1 = lapply(1:5,distreg.sas,drabj=drabj,data=data,iter=100) drsas2 = lapply(1:5,distreg.sas,drabj=drabj,data=data,vcovfn="vcovHC",iter=100) par(mfrow=c(3,2));invisible(lapply(1:5,function(i)plot(density(drsas1[[i]],.1))));par(mfrow=c(1,1)) par(mfrow=c(3,2));invisible(lapply(1:5,function(i)plot(density(drsas2[[i]],.1))));par(mfrow=c(1,1)) ```

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