distreg_cfa.sas {bayesdistreg} R Documentation

## Semi-asymptotic counterfactual distribution

### Description

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

### Usage

```distreg_cfa.sas(ind, drabj, data, cft, cfIND, 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 `cft` column vector of counterfactual treatment `cfIND` the column index(indices) of treatment variable(s) to replace with `cft` in `data0` `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

```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)
cfIND=2 #Note: the first column is the outcome variable.
cft=0.95*data[,cfIND] # a decrease by 5%
cfa.sasobj<- distreg_cfa.sas(ind=2,drabj,data,cft,cfIND,vcovfn="vcov")
par(mfrow=c(1,2)); plot(density(cfa.sasobj\$original,.1),main="Original")
plot(density(cfa.sasobj\$counterfactual,.1),main="Counterfactual"); par(mfrow=c(1,1))

```

[Package bayesdistreg version 0.1.0 Index]