ccfa (version 1.1.0)

cfa: cfa

Description

compute counterfactuals using distribution regression with a continuous treatment

Usage

cfa(formla, xformla = NULL, tvals, yvals, data, method = "dr",
  link = "logit", tau = seq(0.01, 0.99, 0.01), condDistobj = NULL,
  se = TRUE, iters = 100, cl = 1)

Arguments

formla

a formula y ~ treatment

xformla

one sided formula for x variables to include, e.g. ~x1 + x2

tvals

the values of the "treatment" to compute parameters of interest for

yvals

the values to compute the counterfactual distribution for

data

the data.frame where y, t, and x are

method

either "dr" or "qr" for distribution regression or quantile regression

link

if using distribution regression, any link function that works with the binomial family (e.g. logit (the default), probit, cloglog)

tau

if using quantile regression, which values of tau to estimate the conditional quantiles

condDistobj

optional conditional distribution object that has been previously computed

se

whether or not to compute standard errors using the bootstrap

iters

how many bootstrap iterations to use

cl

how many clusters to use for parallel computation of standard errors

Value

CFA object

Examples

# NOT RUN {
data(igm)
tvals <- seq(10,12,length.out=8)
yvals <- seq(quantile(igm$lcfincome, .05), quantile(igm$lcfincome, .95), length.out=50)
## This line doesn't adjust for any covariates
cfa(lcfincome ~ lfincome, tvals=tvals, yvals=yvals, data=igm,
 se=FALSE)

## This line adjusts for differences in education
cfa(lcfincome ~ lfincome, ~HEDUC, tvals=tvals, yvals=yvals, data=igm,
 se=FALSE)

# }