`cfa2.Rd`

the same as cfa method except it computes two results at the same time which allows one to conduct inference on their difference

cfa2( formla, tvals, yvals, data, xformla1 = NULL, method1 = "dr", link1 = "logit", tau1 = seq(0.01, 0.99, 0.01), condDistobj1 = NULL, xformla2 = NULL, method2 = "dr", link2 = "logit", tau2 = seq(0.01, 0.99, 0.01), condDistobj2 = NULL, se = TRUE, iters = 100, cl = 1 )

formla | a formula y ~ treatment |
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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 |

xformla1 | an optional formula for the first set of x variables |

method1 | the first method for estimating the conditional distribution it can be "dr" for distribution regression or "qr" for quantile regression |

link1 | if using distribution regression, set the link variable. It can be any link function accepted by glm, e.g. logit, probit, cloglog |

tau1 | if using quantile regression, the values of tau to use, the default is seq(.01,.99,.01) |

condDistobj1 | if have already calculated a conditional distribution object outside of the model, can set it here |

xformla2 | an optional formula for the second set of x variables |

method2 | the second method for estimating the conditional distribution it can be "dr" for distribution regression or "qr" for quantile regression |

link2 | if using distribution regression, set the link variable. It can be any link function accepted by glm, e.g. logit, probit, cloglog |

tau2 | if using quantile regression, the values of tau to use, the default is seq(.01,.99,.01) |

condDistobj2 | if have already calculated a conditional distribution object outside of the model, can set it here |

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 |

list of two CFA objects

#' data(igm) tvals <- seq(10,12,length.out=5) yvals <- seq(quantile(igm$lcfincome, .05), quantile(igm$lcfincome, .95), length.out=50) ## obtain counterfactual results using quantile regression with ## no covariates and adjusting for education cfa2(lcfincome ~ lfincome, tvals, yvals, igm, method1="qr", xformla2=~HEDUC, method2="qr", se=FALSE, tau1=seq(.1,.9,.1), tau2=seq(.1,.9,.1))#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique#> Warning: Solution may be nonunique