sensitize.data.frame: Compute the sensitivity-adjusted estimates of predicted...

In rnr: Rosenbaum and Rubin Sensitivity

Description

Compute the sensitivity-adjusted estimates of predicted outcome given treatment/control

Usage

## S3 method for class 'data.frame'
sensitize(obj, q, dp, d0, d1, debug = FALSE, ...)

Arguments

obj	data frame to analyze; must include columns $treat: Observed (binary) treatment, e.g., bail_set $resp_ctl: Predicted probability of positive resp given control, $resp_trt: Predicted probability of positive resp given treatment, $p_trt: predicted probability of treatment
q	p(u = 1 | x)
dp	change in log-odds of treat = 1 if u = 1
d0	change in log-odds of response = 1 if treat = 0 and u = 1
d1	change in log-odds of response = 1 if treat = 1 and u = 1
debug	logical, whether or not to return columns of intermediate variables for debugging purposes
...	additional arguments are ignored

Value

A data frame with the columns resp_ctl and resp_trt updated according to the sensitivity parameters. If debug = TRUE, returned data frame will also contain columns of intermediate variables computed for sensitivity, appended with "__" (e.g., gamma__), with the original response estimates renamed as resp_trt_trt__ = resp_trt resp_ctl_ctl__ = resp_ctl

Examples

obj <- data.frame(treat = 0, resp_ctl = .2, resp_trt = .3, p_trt = .5)
sensitize(obj, q = .5, dp = log(2), d0 = log(2), d1 = log(2))

rnr documentation built on May 1, 2019, 9:22 p.m.