| find_border {sensitivityCalibration} | R Documentation |
Find the lambda-delta boundary for a fixed sensitivity parameter p.
Description
Given the dataset, unmeasured confounder, sensitivity parameter p, and a sequence of lambda values, the function uses binary search to find a sequence of delta corresponding to each lambda in the lambda_vec such that the estimated 95% for the treatment effect barely covers 0. The function returns a dataframe consisting of lambda_vec and the corresponding deltas. See below for an example.
Usage
find_border(q, u, p, lambda_vec, start_value_low, start_value_high,
data_matched, n_boot = 2000, tol = 0.01)
Arguments
q |
Number of matched covariates plus treatment. |
u |
Unmeasured confounder; u = c(1,0) if the unmeasured confounder is assumed to be binary. |
p |
The probability vector corresponding to u; p = c(0.5, 0.5) if the unmeasured confounder is assumed to be Bernoulli(0.5). |
lambda_vec |
A sequence of lambda values. |
start_value_low |
Starting value for the binary search (the lower endpoint). |
start_value_high |
Starting value for the binary search (the higher endpoint). |
data_matched |
The dataset after matching. |
n_boot |
Number of boostrap samples used to approximate the CI. |
tol |
Tolerance for the binary search. |
Details
start_value_low and start_value_high are user supplied numbers to start the binary search.
Examples
data(NHANES_blood_lead_small_matched)
attach(NHANES_blood_lead_small_matched)
find_border(9, c(1,0), c(0.5,0.5), c(0.5,1,1.5), 0, 4,
NHANES_blood_lead_small_matched, n_boot = 1000)
detach(NHANES_blood_lead_small_matched)