Description Usage Arguments Value
This function performs a sensitivity analysis of causal effects different assumptions about unmeasured confounding, as described by Blackwell (2013).
1 2  causalsens(model.y, model.t, cov.form,
confound = one.sided, data, alpha)

model.y 
outcome model object. Currently only
handles 
model.t 
propensity score model. Currently assumes
a 
cov.form 
onesided formula to describe any covariates to be included in the parital R^2 calculations. 
confound 
function that calculates the confounding
function. This function must take arguments 
data 
data frame to find the covariates from

alpha 
vector of confounding values to pass the confounding function. Defaults to 11 points from 0.5 to 0.5 for binary outcome variable, and 11 points covering the interquartile range for nonbinary outcome variables. 
Returns an object of class causalsens
.
sens
data frame containing alpha values,
partial R^2s, estimates, and 95
partial.r2
vector of partial R^2 values for
the covariates to compare to sensitivity analysis
results.
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