rcmivbinary: IV approach when X and Y subject to misclassification in...
In lxinhui/RCMIV: A IV method simultaneously addressing unobserved confounding and measurement errors in both treatment and outcome
Description
Usage
Arguments
Value
Author(s)
Description
IV approach when X and Y subject to misclassification in binary IV model
Usage
1
rcmivbinary(gbi, x1, x2, y1, y2)
Arguments
G
A numeric vector of binary instrumental variable.
X1
A numeric vector of the first measurement of binary exposure X
X2
A numeric vector of the second measurement of binary exposure X
Y1
A numeric vector of the first measurement of binary outcome Y
Y2
A numeric vector of the second measurement of binary outcome Y
Value
A vector of causal effect estimate, bootstrap SE and 95
Author(s)
Xinhui Liu
lxinhui/RCMIV documentation built on May 17, 2020, 12:23 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Author(s)
Description
IV approach when X and Y subject to misclassification in binary IV model
Usage
1 | rcmivbinary(gbi, x1, x2, y1, y2)
|
Arguments
G |
A numeric vector of binary instrumental variable. |
X1 |
A numeric vector of the first measurement of binary exposure X |
X2 |
A numeric vector of the second measurement of binary exposure X |
Y1 |
A numeric vector of the first measurement of binary outcome Y |
Y2 |
A numeric vector of the second measurement of binary outcome Y |
Value
A vector of causal effect estimate, bootstrap SE and 95
Author(s)
Xinhui Liu
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.